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Automation in Construction 44 (2014) 227233

Contents lists available at ScienceDirect

Automation in

j ourna l homepage: www.e lsform muscular work. Thus, devices capable of assessing worker's phys-iological conditions and, eventually, work physiological demands canplay a crucial role in supporting the development of the needed tools,

construction sites. For example, some studies used monitoring toolsthat could not continuously monitor the subjects. Other studiesemployed cumbersome and/or uncomfortable monitoring devices thatmands can negatively affect safety and productivity due to a decreasein workers' well-being, attentiveness, motivation, and capacity to per-

physiological demands. Nevertheless, most of thmonitoring tools and/or technologies that can bpast decades while it generally increased in other industries [47].Therefore, it is imperative to develop innovative tools, methods, andtechniques capable of improving productivity and safety in construction.

According to Strasser [8], the assessment of workers' physiologicalconditions is a crucial prerequisite in every ergonomics study. Further,several authors [921] suggest that excessive work physiological de-

mands through the assessment of physiological conditions, such as ques-tionnaires (e.g., rating of perceived exertion), oxygen consumption,motion sensors, and heart rate monitoring. Several authors applied oneor more of these techniques to monitor various workforces, such as con-structionworkers [9,10,2325],manufacturingworkers [2629], farmers[3032], and nurses [3335]. These studies successfully assessed work Corresponding author. Tel.: +1 206 685 1676.E-mail address: gianciro@uw.edu (G.C. Migliaccio).

1 Present address: Department of ConstructionManagemBox 351610, Seattle, WA 98195, USA.

http://dx.doi.org/10.1016/j.autcon.2014.04.0130926-5805/ 2014 Elsevier B.V. All rights reserved.or over 5% and 15% of thenoted elsewhere [3], re-uction decreased in the

rithm capable of characterizing worker's activity in terms of productiv-ity and safety behavior.

Numerous techniques are available to assess work physiological de-

total recorded injuries, respectively. Further, assearches indicate that productivity in constrOccupational health and safetyConstruction managementErgonomics

1. Introduction

Despite enhancements in construcworkplace safety and ergonomics, thethe most dangerous and physically demthe US Bureau of Labor Statistics [1,2] istruction industry had over 180 thousanand 738 fatal occupational injuries accouuipment, methods, andction industry is amongindustries. For instance,that in 2011 the US con-atal occupational injuries

methods, and techniques to enhance constructionworkforce productiv-ity, safety, and well-being. In fact, few recent studies used one or moreworkers' physiological parameters in developing tools to improve con-struction workforce performance. For instance, Hsie et al. [21] imple-mented workers' energy expenditure in developing a theoreticalmodel that can be used to generate efcient workrest schedules.Cheng et al. [3,22] utilizedworkers' thoracic posture to develop an algo-Productivity 2014 Elsevier B.V. All rights reserved.

Work physiology coefcient 0.74; percentagPhysiological condition monitoring of cons

Umberto C. Gatti a,1, Suzanne Schneider b, Giovanni C.a Department of Civil Engineering, University of New Mexico, 210 University Blvd NE, Albuqueb Department of Health, Exercise and Sports Sciences, University of New Mexico, MSC04 2610,c Department of Construction Management, College of Built Environments, University of Wash

a b s t r a c ta r t i c l e i n f o

Article history:Received 13 August 2013Revised 8 January 2014Accepted 19 April 2014Available online 10 May 2014

Keywords:Construction workerPhysiological status monitoring technologyWork physiological demand

Monitoring of workers' physafety, and well-being. Recento assess physiological condand cycle ergometer protococonstruction worker movemPSMs by comparing their meduring dynamic activities resuch as heart rate and breastudy. One of the PSMs proveent, University ofWashington,uction workers

gliaccio c,, NM 87106, USAquerque, NM 87131, USAn, 120 Architecture Hall, Box 351610, Seattle, WA 98195, USA

gical conditions can potentially enhance construction workforce productivity,Physiological Status Monitors (PSMs) were validated as an accurate technologys during typical sport science and medicine testing procedures (e.g., treadmillowever, sport science andmedicine testing procedures cannot simulate routines in a comprehensive manner. Thus, this paper investigated the validity of tworements with gold standard laboratory instruments' measurements at rest andbling construction workforce's routine activities. Two physiological parametersg rate were considered. Ten apparently healthy subjects participated in thebe a viable technology in assessing constructionworkers' heart rate (correlation

Construction

ev ie r .com/ locate /autconwould hinder construction workers during routine activities if used asstandard construction equipment. In addition, some monitoringmethods were suitable only for small groups in experimental settings.

Among the available physiological demand assessment techniques,heart rate is very promising for daily and eld situations [13,36,37].

Numerous portable, lightweight, unobtrusive, and comfortable heartrate monitors are available on the market. In particular, innovativedevices called Physiological Status Monitors (PSMs) can monitorheart rate and many other physiological parameters (e.g., breathingrate, body acceleration, and skin temperature) simultaneously.PSMs have already been implemented to monitor police ofcers,re ghters, and soldiers; to test physical performance (e.g., profes-sional American football and soccer athletes); and, in healthcare(e.g., home/remote healthcare). Few studies analyzed PSM validityin measuring heart rate and/or other physiological parameters[3843]. These studies implemented exclusively typical sport sci-ence and medicine testing procedures such as treadmill, walkjogrun, and cycle ergometer protocols. Routine construction workermovements include walking but also lifting and carrying, and repet-

2. Materials and methods

2.2. Participants

The participants of the study were ten apparently healthy partici-pants (seven males and three females, age 23.8 2.9 years., body stat-ure 179 8 cm, body mass 75.5 10.7 kg). All participants wereinformed about the potential risks of the study and provided a writteninformed consent. Further, the participants completed a physical activityquestionnaire based on the PAR-Q [54] and a health history question-naire to verify that it was safe for them to performmoderate physical ac-tivity. The University of New Mexico's Human Research Policies andProcedures Board (HRRC) granted the permission to perform the study.

2.3. Experimental design

228 U.C. Gatti et al. / Automation in Construction 44 (2014) 2272332.1. Description of PSMs and their key features

PSMs arewearable telemetry systems that are capable ofmonitoringnumerous physiological parameters (Table 1) in a non-invasive, auton-omous, and wireless manner. In fact, PSMs are designed to be comfort-ably and continuously worn for several hours and to not hamper anytype of movement. PSMs can include numerous elements, such as bio-sensors, wearable materials, smart textiles, actuators, power supplies,wireless communication modules and links, control and processingunits, interface for the user, software, and advanced algorithms fordata extracting and decision making [51]. In particular, sensors canbe either located in a fabric chest belt/garments (e.g., a vest), or be im-plantable. Two off-the-shelf PSMs were selected for evaluation(Table 2): BioHarness BT 1 (BH-BT) manufactured by Zephyr Technolo-gy Corporation (Annapolis, MD, USA); and, Equivital EQ-01 (EQ-01)manufactured by Hidalgo Ltd (Swavesey, UK).

Table 1Parameters that can be potentially monitored by PSMs.

Parameter Type of sensor

Electrocardiogram (EKG) Skin/chest electrodesHeart rate Skin/chest electrodes or pulse oximeterBreathing rate Piezoelectric/piezoresistive sensorSkin/body temperature Temperature skin patch/probeBody movement AccelerometerBody orientation AccelerometerBlood pressure Arm cuff-based monitorOxygen saturation Pulse oximeterPerspiration Galvanic skin response sensorElectromyogram (EMG) Skin electrodesitive motions, such as arm lifting and thoracic rotations [4446].Therefore, sport science and medicine testing procedures can hardlysimulate routine construction worker movements in a comprehen-sive manner. Further, these studies demonstrated that PSM accuracyin measuring heart and breathing rate depends on the subject move-ment intensity and characteristics. It is, therefore, necessary to imple-ment testing procedures capable of simulating routine constructionworker movements to effectively investigate the PSM validity in moni-toring construction workforce. Thus, the goal of this paper is to validatetwo off-the-shelf PSMs inmonitoring subjects performing static and dy-namic tasks comparable to construction workforce's routine move-ments. The validation procedure is based on the analysis of theagreement between PSM and gold standard laboratory instrumentmeasurements of Heart Rate (HR) and Breathing Rate (BR) during staticand dynamic activities [4750]. In the following sections an overview ofthe selected PSMs is given. Then, the studyprotocol is presented and theobtained results are discussed.Depending on the monitored physiological parameter, different ex-perimental procedures were implemented. HR and BR were monitoredsimultaneously at rest and during dynamic activities (n= ten subjects).Experimental design detailed description is provided in the followingsections.

PSMs determine HR and BR by collecting the heart's electric signalthrough electrocardiography leads embedded in the PSM chest beltand by measuring chest belt expansions and contractions, respectively.Thus, chest belt displacements due to movements during dynamic ac-tivities can affectHR and BR assessment [55,56].Moreover, EMG activity(i.e., electrical signals generated by skeletal muscle contractions) maysuperimpose the heart's electric signal and, therefore, hinder HR assess-ment [57,58]. Thus, to assess PSM validity in monitoring constructionworkers' HR and BR, a series of static and dynamic activities comparableto construction workforce's routine movements were designed(Table 3) to be performed by the study participants.

The participants were prepared according to the following proce-dure. First, the participants were prepared for EKG monitoring (CASEExercise Testing Electrocardiogram, GE Healthcare, Waukesha, WI,USA) at 500 Hz using ve connections (V5, left arm, left leg, right arm,and right leg) to avoid hindranceswith PSM chest belts. The participantswere then asked to wear a nose clip and a mouthpiece connected to ametabolic cart developed by the University of NewMexico Exercise Sci-ence Laboratory to collect the inspired and expired air and, therefore, todetermine BR. Finally, according to the manufacturer's instructions, thePSM chest belts were placed just below the sternum after moisteningthe skin electrodes. The participants accomplished the designed activi-ties wearing one PSM at a time. First, the participants accomplished allthe activities by wearing the BH-BT device. Then, after resting for20 min, the participants repeated the testing protocol wearing the EQ-01 device.

2.4. Signal processing and data analysis

Signal processing and data analysis were accomplished ofine withMatlab R2008a (The Mathworks, Natick, MA, USA) and MicrosoftExcel 2010 (Microsoft, Seattle, WA, USA). PSMs and lab instruments'

Description of measured data

Electrical activity of the heartFrequency of the cardiac cycleFrequency of the breathing cycle (i.e., inspiration and expiration)Skin surface/core body temperatureAccelerations due to body movementsBody orientation according to the gravityPressure exerted by circulating blood on the walls of blood vesselsAmount of oxygen carried in the bloodElectrical conductance of the skin associated with the activity of the sweat glandsElectrical activity of the skeletal muscles

HR and BR signals were re-sampled from the original reporting period(i.e., BH-BT 1 s; EQ-01 15 s; lab EKG monitor 1/500 s; and, met-abolic cart NA) to 15 s.

Data analysis was performed by calculating Pearson productmoment correlation coefcient (r), and by using the BlandAltman

Table 2Sample of BH-BT, EQ-01 technical specications [52,53].

BH-BT

Dimensions 80 40 15 mmWeight (without belt) 35 gAdjustable shoulder strap Over both shoulders, remStandard monitored parameter(sampling & reporting frequency; resolution)

Heart rate (250 & 1 Hz; 1Breathing rate (18 & 1 HzSkin temperature (1 & 13D accelerations (50 & 50Body orientation (50 & 1

Optional parameter Galvanic skin response;oxygen saturation; and, c

Wireless transmission BluetoothInternal memory Fixed, 229 MbyteViewing and analysis software Proprietary software dev

Unit of measurement: millimeter (mm); gram (g); Hertz (Hz); beat per minute (bpm); breath

229U.C. Gatti et al. / Automation in Construction 44 (2014) 227233technique [59] as done in many similar studies dealing with thevalidation of a physiological monitoring device [3843,6065].Fist, by considering previous validation studies indicating bound-aries for the Pearson productmoment correlation coefcient [41,42,55,66], the following boundaries were assumed:

r N 0.9 excellent correlation; 0.7 r 0.9 very large correlation; 0.5 r 0.7 moderate to good correlation; and, r b 0.5 minor correlation.

Then, differences between the PSM- and lab-measurements wereanalyzed by applying the BlandAltman technique. Thus, the mean dif-ference between the twomeasurementmethods (D), the standard devi-ation of the differences (s), the Limits of Agreement (LoA) equal to D1.96s, and the percentage of differences within the LoA (%DLoA) werecalculated.

Studies analyzing the validity of instrumentsmeasuring HR and/or BRare available in the medical literature. Some of these studies applied theBlandAltman technique to validate innovative clinical monitoring in-struments by suggestingmaximumacceptable LoA and considering an in-novative instrument valid if 95% of the differences were within the

Table 3Description of the activities.

Activity Duration Description1. Static 5 min The participant sits without moving.2. Thoracic rotation 5 min The participant rotates the torso either side

(average pace 20 movements per minute)keeping his/her forearms horizontal andraised at the device level (i.e., just belowthe sternum).

3. Arm lifting 5 min The participant stands and raises his/her armssimultaneously to vertical alongside his/herears position and lowers again(average pace 30 movements per minute).

4. Batting 5 min The participant repeats a combined movementof the arms and the twisting of the torso toeither side (i.e., like a baseball batting; averagepace 12 movements per minute).

5. Weight moving 10 min The participant repetitively moves a 5-kg weightfor a distance of 3 m. The weight is on the oor,thus the participant bends down to pick it up,walks 3 m, and sets it down.

6. Walking 10 min The participant walks on a treadmill at twodifferent walking paces: 5 min at 4.8 km/h(i.e., 3 mph), and 5 min at 6.4 km/h (i.e., 4 mph).maximum acceptable LoA. In accordance with medical and sport sciencestudies, the maximum acceptable LoA selected for this studywere 11 beats per minute for HR [40,41,67,68] and 3 breaths perminute for BR [40,41,60]. Therefore, the percentage of differences withinthese LoA (%Dmv) was also calculated.

Previous studies assessing the validity of portable instruments mea-suring HR and/or BR removed data sets when measurements were evi-dently affected by technical issues affecting the reference and/or testedinstrument [41,42,55]. Therefore, before performing the data analysis,the following data cleaning procedures were adopted for HR and BRdata. A HR data set was removed if the absolute mean of the differences(i.e., absolute value of D)was higher than 20 beats perminute, and a BRdata setwas removed if the absolutemean of the differenceswas higherthan 7 breaths per minute.

3. Results

All participants accomplished the testing procedure with no adverseevents. Nevertheless, one of the EQ-01 monitoring belts stopped work-ing preventing the collection of BR data for two participants. Table 4shows the maximum and minimum values measured by each device(i.e., PSMs and lab instruments), the number of data pairs, and thetotal testing time.

3.1. Heart rate

3.1.1. BioHarness BT 1Validity statistics for BH-BT (unit of measurement beat per min-

ute, bpm) are presented for every activity in Table 5. No datacleaningwas necessary for BH-BTdata. BH-BT obtained excellent corre-lation (r 0.94), small D and s (0.78D 0.22 bpm; s 4.05 bpm),and validity (%Dmv 98.4%) for activity 1, 2, and 3. Decreased precision

EQ-01

123 74 14 mm75 g

ovable Over left shoulder, xedbpm); 0.1 Bpm);Hz; 0.1 C);Hz;0.1 m/s2)Hz; 1)

Heart rate (256 & 0.07 Hz; 1 bpm)Breathing rate (25.6 & 0.07 Hz; 1 Bpm);Skin temperature (0.25 & 0.07 Hz; 0.1 C)3D accelerations (25 & 25 Hz;0.09 m/s2)Body orientation (25 & 1 Hz; 1)

ore body temperatureGalvanic skin response; oxygen saturation; and, core body temperatureBluetoothRemovable, micro SD card

eloped by Zephyr Proprietary software developed by Hidalgo

per minute (Bpm); Celsius scale (C); and, meter per second squared (m/s2).is seen for activity 4, 5, and 6with lower correlation, yet very large (0.74 r 0.78), larger D and s (4.81 D 1.68 bpm; 6.73 s 9.01 bpm), and %Dmv lower than 95% (84.8% %Dmv 90.3%).

3.1.2. Equivital EQ-01Validity statistics for EQ-01 are presented for every activity in

Table 6. When considering all the data (i.e., 10 participants), EQ-01 ob-tained excellent correlation (r 0.92) for activity 1 and 5; very largecorrelation (0.73 r 0.85) for activity 2, 3, and 6; and, moderate cor-relation (r = 0.52) for activity 4. In activity 1, relative small D and sare seen (D = 2.29 bpm; s = 3.49 bpm), and validity is achieved(%Dmv= 98.4%). In all the other activities, statistics presented reducedprecisionwith large D and s (D4.83 bpm; s 5.17 bpm), and %Dmvlower than 95%. After removing datawith clear technical error in activity3, 4, and 6 (i.e., a total of 3 data sets over 60 data sets were removed), re-sults improved with higher correlation (0.68 r 0.89), and smallerLoA though %Dmv remained lower than 95% (55.8% %Dmv 91.1%).

3.2. Breathing rate

3.2.1. BioHarness BT 1Validity statistics for BH-BT (unit of measurement breath per

to enhance construction workforce productivity, safety, and well-being. However, PSM validity in monitoring construction workforce'sphysiological parameters has yet to be determined. Validity refers tothe soundness or appropriateness of the test (i.e.,measuring instrument)in measuring what it is designed to measure. Validity may be deter-mined [] by a comparison to another test known to be valid [49]. Inthe present study, the validity of two PSMs was evaluated by analyzingthe agreement between PSM and gold standard laboratory instrumentmeasurements during static anddynamic tasks comparable to construc-tion workforce's routine movements. Two physiological parameterswere considered such as HR and BR.

4.1. Heart rate

BH-BTmeasurements were extremely accurate in activity 1, 2, and 3(i.e., static, thoracic rotation, and arm lifting) with excellent correlationand low LoA, and proved to be valid. For the other activities (i.e., batting,

Table 4Minimum and maximum values, number of data pairs, and total testing time.

Parameter Device Minmaxvalue

Datapairs

Total testingtime

HR BH-BT 59165 bpm 1504 6 h 16Lab paired with BH-BT 59162 bpmEQ-01 64150 bpm 1499 6 h 14 45Lab paired with EQ-01 47171 bpm

BR BH-BT 662 Bpm 1567 6 h 31 45Lab paired with BH-BT 665 BpmEQ-01 761 Bpm 1207 5 h 1 45Lab paired with EQ-01 791 Bpm

Unit of measurement: beat per minute (bpm); breath per minute (Bpm).

D

230 U.C. Gatti et al. / Automation in Construction 44 (2014) 227233minute, Bpm) are presented for every activity in Table 7. When consid-ering all the data (i.e., 10 participants), BH-BT showed excellent correla-tion (r 0.91) for activity 1 and 6; very large correlation (0.73 r 0.83) for activity 3 and 4; and, minor correlation (0.16 r 0.44)for activity 2 and 5. BR data produced low D and s (D =0.19 Bpm;s = 1.22 Bpm) and %Dmv was equal to 95% in activity 1. In all theother activities, statistics presented reduced precision with large LoAand %Dmv lower than 95%. After data cleaning (i.e., a total of 7 datasets over 60 data setswere removed), results improvedwith higher cor-relation (0.54 r 0.93) and lower D and s (2.42 D 0.08 bpm;3.01 D 4.83 Bpm) though %Dmv remained lower than 95%.

3.2.2. Equivital EQ-01Validity statistics for EQ-01 are presented for every activity in

Table 8. When considering all the data (i.e., 8 participants), BH-BTshowed excellent correlation (r 0.91) for activity 3 and 6; very largecorrelation (0.71 r 0.87) for activity 1, 2, and 5; and, moderate cor-relation (r = 0.55) for activity 4. EQ-01 obtained large D and s, and%Dmv lower than 95% in all the activities. After data cleaning for activity1 and 5 (i.e., a total of 2 data sets over 48 data sets were removed), re-sults improved with excellent correlation (r = 0.97) and lower D ands (D = 1.05 Bpm; s = 1.65 Bpm) though %Dmv remained lowerthan 95% (%Dmv= 88.3%) for activity 1; and, correlation slightly wors-ened (r = 0.81), and D and s slightly improved (D =3.05 Bpm; s =4.26 Bpm) for activity 5.

4. Discussion

By assessing work physiological demands, PSMs can play a crucialrole in supporting the development of tools, methods, and techniques

Table 5BH-BT heart rate validity statistics.

Activity Data pairs (data sets) r1 Static All 202 (10) 0.99 Clean

2 Thoracic rotation All 190 (10) 0.98 Clean

3 Arm lifting All 185 (10) 0.94 0Clean

4 Batting All 185 (10) 0.76 Clean

5 Weight moving All 368 (10) 0.78 Clean

6 Walking All 374 (10) 0.74 Clean

Unit of measurement: beat per minute (bpm).Tabular report of validity statistics: Pearson productmoment correlation (r), mean differeD 1.96s, percentage of differences within the LoA (%DLoA), and percentage of differences (weight moving, and walking), there was a general trend of decreasedaccuracy. Nevertheless, correlation remained very large, LoA relativelylow, and more than 84.8% of the differences within the maximum ac-ceptable LoA (i.e., 11 bpm). Thus, experiment outcomes suggestthat BH-BT is suitable for the monitoring HR during static and dynamicactivities. The obtained results are comparable to the results obtained inthree previous studies analyzing BH-BT performance in monitoring HRat rest and during dynamic activities, such as incremental treadmilland walkjogrun protocols [4042].

EQ-01 proved to be valid in activity 1 (i.e., static) with excellent andlow LoA. In activities 2, 5, and 6 (i.e., thoracic rotation, weight moving,and walking), measurements were relatively accurate with very largeto excellent correlation, relatively low LoA, and more than 82.8% of thedifferences within 11 bpm. Measurements in activity 3 and 4 (i.e.,arm lifting and weight moving) presented the worst performancewith very large to moderate correlation, large LoA, and less than 75%of thedifferenceswithin11 bpm. To remove the inuence of technicalissues, data cleaning procedures were applied to activity 3, 4, and 6 (i.e.,3 data sets over 60 data sets were removed). Erroneous data were notlinked to any specic subject. It can be hypothesized that the need toapply data cleaning procedures might be due to awed lab EKG patchesthat lost contact with the skin after extended usage. After data cleaning,measurement accuracy improved for all the activities but LoA remainedrelatively large and validitywas not attained. Thus, regardless of the useof data cleaning procedures, EQ-01 was not valid during dynamic activ-ities. Moreover, EQ-01 tended to underestimate HR since D was nega-tive for all the activity (Table 6). Thus, the results suggest that EQ-01is valid in monitoring subjects at rest but during dynamic activities itsperformance is likely to worsen and, potentially, lose signicance. Al-though there are differences in the applied data analysis procedures,

s (bpm) LoA (bpm) %DLoA %Dmv

D-1.96s D + 1.96s

0.78 2.21 5.1 3.6 95.0% 100.0%

0.77 2.39 5.4 3.9 92.1% 99.5%

.22 4.05 7.7 8.1 92.4% 98.4%

2.51 6.73 15.7 10.7 93.5% 90.3%

4.81 9.01 22.5 12.9 94.8% 84.8%

1.68 7.10 15.6 12.2 92.2% 87.7%

nce (D), standard deviation of the differences (s), Limits of Agreement (LoA) equal to

%Dmv) within 11 bpm.

Table 6EQ-01 heart rate validity statistics.

Activity Data pairs (data sets) r D s (bpm) LoA (bpm) %DLoA %Dmv

D-1.96s D + 1.96s

1 Static All 185 (10) 0.95 2.29 3.49 9.1 4.5 92.4% 98.4%Clean

2 Thoracic rotation All 183 (10) 0.85 4.90 5.17 15.0 5.2 91.8% 84.7%Clean

3 Arm lifting All 183 (10) 0.79 11.79 8.66 28.8 5.2 95.6% 50.8%Clean 163 (9) 0.86 10.35 7.14 24.3 3.6 96.3% 55.8%

4 Batting All 186 (10) 0.52 7.20 8.69 24.2 9.8 93.5% 74.2%Clean 167 (9) 0.68 5.49 7.09 19.4 8.4 95.8% 82.6%

5 Weight moving All 372 (10) 0.92 4.83 5.55 15.7 6.0 94.1% 86.8%Clean

6 Walking All 390 (10) 0.73 5.44 8.65 22.4 11.5 92.1% 82.8%Clean 350 (9) 0.89 3.51 5.44 14.2 7.2 95.7% 91.1%

Unit of measurement: beat per minute (bpm).Tabular report of validity statistics: Pearson productmoment correlation (r),mean difference (D), standarddeviation of thedifferences (s), Limits of Agreement (LoA) equal toD 1.96s,percentage of differences within the LoA (%DLoA), and percentage of differences (%Dmv) within 11 bpm.

231U.C. Gatti et al. / Automation in Construction 44 (2014) 227233EQ-01 results are comparable to a recent study validating an Equivitalmonitoring unit at rest and during walk at 3 km/h [38].

HR measurements indicate that PSMs perform best in monitoringparticipants at rest. This nding supports the assumption that partici-pant movements can affect PSM performance in monitoring HR. Similarresults were obtained in other studies analyzing HR monitor perfor-mance [41,42,64,65]. Visual analysis of the EKG vs. either BH-BT orEQ-01 data plot over time does not allow for detecting any systematicbehavior (e.g., PSM loses the heart's electric signal) capable ofexplaining performance worsening during dynamic activities.

4.2. Breathing rate

BH-BT results suggest that the device is valid at rest but its accuracycan decrease during dynamic activities. BH-BT was extremely accuratein monitoring BR at rest (i.e., activity 1) with excellent correlation andlow LoA. In all the other activities, overall performance decreased andmeasurements lost any signicance in activity 2 and 5 (i.e., thoracic rota-tion and weight moving). Data sets of a specic male participant wereconsistently removed when data cleaning procedures were applied. Inparticular, four data sets out of the seven removed data sets belonged tothis participant. It is likely that the PSM chest belt was not properly tight-ened and, therefore, moved during the experiment. In fact, this issue didnot occurwhen data cleaning procedureswere applied to his EQ-01mea-surements. After assessing cleaned data, measurement accuracy im-proved but the agreement remained weak for activities 5 and 6 (i.e.,weight movement and walking). Thus, regardless of the use of datacleaning procedures, BH-BT was not valid during dynamic activities. TheTable 7BH-BT breathing rate validity statistics.

Activity Data pairs (data sets) r D

1 Static All 200 (10) 0.98 Clean

2 Thoracic rotation All 195 (10) 0.44 Clean 155 (8) 0.84 0

3 Arm lifting All 194 (10) 0.73 Clean 153 (8) 0.91

4 Batting All 187 (10) 0.83 Clean

5 Weight moving All 397 (10) 0.16 Clean 316 (8) 0.54

6 Walking All 394 (10) 0.91 Clean 356 (9) 0.93

Unit of measurement: breath per minute (Bpm).Tabular report of validity statistics: Pearson productmoment correlation (r), mean differeD 1.96s, percentage of differences within the LoA (%DLoA), and percentage of differences (results are similar to the results reported in previous studies assessingBH-BT performance in monitoring BR at rest and during dynamic activi-ties, such as incremental treadmill and walkjogrun protocols [3942].

Although EQ-01 correlationwas either very large or excellent exceptfor activity 4 (i.e., batting), LoA were large for all the activities. Datacleaning procedures did not improved measurement accuracy anddiscarded data sets were not linked to any specic subject. Thus, the re-sults suggest that EQ-01 measurements are invalid at rest and duringdynamic activities. Results collected in the present study are compara-ble, yet worse, to the results described in a previous study analyzingan advanced version of the EQ-01 at rest andduringwalk at 3 km/h [38].

Collected data support the assumption that movements can affectPSMs'measurements inmonitoring BR. Faetti et al. [69] analyzed the per-formance of two BR measurement systems based on fabric strain gaugesand piezoelectric strain sensors during static and dynamic activitiesreaching results comparable to the ones obtained in this study. Further-more, PSM performance was better in monitoring HR than BR. This out-come was expected since speech, posture, and dynamic activities canaffect more BR monitoring devices than HR monitoring devices [69,70].In fact, regardless of the several BR monitoring devices developed foreld studies, Brookes et al. [60] noted that it is extremely problematic torealize a BR monitoring device that is safe, reliable, robust, and non-invasive.

4.3. Limitation

The main limitation of the present study is the number of partici-pants, which was limited to ten subjects. Although the sample size s (Bpm) LoA (Bpm) %DLoA %Dmv

D-1.96s D + 1.96s

0.19 1.22 2.58 2.20 93.5% 95.0%

1.56 8.83 18.87 15.75 90.8% 65.1%.08 3.46 6.69 6.86 91.6% 81.3%0.71 5.57 11.63 10.20 88.7% 72.2%1.16 3.01 7.06 4.73 95.4% 84.3%0.75 2.66 5.95 4.46 95.2% 85.6%

4.75 6.67 17.99 8.50 94.9% 49.4%2.42 4.08 10.42 5.57 94.9% 57.9%2.57 6.39 15.10 9.97 92.9% 64.0%1.92 4.83 11.39 7.55 93.0% 64.9%

nce (D), standard deviation of the differences (s), Limits of Agreement (LoA) equal to%Dmv) within 3 Bpm.

physiological parameters during dynamic activities resembling con-struction workforce's routine activities. First, general results across ac-

D

Clean 128 (7) 0.97

ferees (

232 U.C. Gatti et al. / Automation in Construction 44 (2014) 227233tivities suggest that BH-BT is a suitable tool to assess HR at rest andduring dynamic activities. EQ-01 collected valid HR data at rest but dur-ing dynamic activities data quality worsened. Thus, EQ-01 cannot beconsidered a suitable instrument for construction workforce HR moni-toring. Then, both PSMswere provednot capable of effectively assessingBR effectively except for BH-BT at rest.

In conclusion, the data suggest that, with prior understanding ofstudy limitations, BH-BT is a viable device in assessing HR and, there-fore, can be implemented to measure work physiological demands onconstruction sites in an unobtrusive and remote manner. Therefore,the use of PSMs, coupled with work physiology and ergonomics con-and/or the amount of the collected data were either comparable to orlarger than other similar research studies [3842,61,6265,71], suchlimited sample size may not be sufcient to assess true differences be-tween device measurements. Furthermore, the present study did notconsider the reliability of instrument performance on repeated mea-sures. Although repeatedmeasurementswith the same subject are rare-ly performed in human subject testing [40], they can provide importantinsight on the instrument reliability and precision [72]. Thus, further ex-amination of PSMs reliability should be considered for future studiesthat continue assessing the maturity of this technology.

5. Conclusions

This study has analyzed the validity of two PSMs in monitoring two

2 Thoracic rotation All 148 (8) 0.87Clean

3 Arm lifting All 150 (8) 0.92Clean

4 Batting All 148 (8) 0.55Clean

5 Weight moving All 303 (8) 0.82Clean 264 (7) 0.81

6 Walking All 311 (8) 0.91Clean

Unit of measurement: breath per minute (Bpm).Tabular report of validity statistics: Pearson productmoment correlation (r), mean difD 1.96s, percentage of differences within the LoA (%DLoA), and percentage of differencTable 8EQ-01 breathing rate validity statistics.

Activity Data pairs (data sets) r

1 Static All 147 (8) 0.71cepts, could foster the creation of innovative workforce managementprocedures allowing enhancements not only in productivity, but alsoinworkers'well-being and safety. In fact, PSMswere successfully imple-mented in analyzing the relationship between physical strain and tasklevel productivity [73], and performing automatic work sampling to fa-cilitate real-time productivity assessment [3] and monitoring of ergo-nomically safe and unsafe behavior of construction workers [22].Moreover, considering that the dynamic activities employed in the ex-periments can resemble many work activities (e.g., material handling),this paper can be a valuable reference for industries other thanconstruction.

Acknowledgment

The authors would like to thank the Exercise Physiology Lab and theMulti-Agent, Robotics, Hybrid, and Embedded Systems (MARHES) Labat the University of New Mexico for granting access to the necessarylab equipment as well as the lab assistant Jeremy Clayton Fransen.References

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Physiological condition monitoring of construction workers1. Introduction2. Materials and methods2.1. Description of PSMs and their key features2.2. Participants2.3. Experimental design2.4. Signal processing and data analysis

3. Results3.1. Heart rate3.1.1. BioHarness BT 13.1.2. Equivital EQ-01

3.2. Breathing rate3.2.1. BioHarness BT 13.2.2. Equivital EQ-01

4. Discussion4.1. Heart rate4.2. Breathing rate4.3. Limitation

5. ConclusionsAcknowledgmentReferences