Multifunctional Pressure and Oxygen Sensoring Health Monitor System for Long-term Computer Users

Ying-Xun Lai1, Hung-Yu Chang, Wei-Yi Lin, Wen-Chang Tsai and Yueh-Min Huang2 Department of Engineering Science

National Cheng Kung University Tainan, Taiwan eetaddy@gmail.com1, huang@mail.ncku.edu.tw2

Abstract—Computers contributed tremendously to the development of human civilization in terms of living convenience improvement, however many health problems may occur to long term computer users, occasionally resulting in death within certain cases. Due to these effects, this study proposes the multifunctional health monitor service, using a blood-oxygen sensor mouse as well as a pressure detection seat cushion to detect the user blood oxygen level and seating posture. Using these sensing design applications that can help the user to offer a long term monitor service for the self-management of users to prevent long term negative effects and solve these societal problems.

Index Terms—Blood-oxygen, Seat Detection, Health Monitor

I. INTRODUCTION According to the online website Pollster in a survey in May

of 2009 called “Do you keep good seating posture”, it was found that “Always remaining in good seating posture” covers the least percentage, only 8.05%, “Most times” covers 23.68%, “Sometimes” covers the most at 42.41%, while “Never caring about my seating posture” covers 25.85%. So “sometimes” or “never caring” covers a total of 68.26% of the surveyed, from these results it is proven most of the population doesn’t care if their seating posture is correct.

If these pains last for a long time it is classified as a slow type pain, not only disturbs the user lifestyle but also affect work mood and emotion, becoming a huge issue in the work place. This system proposes a health monitor system using the blood-oxygen detection algorithm which is made up of a few main components as shown in Figure 1.

Figure 1. Multifunctional Health Monitor System The purpose of this study is in managing and monitoring the

health status of the user when using computer or sitting on the chair. By using the blood oxygen mouse detecting the current heart rate and blood oxygen in determining the vital state of the person in preventing lack of exercise due to computer usage, the system can prevent poor blood circulation, discomfort of the body. The pressure cushion uses the buttock pressure to determine whether the person is sitting slanted and immediately informs the person to correct the posture. This helps develop proper sitting habits, to decrease Computer Usage Disorder related diseases. This system provides remote monitoring as well, personal history records and information about many different types of diseases and methods of improvement allowing a complete system for personal health management. The contributions of this research are as listed below:

Posture Recognition of Users with Different Heights: Targeted at variety of users, by using a learning mechanism and sampling the pressure distribution of different seating postures allows the construction of the user model, then using data distributed algorithm for support to build a miniature model of the seating posture then this model will be used for posture detection.

Personal Information and History Record Portability With the availability of smart phones, mobile devices can be the storage media for health information. Combining Near Field Communication (NFC) technology to allow computers to authorize user accounts and data storage to release the restraint of portable information.

Cross Platform Service The original system was developed using the Windows .Net Framework. By using the open software Mono to build a Common Language Infrastructure that is compatible with the .Net Framework, the system can then operate on many cross platforms such as Linux, Mac-OS, allowing different platforms to be able to use this service.

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II. RELATED RESSEARCH Currently in terms of healthcare, most of the sensor

functions, accuracy and stability are the main topics for discussion. The acceptability and willingness of users are seldom discussed. Good medical equipment and environment is also important, but patients or users willingness to use the equipment also affects the medical effects of the treatments. To combine healthcare ubiquitously with smart life can heavily increase the user acceptability and healthcare effects. In the healthcare detection related topics, the main directions of discussion still focuses on the four main vital signals. One is the oxygen saturation value which has received popularity in the past few years. It mainly uses the equipment layer transfer interface [1-3], light blood oxygen analysis [4], application level quality of sleep [5], breathing termination analysis [6], driver fatigue analysis [7] as the main targets. These are hardly integrated into daily life styles. Discussing the oxygen saturation can be split into saturation of peripheral oxygen (SpO2), venous oxygen saturation (SvO2), arterial oxygen saturation (SaO2), tissue oxygen saturation (StO2). The arterial oxygen saturation module requires invasive measuring methods which offers the highest accuracy but requires invasion into the human body and not suitable for long term monitoring or elderly healthcare. Henceforth non-invasive saturation of peripheral oxygen measuring method is the main method used for long term healthcare. With the advances of light technology, PhotoPlethysmoGraphic (PPG), has been widely used in non-invasive medical tests, while the market pulse blood oxygen saturation measuring devices is also a main application that is based on PPG. Pulse blood oxygen saturation measuring method uses two light sources, infrared LED and one red LED. Using two LED of different wavelengths, the oxygenated haemoglobin (HbO2), and reduced Hb, of the blood can be measured. Using the calculation of PPG and after removing the noise the blood oxygen percentage can be calculated. The current SpO2 probe can be classified as reflectance and transmission types. The reflectance type uses the probe and receiver close to the skin surface, by the absorbance of the different red blood protein absorbance rate in the blood caused by the reflectance difference to calculate the blood oxygen saturation, which will not be affected by the measuring load wideness that solve the transmission specific measure point limitation. However the accuracy is still not up to par with transmission probes.

Transmission probe type blood oxygen saturation measurement method uses a finger sensor that uses a finger piece fitted at the finger tip, which cannot be integrated into daily life behaviors. To measure, the user has to halt the current work or whatever he or she is doing to specially use the probe. The same time traditional blood oxygen probe mostly uses a miniature or palm screens to display the current blood oxygen state. This type of detection mode can only be used to detect normal state patient or users. If the user has an accident during the measuring process there is no mechanism to deal with these situations.

III. SYSTEM SOFTWARE ARCHITECTURE AND IMPLEMENTATION DETAILS

This section will list the functions included such as shown in Figure 2.

Figure 2、System Function Architecture

A. Device Layer The system layer lower level device layer uses a custom made mouse for blood oxygen PPG detection probe. This probe design included the hardware design model and circuit path design. The hardware model, the user does not need to change any behaviors. This paper proposes the method to use the measure point at the finger tip. The purpose is normal user behaviors, using the fingers on the mouse, the thumb and pinky is used to move the mouse while the other three fingers are for the button and scrolling functions. The three fingers move the most which would affect the PPG light source detection due to vibrations, causing error in measurement. The pinky also bends according to different user behaviors. Hence the most suitable finger for measurement is the thumb with the least motion, as shown in Figure 3a. The seat cushion uses pressure detection with pressure detection placed as shown in Figure 3b, then using wireless module to send the values to the computer for posture detection.

Figure 3. Mouse Model (a) and Pressure Placed Design (b)

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Figure .4 Program Structure of Proposed System.

B. Program Structure The entire program structure is shown in the figure 4. The user enters the program through Entrance_View. The attribute under this class includes login, registering user information which includes user name, weight, email, and contact information. During login, the user can use the NFC function on the phone to enable fast login. Main_View is the main interface of the entire data presentation. The functions here include history information, user information setting, and detection display. Detection display further classified into blood oxygen, posture and warning. Posture detection includes left, right, cross-left, cross-right, front and normal states for the main postures. Warning function designs to allow warning during life threatening states to contact the user or use messaging to contact emergency contacts. C. Blood Oxygen Detection In the middle part of the system architecture, the blood oxygen value is used to estimate the oxygen desaturation index to predict the life threatening state of the user as the algorithm. This system has an analysis software that uses the background mode to record the user blood oxygen saturation percentage and analysis the rate of change, including Max, Min, Avg, T90%, T89%, T88%, T87%（SpO2 < 90%, SpO2 < 89%,

SpO2 < 88%, SpO2 < 87%）, blood saturation parameters. Using these the user health state can be analyzed to see if the user is currently in a life threatening situation. When the user uses the computer, the designed blood oxygen mouse will receive the blood oxygen signal from the user fingertip. Using the blood oxygen detection microprocessor embedded into the mouse, and combining the blood oxygen analogue PPG analysis to change to the digital value for blood oxygen saturation percentage, the user blood oxygen saturation value can be measured. Finally using serial interface, the result is

transmitted to the desktop for further analysis of vital signs and warning requirements. If the value reaches an abnormal value, the data would be sent to the hospital end along with the original data, rate of change, abnormalities, and so forth to offer the best instant medical analysis. D. Posture Detection The entire posture detection process chart is as shown in the figure below. The user will let the system learn the basic posture values and vital values during registration. The user can see the posture and vital information while using the computer. When the sensor receives the values, the algorithm will detect the current posture state. Sort differentiates the cross-left foot and cross-right foot with the other postures. The force sensors are separated in to the left half and right half. When crossing the left foot the left side sensors will detect that the foot isn’t pressed on the cushion and same for crossing the right foot using the right side sensors. Using this mechanism the cross-foot states can be easier detected. When both sides detect pressure values, the program enters a compare function. The values are standardized for different statistical analysis, so the result will not be affected by a single abnormal sensor. After the sensors are standardized and tabulated before seating, a basic reference value is saved. After you sit on the cushion, both sides will use the reference value as the basis of comparison. If the left side value differs from the right side by a set value, the state will be set as leaning left, the reverse showing the right side value differing by a set value the state is determined as leaning right. If the difference is not big the state is normal. The program reads the value every 0.25 second; hence one second will get four sets of values. Every set of values will use the algorithm to detect the posture, meaning four results every second. The program itself updates the display state every ten seconds. If within the ten seconds more than half the results are abnormal, the user

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will be sent a warning to return to the normal posture. Table 1. Result of Seating Precision Experiment

Lean Left Lean Right Cross Left Cross Right Lean Forward Normal User#1 36/36 25/32 30/36 32/34 22/36 36/36 User#2 34/34 37/37 30/38 28/35 28/34 33/35 User#3 34/35 35/37 22/38 27/34 12/30 28/30 User#4 18/34 34/34 28/30 20/32 22/40 29/38

User#5 22/28 30/38 28/35 28/37 27/33 24/32

IV. IMPLEMENTATION AND RELATED RESULT DISPLAY This research uses the mouse by Ttesports as basis to

create a pulse blood oxygen light detection model, by integrating the light blood oxygen measuring module, light driver module, and serial communication module into the mouse, then using the notebook as the user terminal. The research uses a mobile app for users to read history information, while another related computer terminal to monitor the current blood oxygen and seating posture.

A. Seating Precision Experiment In seating precision experiment, five samplers were

tested. In explaining the entire process and the testing motions, two samplers (1,2) had technique training, the other three haven’t had (3,4,5). Every person on average had 32-40 different posture detection experiment, where the results are shown as Table 1. Where we can that lean forward detection rate is lower, because different people might have their center of gravity not change as much causing the result to be detected as normal. While user #4 Lean Left and Cross Right are also detected as similar cases.

Figure .5 Oxygen Assessment Analysis

B. Blood Oxygen In the software part, the thesis proposed a blood

oxygen value estimation method as shown in Figure 5. The A area are the Δ SpO2 that resides higher than 3% within 5 minutes, where B is the area where Δ SpO2 resides greater than 5% within 5 minutes, this estimation method will use the current blood oxygen method to estimate the severity of the current state and also warn the user to take suggestive action to prevent fatal or harmful events from occurring.

IV. CONCLUSION This research proposes a Multifunctional pressure and

oxygen sensoring health monitor system. The main usage is for incorrect seating posture or tiredness happening due to stress or overwork when using the computer for a long period of time. Incorrect postures can reach recognition precision up to 89.45%, which can allow health monitoring for long term computer work. For future work, other devices can also be integrated into the system to implement digital and real life ubiquitous computing. Adding in neuro-networking, professional system and Bessel Network Algorithm, it can offer a complete symptom analysis system and health care mechanism.

ACKNOWLEDGMENT This paper is a partial result of project No. 101CC02 conducted by National Cheng Kung University under the sponsorship of the National Science Council and Southern Taiwan Science Park Administration, ROC.

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