Glasses for the Third Eye: Improving theQuality of Clinical DataAnalysis with Motion Sensor-based Data Filtering

Jaeyeon Park, Woojin Nam,Jaewon ChoiAjou Univerisity

Department of Computer Engineering

Taeyeong Kim, Dukyong YoonAjou University School of Medicine

Department of BiomedicalInformatics

Sukhoon LeeKunsan National University

Department of Software ConvergenceEngineering

Jeongyeup PaekChung-Ang University

School of Computer Science andEngineering

JeongGil KoAjou Univerisity

Department of Software andComputer Engineering

ABSTRACTRecent advances in machine learning based data analytics are open-ing opportunities for designing effective clinical decision supportsystems (CDSS) which can become the “third-eye” in the currentclinical procedures and diagnosis. However, common patient move-ments in hospital wards may lead to faulty measurements in physi-ological sensor readings, and training a CDSS from such noisy datacan cause misleading predictions, directly leading to potentiallydangerous clinical decisions. In this work, we present MediSense, asystem to sense, classify, and identify noise-causing motions andactivities that affect physiological signal when made by patients ontheir hospital beds. Essentially, such a system can be consideredas “glasses" for the clinical third eye in correctly observing medicaldata.MediSense combineswirelessly connected embedded platformsfor motion detection with physiological signal data collected frompatients to identify faulty physiological signal measurements andfilters such noisy data from being used in CDSS training or testingdatasets. We deploy our system in real intensive care units (ICUs),and evaluate its performance from real patient traces collected atthese ICUs through a 4-month pilot study at the Ajou UniversityHospital Trauma Center, a major hospital facility located in Suwon,South Korea. Our results show that MediSense successfully classi-fies patient motions on the bed with >90% accuracy, shows 100%reliability in determining the locations of beds within the ICU, andeach bed-attached sensor achieves a lifetime of more than 33 days,which satisfies the application-level requirements suggested by ourclinical partners. Furthermore, a simple case-study with arrhythmiapatient data shows that MediSense can help improve the clinicaldiagnosis accuracy.

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.SenSys ’17, November 6–8, 2017, Delft, Netherlands© 2017 Association for Computing Machinery.ACM ISBN 978-1-4503-5459-2/17/11. . . $15.00https://doi.org/10.1145/3131672.3131690

CCS CONCEPTS•Networks→ Sensor networks; •Applied computing→Healthcare information systems;

KEYWORDSClinical Decision Support System,Wireless Sensor Network, HealthCare Information Systems, Motion Sensing, Noise FilterACM Reference Format:Jaeyeon Park, Woojin Nam, Jaewon Choi, Taeyeong Kim, Dukyong Yoon,Sukhoon Lee, Jeongyeup Paek, and JeongGil Ko. 2017. Glasses for the ThirdEye: Improving the Quality of Clinical Data Analysis with Motion Sensor-based Data Filtering. In Proceedings of SenSys ’17 . ACM, New York, NY, USA,14 pages. https://doi.org/10.1145/3131672.3131690

1 INTRODUCTIONMany urban clinical environments are overwhelmed with patients,distracting caregivers (e.g., medical doctors and nurses) from pro-viding unceasing high quality clinical services. Such circumstancesbecome even more critical in emergency hospital units such asemergency rooms and trauma centers [14]. While patients at a clin-ical facility can receive urgent care when necessary, missing thepreambles of critical disease events can threaten the health status ofalready-suffering patients. Fortunately, recent advances in data ana-lytics and machine learning are providing opportunities for design-ing effective clinical decision support systems (CDSS) [8][10][17]that can help resolve some of these issues by automating the detec-tion of symptoms that caregivers may miss. While they cannot bea full substitute of medical personnel, yet1, results obtained fromsuch intelligent systems can assist important clinical decision mak-ing, acting as the “third eye” that focuses on patient care. Theseintelligent systems are made possible by multiple modalities of datathat can be gathered from the hospital, including the electronicmedical record (EMR) containing information on patient diagnosisand lab results, a biosignal data repository holding fine-grainedphysiological signal data, and the picture archiving and commu-nication system (PACS) data which serves as the repository formedical images (e.g., endoscopy or CT images).

However, unlike many applications where inaccurate predictionsor error in the data can be treated simply as a “mistake”, clinicalapplications represent one of the most accuracy-sensitive domains.1Some recent research has shown promising possibilities to some extent [8].

SenSys ’17, November 6–8, 2017, Delft, Netherlands Jaeyeon Park et al.

Figure 1: Pictorial view of our target deployment site.MediS-ense is deployed in three ICUs within the Ajou UniversityHospital Trauma Center.

In the clinical domain, the consequences that can result from thetraining and prediction using faulty data can be much more criticalthan many other applications in which big-data based machinelearning algorithms are used. Therefore, it is important that a dataanalytics-based clinical decision support system, empowered bylarge clinical/medical datasets and machine learning algorithms,are trained and utilized with proper, error-free data.

Unfortunately, and ironically, data collected from the hospitalsare known to be noisy [9][27]. For example, EMR data often in-clude noise introduced by humans in which the caregivers fail toinput the precise times of events or make minor mistakes in therecording process. On a more significant scale, the biosignal data,or physiological signal data collected from a patient’s body, possesserrors due to domestic motion artifacts and inaccurate attachmentof on-body sensing units. Despite the efforts to gather and digital-ize such medical/clinical data [10][17], the noise in the data pointschallenge the designing of a CDSS effective enough for real-clinicaluse. As a result, we are in a need of a system that (at the very least)filters out such noisy samples from being used as inputs of a CDSStraining set. We argue that it is extremely dangerous to attemptsignal reconstruction [2][9][18] in order to counter the artifactsgiven that this process would generate “fake” signals that clinicalexperts cannot trust. Potentially, using a cleaner version of suchdata (with noisy data filtered) may not only support the designingof a reliable CDSS, but also facilitate interesting research in theclinical sector.

The goal of this work is to design a system that addresses theaforementioned challenges. To this end, we present MediSense,which combines a gyroscope sensor with an embedded board wire-lessly connected to a gateway and a server. MediSense collectsmotion information at each patient-occupied bed and makes pre-dictions on the types of motion made by the patient. The summaryof this motion information is sent to the server and later used tofilter corrupted (and time-synchronized) biosignal data. Our systemincludes a localization scheme to identify which in-bed motionsensor corresponds to which biosignal data stream, timestampingto synchronize the motion data with biosignal data, and a routingscheme to reliably deliver the motion classification data to the gate-way. Furthermore, our system includes a quick-and-easy buttoninterface connected to the embedded board on the bed units as a

way for clinical personnel to indicate different types of patient diag-nosis activities without the need to reach a computer for includinginformation to the EMR. Combination of these hardware compo-nents and software modules to predict patient movement and assurelow-power operations allow MediSense to meet the requirementssuggested by our clinical partners.

As part of an Institutional Review Board (IRB) approved pilotstudy, we deploy MediSense at the Ajou University hospital traumacenter, serving ∼180 patients monthly (c.f. Figure 1). Our deploy-ment span over three intensive care unit (ICU) wards which include30 beds, and combined with our biosignal capturing backend sys-tem, our overall deployment has been in operation for ∼9 months.MediSense alone has been in operation for ∼4 months. During thisperiod, we have collected physiological signal data from 616 pa-tients, which add up to 2.59 TB. Our results show that MediSensesuccessfully classifies patient motions on the bed with >90% accu-racy, shows 100% reliability in determining the locations of the bedswithin the ICU, and achieves a lifetime of more than 30 days, whichsatisfies the application-level requirements suggested by our clini-cal partners. On a clinical application perspective, our case-study ondetecting patients with arrhythmia shows that MediSense can helpreduce mis-diagnosis by filtering out motion-affected physiologicalsignal.

Specifically, this paper makes the following contributions.

• Through an observational study made at an urban ICU, we iden-tify common motions that patients make on their beds and ex-amine their impact on the quality of physiological signal data.

• We design MediSense, an intelligent embedded wireless sensingsystem for classifying patients’ motions on the beds for filteringnoisy physiological signal at the biosignal data repository.

• We evaluate MediSense using a 4-month (and continuing) pilotdeployment at ICUs at the Ajou University Hospital TraumaCenter. Through this process, we identify additional challenges,and show the feasibility and applicability of MediSense.

The remainder of this paper is structured as follows. We firstmotivate our work and state the problem in Section 2, and presentthe main components of MediSense in Section 3. We then introduceour motion sensor-based patient movement classifier in Section 4,and discuss the complete MediSense architecture in Section 5. Weevaluate our system through a real deployment in Section 6, discussrelated work in Section 7, and conclude the work in Section 8.

2 MOTIVATION AND PROBLEM STATEMENTPatients hospitalized in modern clinical environments are usuallyconnected to high-fidelity patient monitors to capture and monitortheir accurate physiological signal data. Clinical personnel rely onthis data (typically using short term record charts or the monitordisplaying real-time signals) to determine the current status of apatient, and the values are periodically recorded to the patient’selectronic medical record (EMR). However, this EMR recordingonly occurs approximately once every hour or after an emergencyevent occurs. Given the large number of patients that each clinicalpersonnel must take care of, it is difficult to assure that doctors ornurses can well-monitor a patient’s status continuously [14].

Glasses for the Third Eye SenSys ’17, November 6–8, 2017, Delft, Netherlands

Figure 2: Sample ECG and plethysmograph signal traces col-lected from a real patient. The patient was mostly in stablestate, but the shaded portion represent the region when armwaving gesturesweremade. Notice the changes in physiolog-ical signal patterns.

We computer scientists argue that computing systems can help.By utilizing software that continuously analyzes a patient’s physi-ological signal data, computers can automate some of the routinemonitoring processes and provide clinical staff with the informationthey might miss. Specifically, newly proposed machine learning /deep learning algorithms are considered to have high potential forserving such purposes. Once we gather a sufficient amount of datafrom real patients, we can combine this information with the EMRdata and train the learning models to make predictions for targetevents, which would enable us to design effective clinical decisionsupport systems (CDSS) for clinical staff to utilize.

2.1 Preliminary StudiesWith such purpose in mind, we first collected real physiologicalsignal traces from patients at an ICU using the system and methodsthat we will elaborate in Section 3.1. While analyzing the physi-ological signal data, we noticed irregular patterns in the signals.As exemplified in Figure 2 where we plot the plethysmograph andECG signal traces of a patient over time, we noticed that parts ofthe signal were noisy and corrupted (c.f., shaded region in Figure 2).Deeper investigation into the data and visual observations made atthe ICU revealed that these were an impact of patient movementson the ICU beds. Given that our deployment targets a trauma cen-ter, many patients go through a series of critical operations. Oncethe patients wake up from anesthesia, patients very actively movetheir body to express their pain. In addition to such post-anesthesiamovements, many patients experience seizure during their stay inthe ICU. Figure 2 reveals to be the physiological signal traces of apatient making arm waving and body rotating gestures.

In order to design an effective prediction model for makingaccurate estimations on a patient’s health status, it is of utmostimportance that we train the model with accurate data. Whilethe data with noise represent “real” data, different patients canshow different noise variations. Our surveys with medical doctorsindicate that when clinical staff observe noisy physiological signals,they tend to observe the patient for longer periods after keepingthe patient still before making a clinical decision. This suggests thateven clinical staff rely on physiological signals collected during a“calm state”, and suggests the need for filtering out such noisy datafrom the CDSS training set.

As part of our preliminary data collection and system perfor-mance analysis, we also made efforts to tag physiological signalswith clinical events marked in the EMR as the ground truth of pa-tient outcomes. Operationally, when a doctor notices that a patientis in an urgent state, she will perform necessary treatments and(most likely) later record this information in the EMR. While thisrecord includes the time at which the event was detected, humanerrors (in the order of few minutes) are common given that manymedical events in a trauma center are urgent and do not allowdoctors to accurately record the exact time of event occurrence.This can cause problems in the training data as well. Given thathuman errors can cause a training dataset to include inconsistentinformation, it is important that we capture the exact times of whena target medical / clinical event occurs.

Overall, our experiences from the initial data collection phaserevealed interesting challenges and tasks, which include the filteringof noisy physiological signal data and the elimination (or reduction)of human errors in the ground truth recording process. Based onthis study, we aim to identify when and how patients move on theirbeds, and alert the data collection server to closely examine andeliminate noisy physiological signal data from being part of theclinical learning system training set.

2.2 System RequirementsBased on the experiences from our preliminary study and an oralsurvey conducted with eight clinical caregivers (two medical doc-tors and six nurses) in an urban trauma center, we outline some ofthe core requirements that a system for supporting clinical data-based machine learning systems should satisfy. Specifically, thesurvey includes questions that cover the need for motion classi-fication, management challenges of externally installed devices,security concerns and issues that we should consider for devicedeployment.

• High Reliability: The system should be able to deliver andgather physiological signal data and related metadata with highreliability in terms of both data yield (network reliability) andalso data quality (noise and artifacts).

• Motion Classification & Physiological Signal Correlation:Patients may perform different types of motions on the ICU bedand these need to be distinguished because, depending on theparts of the body the patients move, different physiological signalmodalities can be impacted.

• Minimal Impact on Clinical Protocols: A trauma center, aswell as many other clinical environments, have their own “proto-cols” to obey. It is important that our system does not interferewith the clinical protocols being performed in the hospital. Clin-ical staff interaction with the system should be minimal, andwires should not interfere with the clinical procedures. In addi-tion, the recharging of the devices should happen infrequently;thus, devices should maintain a low-power profile.

• Preserving Original Physiological Signals: Despite introduc-ing clinical staff to state-of-the-art signal reconstruction tech-niques [9][18], they insisted that absolutely no modificationshould be done to the original data. Clinical staff preferred tohave a dataset with time ranges of when patient’s performedspecific motions to “eliminate” data, but were pessimistic on

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In-bedSensornodes

Loca/on-Beaconnodes

Gateways

BackendServer Physiologicaldatacollec/on

Bio-signalRepository

MediSense

Mo/onsensing&classifica/on

Noisefiltering

Localiza/on

Rou/ng

Pa/entMonitors

HardwareComponents So@wareSubsystems

Figure 3: Main components our overall system including thebiosignal repository andMediSense.

the concept of producing and substituting noisy samples with“scientifically-emulated” data for reconstruction.The goal of this study is to provide a well-operating base sys-

tem for designing a clinical data-based learning system that canreduce misdiagnosis. The system we propose and evaluate in thefollowing sections, MediSense, targets to increase the reliability ofremotely collected physiological data by filtering out noisy sig-nals, and improve ground truth data quality using a combination ofsmall-sized motion sensors, embedded platforms, and low-powerwireless radios.

3 MAIN SYSTEM COMPONENTSBefore going into the details of our motion sensing, classifier design,and system architecture, in this section, we first present the mainhardware components of our system. We introduce the backendphysiological signal data collection system at our trauma centerdeployment, what hardware components and devices are used inMediSense, and discuss howMediSense interacts with the physiologi-cal data collection backend to accurately classify patient movementsfor noisy physiological signal data filtering.

3.1 Biosignal RepositoryWith the goal and system requirements in Section 2 in mind, wefirst designed a physiological signal data collection system to gatherpatient’s biosignal data effectively. This serves as the backend ofMediSense. Instead of attaching additional physiological signal sens-ing equipments to patients, or tamper with the hospital’s maindatabase, we interface our backend server directly to the “at-bed”patient monitors already being used in clinical environments. Oursystem is developed to diretly dump data from patient monitorsmanufactured by Philips [15] and Nihon Kohden [20], which are thetwo major types used at our target hospital. We installed our sys-tem in three ICUs of the hospital’s trauma center (named TICU-A,TICU-B, TICU-C), and this deployment currently covers a total of30 beds, 10 in each ICU. We use the backend server to store theraw physiological signal data collected from these at-bed patient

monitors (e.g., ECG, plethysmograph, SpO2, heartrate, respiration,etc.) and also to periodically fetch the EMR of associated patients.All patient monitors are connected to a switch installed withineach ICU via wired Ethernet, and the switch redirects traffic to acentral backend server for storage and future processing. With thissystem, the physiological signals from all patients admitted to ICUbeds are collected and stored in our data repository. At the point ofwriting, this deployment is active and has been active for 9 months,summing up to 2.59 TB of biosignal data collected from 616 patients.The initial purpose of this system was to enable clinical researchusing a large-sized physiological signal data repository.

3.2 Hardware Platforms in MediSenseTo interconnect with the aforementioned biosignal repository andto offer an additional sensing modality on patient motions, wedesign MediSense. MediSense consists of three types of hardwareplatforms; in-bed sensor nodes, localization-beacon nodes, and agateway as illustrated in Figure 3. The sensing platforms, beaconnodes, and gateway are all designed using an Atmel SAM R21XPlained board, which is an SoC of a ARM Cortex-M0+ MCU andan IEEE 802.15.4 radio [4]. We select to construct an independentnetwork of IEEE 802.15.4 radios (rather than using the hospital’sWiFi network) given that the hospital’s WiFi was not fully reliableand we wanted to achieve a low-power profile for our batterypowered bed-sensing platforms. The floor plan of one of our targetdeployment site, TICU-A, and the main components of our systemare depicted in Figure 4. Note that this is an abstract view, and israther compressed and miniaturized. In addition to these devices,the environment is full of mobile persons and medical devices thatnegatively impact our device connectivity.

3.2.1 In-Bed Sensor Node. An embedded sensing platform isinstalled under each ICU bed and attached to a gyroscope sensorin the bed for patient motion detection. This is shown as in-bedsensor nodes in Figure 4. Given that these beds are often movedand re-positioned to different locations within the ICU (or movedto operation rooms), we power each device using a rechargeableLi-Ion 12,800 mAh battery. Using IEEE 802.15.4-based radios, theseembedded platforms report data on patient motions to a gatewaynode located within the same ICU, but connected via Ethernet tothe biosignal collection server. Note that the choice of using batter-ies and wireless radios was due to our constraints of minimizingthe impact on the hospital’s treatment protocols. Any wired con-nection required additional tasks for the clinical personnel, whichwas unwanted in our application. We detail the networking andsystematic protocols to overcome the unique challenges of a clinicalenvironment in Section 5.

Furthermore, this board is alsowiredwith two buttons positionedat each sides of the bed, serving as a simple interface for clinical staffto communicate with the server. Specifically, MediSense providesclinical staff with a set of button pressing patterns representingdifferent types of events. For example, if the button is pressed for ∼2seconds, it indicates that a noticeable clinical event is detected, andthree short presses alerts the MediSense management staff to checkon the system.While system-alerts are directly sent to themanagingteam, clinical events are later mapped with the EMR to identify the

Glasses for the Third Eye SenSys ’17, November 6–8, 2017, Delft, Netherlands

Figure 4: Floor plan of our target deployment site, the Ajou University Hospital Trauma Center, and the main components inour system. This is an abstract view, and is rather compressed and miniaturized. Vast amount of medical devices present inthe room to focus on ourMediSense.

events. This feature is useful in capturing accurate “ground truthtiming" for clinical events correlated with physiological signals.

3.2.2 Location-Beacon Node. Second type of hardware inMediSense is the backbone “location-beacon node”. As shown inFigure 4, we placed one location-beacon node adjacent to a bed-side monitor2. Mounted at the walls above each of the bed’s headlocation, the beacon node takes two major roles. One is, as its nameimplies, localization for the sensor nodes. Since the ICU beds areportable and can be positioned at different locations within the ICU(or even outside the ICU), the beacon nodes are used to provideground truth proximity information for the in-bed sensor nodes. Inaddition, these beacons are also used for packet routing. If the bed-installed sensing platform fails to deliver messages to the gateway,the message is forwarded to the associated beacon node, whichforms a wall-powered backbone network to relay packets to thegateway. Details of these operations will be discussed in Section 5.

3.2.3 Gateway. Finally, the third hardware component is agateway device installed at each of the three ICUs. Specifically, onegateway node is installed within each ICU and is wired to a Ethernetswitch that aggregates all the physiological signals from the patientmonitors in the ICU. The data is forwarded to the gateway and thenthe switch, and is finally collected at the physiological signal datacollection backend server for further processing (e.g., data filtering).

2 Figure 4 shows the location-beacon nodes to be on top of the bed-side monitors, butit is actually placed below and behind where a bed would locate, if any, to be hiddenfrom the patients. This was requested by the medical staff.

4 PATIENT MOTION CLASSIFICATIONIn this section, we discuss the motion sensor data based patientmotion classifier. This classifier is used to identify what parts of thephysiological signal data should be filtered based on the motionsthat patients make on their beds.

4.1 Physiological Signal Noise-causing MotionsWhile lab settings can provide a controlled environment with cleandata, given that our data is collected in a non-controlled environ-ment with real patients, noise caused from natural patient move-ments are inevitable. To well-understand and accurately classifythe types of movements a patient can make within an ICU bed, weconducted an observational study over the course of two weeks tomonitor the motion activities that occur at 10 ICU beds. Table 1summarizes the five major types of motions and their occurrencesthat we observed from this study. The most common motion weobserved was arm waving. This happened most often when a pa-tient wanted to call the clinical staff for issues such as to expresstheir willingness to expectorate phlegm.

Based on our observational study and clinical staff survey, wedesign our embedded computing platform to capture motion dataand focus on detecting and classifying the current state of patientmovement for four different types of motions: (1) lying still, (2) armwaving, (3) body rotating, and (4) body shaking. Clinically, thesemotions correlate to changes in physiological signal data such asthe plethysmograph, electrocardiograph (ECG), and respirationpatterns. While patients are capable of making a diverse set ofmotions, our observational study suggests that these four have the

SenSys ’17, November 6–8, 2017, Delft, Netherlands Jaeyeon Park et al.

Motion Type Occurrences/day/bed Percentage (%)Head Motion 56 7.77Arm Motion 264 36.66

Full Body Motion 176 24.44Chest Motion 112 15.56Leg Motion 112 15.56

Table 1: Results from two-week observational study on thetypes of motions that patients make on their beds.

GYROSCOPE SENSOR IN-BED SENSOR NODE

BED-SIDE MONITOR 1

2 3

4

LOCATION-BEACON NODE

Figure 5: Embedded sensing platform, the in-bed motionsensor, installed on ICU beds, along with a location-beaconnode and bed-side physiological monitor.

highest impact on the core physiological data used in the clinicalroutine. Once the board classifies the motions, the summary ofrecent activities are sent to the gateway.

4.2 Sensor Selection and ConfigurationFor the motion sensors, we chose to use a 3-axis gyroscope. In ourpreliminary studies, we have tried using both 3-axis accelerometersand gyroscopes. However, we found that accelerometers were toosensitive to movements of the bed itself other than patient motion,and they did not provide additional information compared to thegyroscope alone. Furthermore, while we omit details due to thelack of space, we noticed that the gyroscope data provided us witha more correlated set of measurements over multiple test datasets.Thus, we chose to focus on using gyroscope data for detectingpatient movements.

After selecting the sensor, the first challenge we faced was ondeciding the installation location of the gyroscope sensor on thebed. Given the characteristics of a trauma center, there were highchances of beds being flooded with blood, which would interrupt orhinder the continuous operation of MediSense and also potentiallycause dangerous situations. Based on suggestions from clinical staff,we decided to install the devices under the air-mattress of the bed.While such positioning sacrificed the detection of some detailedpatient movements, the position was safe enough to protect patients(and our devices) from potential electric shock.

The next question was to identify at what position below theair-mattress to install the gyroscope. For this, we took an empiricalapproach by capturing gyroscope readings at various locations. Asthe circled numbers in Figure 5 show, we captured gyroscope read-ings at four different locations. Given that the majority of patient

monitor sensors are attached to a patient’s upper body and the mo-tions that affect their physiological signal readings were focused onthe arms and chest, we easily removed sensor position 4 (hips) fromour candidates. Using the three remaining locations, we performeda series of controlled experiments where six “fake patients” (e.g.,study volunteers) lie on the bed and mimic the four actions dis-cussed in our observational study. A total of 22 independent streamsof data samples were collected in this controlled setting. Note thatthese six patients were selected to represent patients of differentweight groups, which have high impact on the motion-sensor pat-terns. Through such an empirical process, we identified that thereadings from the gyroscope showed distinguishable patterns (fordifferent movements) when installed at position 3 (waist). We notethat prior to our experiments, we intuitively thought that the shoul-der position would be a suitable location for our sensor. However,we noticed high amplitudes in the gyroscope readings when thepatient made arm-related movements, while other motions werenullified; making it difficult to distinguish different patient motions.

Furthermore, we noticed that most patient movements on the bedinvolve motions on the gravitational axis (Z-axis in our data); thus,despite using a 3-axis gyroscope, we focus on the Z-axis readingsfor the remainder of this work. We note that a systematic empiricalstudy, revealed that the classification accuracy increases by ∼10%when using the Z-axis data compared to the other measurements.This helps our sensor data processing component on the embeddedcomputing platform to operate with minimal memory overheadand speeds up the classification process.

To make sure that we maximize the lifetime of the battery-powered embedded platforms, we design algorithms to make predic-tions on the patient movement “on-the-board" rather than exportingraw data points over the wireless radio. The following sections de-tail the process of detecting such different types of movements onthe bed. We develop our motion classifier using samples acquiredthrough preliminary studies conducted with six healthy volunteerssimulating different (common) patient movements in an ICU bed.We made sure that our experimental procedures minimally im-pacted the everyday operations within the trauma center throughproper authorization processes required in the ICU.

4.3 Peak Detection & ClassificationClassifier of MediSense is designed to be simple rather than unique.We designed a low complexity algorithm for embedded platformsthat is partially based on ideas from previous work [11][7][25]. Fig-ure 6(a) presents a sample raw trace collected from the gyroscope’sZ-axis in our controlled experiments. The alphabets in the figurerepresent the different types motions we asked the study partici-pants to perform. Specifically, compared to the four motions wetarget to detect, we show finer-grained motions as the following:patient on the bed stays still (region a), waves one arm (region b -left arm, c - right arm), waves two arms (region d), slowly rotatesbody (region e), quickly rotates body (region f ), and shakes bodyactively (region g). From the figure we can make an intuitive deci-sion that motions (e), (f) and (g) are visually distinguishable fromthe other cases. Nevertheless, due to the noise introduced by thesensors itself, we found the need for a low-pass filter to effectivelycapture arm gesture movements (e.g., cases b, c and d). We note

Glasses for the Third Eye SenSys ’17, November 6–8, 2017, Delft, Netherlands

(a) (b) (c) (d) (e) (f) (g)

(a) Raw values

(a) (b) (c) (d) (e) (f) (g)

(b) Low-pass filtered

Figure 6: Raw and Low-pass filtered gyroscope Z-axis data.

again that our embedded platform is resource-limited; thus, a com-plicated classifier is unsuitable. Another purpose for the low-passfilter is to reduce the computational complexity for classificationoperations. For this, we apply an exponentially weighted movingaverage (EWMA) filter to the raw data.

st = α · xt + (1 − α) · st−1, t > 0, s0 = x0

Here, xt is the data sample collected at time t , and we set thesmoothing factor α as 0.01 through empirical optimization on thepreliminary data we collected (details omitted due to lack of space).As we see in Figure 6(b), the EWMA filter allows us to successfully,at least visually, differentiate case (a) with (b), (c) and (d), while stillallowing us to detect (e), (f), and (g).

Using the low-pass filtered gyroscope sensor samples,MediSensetargets to distinguish different motions using ‘peak detection’ givenits simplicity in capturing important characteristics in a time-seriesdata [23]. Specifically, our peak detection algorithm on the incomingdata operates as follows. As the embedded platform gathers sensingdata, we configure n samples as a block of data samples (n=10 usedin our current design to maintain a low memory usage profile) andselect the points within the data block that have the maximum andminimum values. Note that not all maximum and minimum valuesare set as high and low peaks. Rather, we declare peaks only whenthese min/max values and the most recently detected peak showdifferences of more than thresholdTh∆. Instead of selecting a fixedTh∆, we allow the threshold to change adaptively with respect tothe previous data samples. Adopting ideas from previous work inanalyzing ECG signals [3][22], we configure our adaptive Th∆ asthe following.

Th∆(t ) = β · γ · st + (1 − β) ·Th∆(t−1)

where, similar to the parameter α above, we empirically selectβ=0.05 and γ=0.1 using parameter optimization on our data set.

As a result, while small amounts of motions will not affect Th∆,motions with high levels of activity will generate enough amplitudein the data to modify Th∆. While being adaptive to the incoming

(a) (b) (c) (d) (e) (f) (g)

Figure 7: Peaks detected for our controlled data set.

Sequence Number

Figure 8: Peak time-distance scatter plots from gyroscopedata for different patient motion activities.

data, we make sure that Th∆ also has a lower-bound configuredto a value referenced to the amplitude differences observed whenthere is no moving activity on the bed.

As more sensing data is gathered, the block continuously movestowards the most recent measurements and computes a new mini-mum and maximum value. If the minimum and maximum valuesof the current block is larger or smaller than that of the previousdata block, respectively, the minimum or maximum values of theprevious block is marked as a lower or upper peak, again, respec-tively. The dots in Figure 7 presents the high and low peaks detectedusing our scheme for the gyroscope readings after the EWMA. Formotion classification, our scheme declares an activity classificationwindow duration ofT , which presents a time duration for detectingactivities. If no peaks are detected within T , MediSense classifiesthe patient activity within this window as “Patient Still”.

Once MediSense detects peaks in the data, it computes the time-distance between the peaks (time elapsed since the detection ofthe most recent peak). We noticed from our experiments that bodyshaking movements showed noticeably low variations in the peaktime-distances (c.f., region (g) in Figure 8). If the variance of peaktime-distances within T is less than Vmin , we classify the currentmotion activity as “Body Shaking”.

In the case where samples in T show peaks, but their peak time-distance variances exceedVmin ,MediSense moves to the next phaseof its classification scheme, in which we examine the incomingsignal’s amplitude. Here, MediSense selects an appropriate fluctua-tion amplitude Nf to distinguish between “Arm Waving” and “BodyRotation” states. Given that the gyroscope is installed at the waistposition of a patient, the amplitude of incoming signal fluctuationis noticeably larger for the “Body Rotation” activities compared toarm gestures. Based on reviewing samples collected from volun-teers in our controlled experiments (adults ranging 50-102 kg), wenoticed that a fluctuation amplitude of Nf = 30 was suitable fordistinguishing the two motions. Therefore, within windowT , if theaverage signal amplitude of the signals are < Nf , our algorithm

SenSys ’17, November 6–8, 2017, Delft, Netherlands Jaeyeon Park et al.

Figure 9: Accuracy and recall for our motion classificationscheme with varying gyroscope sampling rates.

classifies a window activity as “Arm Waving”, else, as “Body Rota-tion”. Note that our algorithm in its current state cannot distinguishbetween detailed actions within the four target motion states, butthe four-stage classification is currently sufficient enough for ourapplication goals. We also point out that for our evaluations, weselect system parameters such as T , Nf , Vmin experimentally, andleave parameter generalization as part of our future work.

4.4 Filter EvaluationsWe evaluate our motion classifier using patient data collected overa 2-month preliminary pilot study at our target ICU prior to ouractual deployment. Through this pilot study, we collected bothmotion sensor data and physiological signal samples from a total of20 individuals summing up to ∼500 minutes of data. For evaluatingthe classification performance, we use only the data that we haveground truth for (collected via manual time recordings), whichleaves us with data from 12 individuals (i.e., ∼370 minutes of data).

4.4.1 Impact on Sampling Rate. Through experimental analysis,we show the impact of gyroscope sampling rate on the detectionperformance. Figure 9 plots the accuracy and recall with respectto different sampling rates of the gyroscope sensor. Intuitively,we can acquire better performance with higher sampling rates.However, as the sampling rate exceeds 300 Hz, we see a decreasein performance. This degradation is mainly due to the gyroscopereacting too sensitive towards small vibrations. We can also seefrom Figure 9 that the classification performance for 150 Hz and200 Hz are almost identical, both achieving an average accuracy of>90%. While the difference in power consumption was similar for150 Hz and 200 Hz, we select to configure MediSense’s embeddedplatforms to sample their gyroscopes at 150 Hz to balance thetradeoff between the accuracy and the memory and processingoverhead on the devices.

4.4.2 Application-level Evaluation. We plot a sample time-seriesof a patient’s ECG, plethysmograph and EWMA-passed gyroscope

Figure 10: Time-series of a patient’s ECG, plethysmographand gyroscope readings with different motions.

TP FP FN TN Accuracy Precision RecallStay Still 17.7% 3.9% 5.8% 72.6% 90.3% 81.9% 75.5%Arm Wave 43.8% 6.7% 5.1% 44.3% 88.1% 86.7% 89.5%Body Rotate 21.1% 4.1% 4.7% 70.1% 91.2% 83.8% 81.6%Body Shake 7.3% 2.3% 1.4% 89.0% 96.2% 75.5% 83.5%

Table 2: Motion classification results from 370 min. dataset.

readings in Figure 10. Within this time frame, as the figure’s textstates, the target subject (i.e., patient) moved his right arm (e.g.,arm with plethysmograph sensor and close to the ECG sensor onthe chest), shook his body in pain, waved both arms, rotated (orrolled) his body on the bed multiple times, and came to a calmstate as the clinical staff reached him. Furthermore in this figure,we show regions of where our filter failed in detecting the groundtruth state of the patient. Note that our filter was very successfulin detecting the states while the patient was moving. The failedclassification was in the initial states of the patient being still. Wepresent these physiological signal traces and filtering results toshow how motions classified through our scheme can be used aspointers for identifying noisy physiological signals.

As we present in Table 2, when computing over the full datasetwith ground truth values, the motion classification algorithm ofMediSense shows an average accuracy of 91.37% and a recall of∼80%. While not perfect, we see this as a promising level of accu-racy. Nevertheless, we point out that the recall value of the “patientstill” state is relatively low: meaning that MediSense classifies mo-tions, despite when the patient is stable. This is mainly due to thegyroscope readings fluctuating while Th∆ is set low, and a peak isdetected. To address these current limitations we plan to improveour adaptive threshold adjustment scheme with more patient data.

5 SYSTEM ARCHITECTURE OFMEDISENSEWe now discuss the overall system architecture and software sub-systems consisting MediSense. We discuss how MediSense mapseach in-bed sensor to a patient monitor, how the physiological sig-nal and motion data streams are aligned in time, and how motiondata is delivered from each in-bed sensor to the server to accuratelyclassify patient movements for noisy physiological signal filtering.

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Figure 11: RSSI summaries collected by nodes positionedat each bed location in the ICU (c.f., Figure 4) for packetstransmitted from the gateway. The similarity in RSSI andhigh variations suggest that using well-known localizationschemes such as RSSI finger-printing will not be suitable.

5.1 Device-to-Bed LocalizationThe main purpose of our system is to filter out noisy physiologicalsensor measurements due to patient motion on the beds. Formercomes from the bed-side patient monitors, and the latter from in-bed motion sensors. Thus to do this, we must first identify whichin-bed sensor corresponds to which bed-side monitor given the factthat beds can move in and out of, or relocate within, the ICU. Onesimplifying aspect to this problem is that the number of bed-sidepatient monitors (i.e., the possible locations for the beds) are fixedand static, there can be at most one bed at each monitor, and a bed isalways closely located adjacent to a monitor when a patient on thebed is being monitored. When a bed is outside of the ICU or mobile,it need not be localized since there is no patient monitor data tocorrelate with. Therefore, the localization problem is reduced tofinding the closest bed-side monitor from the in-bed sensor.

As depicted in Figure 4 and explained in Section 3.2.2, we placedone location-beacon node next to each bed-side monitor. In ourpreliminary studies, we have explored and investigated the useof various localization schemes such as RSSI-based trilateraliza-tion [21], triangulation [28], and RSSI finger-printing [16] usingthese location-beacon nodes. However, as can be seen in Figure 11,RSSI readings (4 Hz interval) at several locations within the ICUvaries significantly over time and space to render these attemptsuseless. For this reason, and by utilizing the fact that a bed is alwaysclosely located adjacent to a monitor when a patient on the bedis being monitored, we adopted a simple ‘strongest-RSSI’ schemewhere each in-bed sensor node will locate and identify itself withthe location-beacon node from which it had received a beaconpacket with the strongest RSSI. More specifically, each location-beacon node will transmit a beacon packet with its ID every 1.5seconds. When an in-bed sensor node receives these beacon pack-ets, it records the ID of the location-beacon with the strongest RSSIabove a pre-determined thresholdThRSSI . Then, if the same location-beacon remains the strongest for contiguous 60 seconds withouthaving a new strongest beacon, then the in-bed sensor node deter-mines that this location-beacon is the one at the closest bed-sidemonitor and locates itself at the IDth location by identifying itselfas the ID number provided by the beacon. On the other hand, if thelocation-beacon from which it had received a packet with strongestRSSI changes within 60 seconds, or if it does not receive beaconpackets with RSSI greater than ThRSSI it restarts the localization

Figure 12: CDF using a 24 hour trace from all in-bed sensorson the number of additional transmissions needed to trans-mit a packet to the gateway if the first transmission fails.

process. We note that MediSense takes average samples of RSSI toavoid the impact of instantaneous RSSI measurement peaks thatneighboring nodes may generate which can lead to infinite loops inthe localization process. This simple scheme was able to correctlylocate all in-bed sensor nodes with the bed-side monitors, with100% accuracy, and also exclude all mobile beds since the mobileroutes are sufficiently far away enough to not be located.

We note that timing parameters in the localization scheme areselected as a result of data collected from an observational study, inwhich we manually logged the duration of how long each patientbed preparation took after operations or medical examinations.Specifically, when a patient’s bed first arrives at the ICU after theinitial operation, the average time required for the clinical staff tomove the bed to the correct location, prepare the bed, and attachproper sensors to the patient was ∼31 minutes. For medical exami-nations throughout a patient’s stay in the ICU, this process took ∼6minutes on average. Therefore, we target our localization schemeto make a decision on the bed location within this time frame.

5.2 RoutingThe design goal of our routing system inMediSense is that it shouldbe simple enough to be implemented on embedded platforms whilebeing robust, scalable, reliable, and practical. The routing systemmust find a path from an in-bed sensor node to the gateway as longas the bed is in the ICU, have low latency for better time synchro-nization, and routing state on the sensor nodes should be minimaland constant. Most importantly, it should achieve high packet de-livery reliability and work effectively in the mission-critical ICU.To satisfy all of these requirements, we designed a simple routingenvironment that combines “best-effort single-hop” delivery alongwith “beacon-as-backbone” routing.We note that a solid WiFi con-nection may help ease the routing process of packets generatedat the patient beds. However, this may not always be available,and our ICU’s WiFi is already used for many clinical systems. Toavoid colliding with these systems in any way, we designed theaforementioned routing scheme for our pilot studies.

The first part, “best-effort single-hop”, is a simple 1-hop transmis-sion. An in-bed sensor node will attempt to transmit directly to thegateway in 1-hop as long as it is within reach. When experiencingpacket loss (if it fails to receive an ACK from the gateway), thein-bed sensor node will retransmit the packet at the link layer up tofour times. For selecting this maximum retransmission threshold,

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we performed an experiment within the target ICU, and in Figure 12we plot the CDF of the number of additional transmission attemptsmade by each node to successfully deliver its packets to the gatewaywhen the first transmission attempt fails. The samples used for thisplot were collected over 24 hours for all 10 nodes in a single ICU.

However, if an in-bed sensor node is unable to deliver its packetsto the gateway in 1-hop within four retransmissions, then it willfall-back to the second strategy, the “beacon-as-backbone” scheme,where it uses the location-beacon nodes in the ICU to relay packetsto the gateway. That is, the in-bed sensor will act as a leaf node andselect a location-beacon node to delegate the packet delivery, andthe location-beacon nodes will form a routing backbone to deliverpackets to the gateway over multihop, if needed.

The design choice of utilizing location-beacon nodes for mul-tihop routing instead of the in-bed motion sensor nodes comesfrom three major reasons. First, the in-bed sensor nodes are alreadyperforming various computation for sensor data processing andmotion classification, whereas the location-beacon nodes are lightlyloaded with minimal functionality. Thus, we wanted to distributethe load. Second, location-beacon nodes are statically located withinthe ICU while in-bed sensor nodes are potentially mobile. Usingthe location-beacon nodes provides more static and robust rout-ing paths, whereas using the mobile nodes will inevitably demandenergy overhead vs. responsiveness trade-off in the routing proto-col. Lastly, since location-beacon nodes are statically located nearthe bedside monitors of each bed, they can be wall-powered whilethe motion sensing platforms on the (potentially) mobile beds arebattery-operated. As a result, the location-beacon nodes are rela-tively free from energy constraints and provide opportunity for aresponsive and proactive routing backbone.

Location-beacon nodes form a routing tree towards the gatewayusing a simple MultihopRSSI routing protocol [13], which usesRSSI as the primary link metric for selecting routing parents andshortest hop-count for the path metric. We chose this over othermore advanced routing protocols such as CTP [12] or RPL [29]because it is simple, consumes less memory and computation, andreliable enough as long as RSSI is above a certain threshold [26].

5.3 Time SynchronizationTo filter noisy physiological signal measurements due to patientmotion on the beds, we not only need to identify which in-bedsensor corresponds to which bed-side monitor and classify the re-spective motions, but also need to align the two data points in time.Specifically, given that motion data is collected on MediSense’sembedded platform and physiological data is gathered througha separate interface, time synchronization between the two datapoints becomes an important issue. However, since we are not cor-relating the two data streams directly, but only filtering out smallportions of the physiological signal data from being used as clinicallearning systems’ training sets, we do not require very tight timesynchronization. For such application purposes, a sub-second accu-racy would suffice. For this reason, we choose a simple approachwhere we time-stamp the in-bed sensor data at the gateway usingthe reception time. For this, MediSense makes two compensations.Since the in-bed sensor nodes transmit sensor data in batches offive samples, and since it may buffer the samples before they are

transmitted, we calculate the offset between the time of the firstsample in the packet and the time when it is transmitted, and sub-tract the offset. Furthermore, if a packet has been retransmittedseveral times, or if the packet has travelled over multiple hops (viabeacon-as-backbone routing), we estimate the latency by multiply-ing the number of transmissions with the average transmissiontime of a single packet and subtract this latency. We note that theinformation on packet retransmissions or backbone network usageis included in the message headers. Overall, the latencies of packets,either due to retransmissions, multihop or buffering, are less thana second in most cases; thus, time-stamping at the gateway servessufficient for our purpose.

5.4 Power and EnergyOn the application level, a core challenge that MediSense faces isthe fact that nodes are deployed in real clinical settings; therefore,system engineers cannot freely access components in the system forissues such as battery recharging. As a result, despite conductinga number of computationally heavy tasks, it is important that thenodes (especially the battery powered in-bed sensors) maintaina sufficient enough life cycle. Specifically, the clinical staff at ourdeployment site suggested that we try to keep the recharge cycleto at least one month and manage a few additional days of bufferto make sure that the patients are discharged prior to any systemmanagement-related operations. Based on this requirement, wetarget the lifetime of the in-bed sensors to 33 days, and design itsradio duty cycling protocol and peripheral management schemeswith respect to this target goal.

While this is a challenging goal, on the positive side, the simplic-ity of our networking protocols allow the in-bed sensors to veryaggressively duty-cycle their radios. In MediSense, since there is notwo-way traffic (other than link-layer ACKs) and in-bed sensors areconsidered as leaf nodes, the radio is turned on only when trans-mitting a packet, resulting in a 0.92% radio duty cycle. In the idealcase with no packet retransmissions, the in-bed sensor consumesan average of 15.46 mA. With a 12,800 mAh rechargable batteryattached, this translates to 34.50 days of continuous operation.

6 EVALUATIONAs part of a fully functional deployment, we deployed MediSense inall 30 ICU beds within a university hospital trauma center. The de-ployment process took ∼3 months to finalize, given that we startedwith a small pilot study of three beds to validate the effectivenessof each component aforementioned.

6.1 Motion Classification and Data FilteringUsing the in-bed sensors that were in operation for more than 30days, we present statistics on the motion classification. Unfortu-nately, given that camera recordings were not authorized as part ofour IRB, we cannot measure the accuracy of our proposed algorithmas in Section 4 with controlled experiments.

We observed an average of 375 motion events per day, where91.51% were arm waving, 7.66% were body rotation and the last0.83% were body shaking activities. Given that we remove biosig-nals that are within ±10 seconds of the detected event, we removedan average of 2.417% of the entire physiological dataset collected

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Figure 13: RSSI values captured for associated and neighbor-ing location-beacon nodes. These are traces captured fromin-bed sensor at location 4.

MOBILE BED

Figure 14: Illustration of a mobile bed entering the ICU andbeing positioned at location 2.

each day. Samples collected from the patient with the most amountof movements experienced a total of 3,641 events over the dura-tion of 9.71 days, and 5.6 hours of the patient’s physiological datapoints were removed and marked as motion-caused noise. Higherpercentage of arm motions compared to our observational studyis due to the fact that MediSense outputs results every 5 secondscontinuously; thus, a continuous arm motion would count as 1occurrence in our observational study while the same motion isdetected over many samples in MediSense. We note that, while outof the scope of this work, our target application using these physio-logical signal information is in predicting sepsis occurrences in thetrauma center. We plan to evaluate the impact of this data filtering(on an application perspective) as part of future work.

6.2 Bed Localization PerformanceWe now evaluate the performance of our localization scheme. First,given thatMediSense relies on the RSSI values of beacon nodes at in-bed sensors for localization, we start by examining the RSSI samplesfrom near-by beacon nodes. Specifically, Figure 13 presents the RSSIsample readings at the in-bed sensor node located in position 4 forbeacon packets from location-beacon nodes installed in positions3, 4 and 5 of our ICU. Despite being in close proximity, Figure 13shows that there exist noticeable differences in RSSI between theassociated beacon and other neighboring nodes. This result serves

as preliminary evidence that our RSSI-based localization schemecould suffice for operating in our target environment. Given thatthe ICU beds are crowdly positioned (i.e., ∼1 meter apart) in ourcase, this difference in RSSI will be even more significant in lesscrowded ICU environments.

Next, we examine how the in-bed sensor observes RSSI valueswhen it is mobile, specifically when it enters the ICU and “parks”at location 2 as illustrated in Figure 14. Figure 15 presents traces ofthe RSSI values observed by a mobile in-bed sensor for all location-beacons in the ICU while following the path in Figure 14. When abed is mobile, the RSSI levels do not exceed the detection threshold,and once exceeding ThRSSI near location 2 (c.f., the ‘enter’ state inlocation 2 plots of Figure 15), MediSense makes a system-level deci-sion on the bed’s location. The observed localization latency was∼60 seconds, which satisfies our application requirements of prop-erly recognizing the bed location prior to the clinical preparationprotocols finishing (e.g., ∼6 mins as discussed in Sec. 5.1).

Overall, based on ground-truth information from the EMR andnurse reports, MediSense was able to correctly locate all in-bedsensor nodes with the bed-side monitors with 100% accuracy, andalso exclude all mobile beds from being associated with a patientmonitor without any false positives.

6.3 Packet Delivery and Routing PerformanceTo evaluate the packet delivery performance of MediSense, we usethe packet reception ratio (PRR) and the average number of requiredtransmissions per packet (ATX ) as the metric where ATX is definedas the ‘total number of actual transmissions made’ divided by the‘number of successfully delivered unique packets’. ATX is used tomeasure the overhead due to packet retransmission, and it shouldideally converge to the well-known ETX (expected number of trans-missions) metric [5] (or vice versa) in the link layer unless the linkis completely broken (in which case PRR is zero and ATX becomesinfinity). The minimum value of ATX is 1 when all transmissionsare successful on the first try, and smaller ATX value means bettertransmission performance.

In our deployment, majority of the packets transmitted by the in-bed sensor nodes are successfully delivered to the gateway via 1-hop.Furthermore, during the first week of full deployment in TICU-A, alllinks towards the gateway show a low ATX of <1.02. Nevertheless,there were cases throughout our deployment in which more than4 retransmissions were required. In such cases, as we discussed inSection 5.2, the in-bed sensors send their packets to the associatedbeacon node to deliver packets via the backbone network. Thepacket delivery performance once packets from the in-bed sensorsreach the backbone network also shows a very reliable performancewith an average ATX of 1.02.

6.4 Node Lifetime DistributionsAs mentioned in Section 5.4, surveys with the clinical staff suggestthat devices in MediSense should operate for at least 30 days with afew extra days for possible maintenance delays. Using the currentdeployment configuration, we managed to run the devices for morethan three full charge cycles. Figure 16 plots the distribution ofnode’s average lifetimes we experienced in our deployment. Thevariances in lifetimes are caused mainly due to the link qualities

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Figure 15: RSSI readings observed by themobile bed for all 10 location-beacons when it enters the ICU as depicted in Figure 14.

Figure 16: Lifetime distribution of nodes deployed at our de-ployment hospital

where nodes located relatively far from the gateway (generally)showed shorter lifetimes. However, the variance is less than 4%only, and most nodes show a predictive behavior, achieving ourgoal of 33 days or more.

6.5 Deployment ExperienceWhile MediSense has been active for 4 months, the initial deploy-ment process took ∼3 months to finalize. During this period, weencountered several non-technical challenges while pursuing ourgoal. The first challenge was, as mentioned earlier, not interferingwith the clinical protocols of the hospital. The medical staff, as wellas the patients, were reluctant to have non-medical personnel (e.g.graduate students) lingering around in the medically critical ICUwith laptops and wireless devices. Furthermore, absolutely no wiresshould interfere with their normal medical procedures, and clinicalstaff did not allow us to install any of our devices in visible sightof the patients. This not only was one of our system requirements,but also provided us with a challenging environment to work in.

The second challenge was the time and access to the deploymentsite, the ICUs. We (computer scientists) did not have the authoriza-tion to directly enter the ICU ourselves, but had to get permission

Patient Normal as Normal Normal as ArrhythmiaNon-moving 100% (211/211) 0% (0/211)

Moving 30.22% (81/268) 69.77% (187/268)Patient Arrhythmia as Normal Arrhythmia as Arrhythmia

Non-moving 4.5% (9/200) 95.5% (191/200)Moving N/A N/A

Stay Still Arm wave Body Rotate Body ShakeAccuracy 93.7% 91.24% 94.03% 96.82%

Table 3: Arrhythmia classification for non-moving ar-rhythmia patient and moving patients without arrhythmia(above), and motion classification results using MediSensefor moving patients.

from, and be accompanied by, a medical personnel to enter theroom. This restricted the time and frequency of our access to theICUs. Furthermore, we had no control over which beds are andwhich are not occupied by the patients, which meant we had verylimited chance of doing any controlled experiments.

Lastly, capturing ground truth data for our experiments itself wasa challenge. For capturing accurate ground truth data, we wantedto capture as much samples as possible and also have highly precisetimings. However, given that cameras were not allowed as part ofour IRB approval, we had to have either healthy volunteers makespecific motions (e.g., controlled experiment) or have a studentmanually jot down movements when a real patient made motions.This limited the amount of ground truth data we could capture andthus, we are in the process of persuading the clinical IRB committeefor the need of a camera installation.

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6.6 Case-study: Arrhythmia DetectionTo present a clinical example of howMediSense can benefit, we takeECG signal samples collected from ten patients. Of these ten pa-tients, six patients show signs of arrhythmia, while the other four donot. Using the ECG signals from the five of the patients with arrhyth-mia, we train a multi-class Support Vector Machine (SVM) with thecount, intervals, and variances of heartbeat peaks as features for150,000 10-second ECG traces. Note that data from arrhythmia didnot contain any artifacts caused from patient motions, as the datawas collected from unconscious patients in the ICU, while the fourwithout arrhythmia contained data affected with motion-artifactsas detected by MediSense. Ideally, when testing with the remainingdata, all data except for a subset of ECG samples collected from thepatient with arrhythmia should be classified as non-arrhythmia.Results in Table 3 shows that our classifier achieves 100% and ∼95%accuracy when classifying non-arrhythmia and arrhythmia datasets, respectively, for a non-moving patient. However, when normalpatients make motions on the bed, the classifier detects arrhythmia-like occurrences from the ECG signals despite not experiencingarrhythmia, which leads to false alarms on a clinical perspective.Fortunately, with MediSense, we note that ∼93.95% of the on-bedmotions were successfully detected and filtered. This case-studysuggests that motion-affected physiological data can negativelyimpact the quality of diagnosis for clinical learning systems, andalso shows that MediSense can ease such challenges by identifyingnoisy samples that are affected by patient movements on the beds.

7 RELATEDWORKThere are several pieces of prior work that attempt to detect patientmotion at hospitals. For example in [19], authors have developeda computer vision based fall prevention system for hospital wardapplication where 3D depth cameras were used to identify activitieson ICU beds. Features from multiple modalities and channels arefused via a multiple kernel learning framework for training theevent detector. However, such an approach is costly and requirescamera recordings of a sensitive environment. Our work takes amore indirect approach in identifying patient motion using onlymotion-related sensors such as gyroscopes and accelerometers.Furthermore, given the responses from our IRB committee, it isdifficult to assume that using cameras can be a general approach.

In a different direction, there has been some efforts to cancelnoise from physiological signals. In [1], an active noise cancellationmethod using a MEMS accelerometer is developed for recoveringcorrupted wearable sensor signals due to body motion. The methodis developed for a finger ring photoplethysmography (PPG) sensorwhich detects the hand acceleration, and is used for recovering thecorrupted PPG signal. The correlation between the acceleration andthe distorted PPG signal is analyzed, and a low-order FIR modelrelating the signal distortion to the hand acceleration is obtained.The work in [24] designs a system for wearable PPG comprisinga magnetic earring sensor and wireless earpiece. The sensor con-tains an embedded accelerometer to provide motion reference foradaptive noise cancellation. In [9], authors propose a solution toeffectively remove artifacts from intracranial pressure (ICP) moni-toring signals collected in neuro intensive care units (NICU). Theauthors identify that ICP signals often contains large amount of

artifacts, and they not only directly lead to false alarms in automaticintracranial hypertension (IH) alert systems, but also severely con-taminate the characteristics of the underlying signal, which makesaccurate forecasting of impending IH impossible. Therefore, theyproposed a method to effectively detects artifacts by decompos-ing the ICP monitoring signal with empirical mode decompositionmethod, and also an iterative filtering method to extract artifactsfrom the decomposed components of ICP signals. The detectedartifacts are then imputed based on the auto-regressive movingaverage model to preserve the original characteristics of the ICPsignal. Such schemes analyze and modify the physiological signalmeasurements directly; thus, do not require additional sensing inthe physical environment. However, such noise cancellation meth-ods can only be indirect implications of motion artifacts, and cancause misleading data interpolation, which can be a risky approachfor clinical applications.

In designing classifiers for detecting human motion or activi-ties, there has been a wide body of work. For example, the workin [11] and [25] show the effectiveness of using EWMA filters incancelling motion sensor noise, and works such as [7] discuss howlow-complexity algorithms can be used to classify a person’s activ-ity. Given that MediSense performs classification as a stand-alonecomponent, we find these work as supportive work to simplify orenhance our motion classification algorithm.

Finally, the work in [10] have developed an integrated and intelli-gent system, namely iSyNCC, to enhance the effectiveness of patientmonitoring and clinical decision makings in NICUs. A modular 2-tier system is proposed to enable remote and centralized patientmonitoring and provided computational intelligence to facilitateclinical decision makings. There has also been several proposals forsuch intelligent systems in hospital applications, and the work in[6] provides a good review of such monitoring and data acquisitionsystems for patient care and research in critical care.

8 CONCLUSIONProviding learning systems with proper training data can be of ut-most importance as we design complex and mission critical clinicaldecision support systems (CDSS). This work examines the impactof typical patient motions on the physiological signals collected inclinical environments, and presents a complete system calledMediS-ense, which can sense, detect and identify noise-causing motionsthat affect physiological signal data quality when made by patientson their hospital beds. MediSense utilizes wirelessly connected em-bedded platforms to collect gyroscope-based motion sensing datafrom ICU beds, and is equipped with a light-weight classificationalgorithm to infer patients’ motion activity states. We correlate thisinformation with the physiological signals collected from bedsidepatient monitors to filter noisy physiological signals from the CDSS’training process. We deploy our system on 30 intensive care unit(ICU) beds at the Ajou University Hospital Trauma Center, andevaluate its performance from real-patient traces collected at theICU through a pilot study. Our results show that MediSense well-identifies physiological signal-affecting motions made by patientsat ICU beds and the correlation for determining motion-impactedfaulty samples are high. Furthermore, our case-study with arrhyth-mia patient data shows that motion-affected physiological data

SenSys ’17, November 6–8, 2017, Delft, Netherlands Jaeyeon Park et al.

can negatively impact the accuracy of clinical learning systems,and MediSense can help. We foresee this work as an essential steptowards realizing reliable learning systems in the clinical domain.

ACKNOWLEDGEMENTSWe thank our shepherd, Dr. Branislav Kusy, and the anonymous re-viewers for their helpful feedback. We would also like to thank theclinical staff team at the Ajou University Hospital Trauma Center,especially, Ms. Kyungjin Hwang, Dr. Jayun Cho, Dr. KyoungwonJung, for their great help in designing, installing and maintain-ing MediSense. This work was partially supported by the Ministryof Trade, Industry and Energy (MOTIE) and Korea Institute forAdvancement of Technology (KIAT) through the International Co-operative R&D program, by the Korea Health Technology R&DProject through KHIDI funded by the Ministry of Health & Welfare,Korea (HI16C0982), by the Institute for Information & communica-tions Technology Promotion(IITP) grant funded by the Ministry ofScience and ICT (2017-0-00526) and by the Basic Science ResearchProgram through the National Research Foundation of Korea (NRF)funded by theMinistry of Education (NRF-2017R1D1A1B03031348).

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