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Feasibility analysis of electrodermal activity (EDA) acquired from wearable sensors to assess construction workers’ perceived risk Byungjoo Choia, Houtan Jebellib, SangHyun Leec,
Department of Architectural Engineering, Ajou University, 206, World cup-ro, Suwon-si, Gyeonggi-do 16499, South Korea Tishman Construction Management Program, Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward St., Suite 1148 G.G. Brown Building, Ann Arbor, MI 48109, United States c Tishman Construction Management Program, Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward St., Suite 2340 G.G. Brown Building, Ann Arbor, MI 48109, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Construction safety Perceived risk Wearable sensor Electrodermal activity Physiological monitoring
Risk perception is known as a critical step in workers’ safety decision-making process. However, current approaches to assess workers’ perceived risk include surveys and interviews which are post hoc, subjective, and cumbersome to implement at construction sites. To address the issues associated with these methods, the authors propose a novel approach for the continuous and quantitative assessment of workers’ perceived risk using physiological responses acquired from wearable sensors. With this background, this study aims to investigate the potential of using physiological sensory data (e.g., electrodermal activity (EDA)) collected from oﬀ-the-shelf wristband typed sensors to understand construction workers’ perceived risk during their ongoing work. To achieve this objective, 30 h of physiological sensory data were collected from eight construction workers during their ongoing work. The results indicate that: (1) electrodermal response (EDR), which refers to short-term changes in EDA, shows signiﬁcant diﬀerences between low and high-risk activities; (2) high-risk activities signiﬁcantly aﬀect workers’ EDR during their ongoing work. The main contribution of this study is to show the feasibility of using wearable sensors to understand workers’ perceived risk in construction sites continuously. Considering the complexity and dynamicity of workers’ tasks on construction sites, the development of an objective, continuous, and non-intrusive method for monitoring workers’ physiological responses is expected to contribute to a more in-depth understanding of construction workers’ perceived risk.
1. Introduction Despite continuing eﬀorts to improve construction safety, the construction industry still remains one of the most dangerous industries in the world (Choi and Lee, 2017b). For example, the U.S. construction industry reported 911 fatal occupational injuries in 2016, with a fatal injury rate 2.8 times higher than the national average (U.S. BLS, 2017a). While the construction industry comprised only 5.1% of the U.S. workforce (U.S. BLS, 2017b), it accounted for more than 19% of total fatal occupational injuries in 2016 (U.S. BLS, 2017a). That same year, the construction industry reported more than 197,000 nonfatal occupational injuries, with an injury rate four times higher than national average nonfatal occupational injury rate (U.S. BLS, 2017c). Previous studies estimated that the annual cost associated with construction accidents exceeded $11 billion in 2002, 15% of the costs for all private construction sector (Waehrer et al., 2007). Furthermore, construction safety has been a signiﬁcant concern not only in the U.S., ⁎
but also throughout Europe (Meliá et al., 2008; Törner and Pousette, 2009), Asia (Fang et al., 2016; Jiang et al., 2010), and Australia (John and Anthony, 2001; Zou Patrick and Zhang, 2009). Previous accident investigations have noted that the vast majority of accidents in construction sites are associated with workers’ unsafe behaviors (Choi et al., 2017; Haslam et al., 2005; Hinze, 2006; Seo, 2005; Suraji et al., 2001). After studying more than 75,000 accident cases, Heinrich et al. (1950) reported that 88% of accidents were attributed to human errors. After examining 277 accident reports between 1985 and 1989, Salminen and Tallberg (1996) found that 84–94% of construction accidents were caused by unsafe actions in construction activities. According to established research, construction workers’ choice of unsafe behaviors primarily originates from their inability to adequately perceive and respond to risk (Carter and Smith Simon, 2006; Chen et al., 2016). Workers tend to take risks and perform unsafe behaviors if the perceived risks are insigniﬁcant compared with their internal threshold (i.e., risk acceptance) (Bohm and Harris, 2010; Choi and Lee, 2017a;
Corresponding author. E-mail addresses: [email protected]
(B. Choi), [email protected]
(H. Jebelli), [email protected]
https://doi.org/10.1016/j.ssci.2019.01.022 Received 14 June 2018; Received in revised form 25 December 2018; Accepted 24 January 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.
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2013). In this sense, previous studies showed a signiﬁcant relationship between emotions (e.g., fear, anxiety, emotional stress, etc.) and risktaking behaviors in various contexts such as safety behaviors (Leung et al., 2012; Tixier et al., 2014), driving behaviors (Schmidt-Daﬀy, 2013), and evacuation (Iwahashi et al., 2014; Zou et al., 2017). Considering that construction workers’ risk perception is an immediate response to potential hazards during their work, risk as feeling would be more related to risk perception in the safety decision-making process.
Wilde, 1982). Therefore, understanding how construction workers perceive the risks to which they are exposed is critical to safety management in construction sites (Arezes and Miguel, 2008; Tixier et al., 2014). Construction practitioners and researchers have mainly relied on questionnaires to assess workers’ perceived risk in the workplace (Hallowell, 2010; Jiang et al., 2014; Tixier et al., 2014). However, such an approach may not be enough to understand workers’ perceived risk because post hoc surveys are not capable of showing changes in perceived risk over time during ongoing work. Also, asking workers to participate in surveys during work hours could be cumbersome and interfere with their ongoing tasks. Finally, the survey may not be free from the self-reported bias which is inherent in the subjective scale. Therefore, there is an increased need for objective and nonintrusive methods that can continuously assess workers’ perceived risk. To ﬁll the gap, the authors propose a novel approach for continuous and quantitative assessment of workers’ perceived risk using physiological responses (e.g., heart rate, electrodermal activity, skin temperature) acquired from wearable sensors (e.g., wristband). With this background, this study aims to investigate the feasibility of the physiological response to assess construction workers’ perceived risk during their ongoing tasks.
2.2. Electrodermal activity (EDA) and risk as feeling Despite the increasing interest in risk as feeling, it is very challenging to develop an objective, nonintrusive, and continuous measurement of individuals’ perceived risk as feeling. Individuals’ physiological responses related to the sympathetic nervous system such as heart rate (HR), electrodermal activity (EDA), and skin temperature (ST) possess a great potential to assess risk as feeling. When people perceive signiﬁcant risk, the sympathetic system of the autonomic nervous system becomes aroused, which results in substantial changes in physiological responses. As such, these physiological responses could be indicative of individuals’ perceived risk. Speciﬁcally, EDA is a useful index of an individual’s perceived risk. EDA refers to autonomic changes in electronic properties of the skin in response to sweat secretion (Benedek and Kaernbach 2010). EDA could be a useful indicative of activities of the sympathetic branch of the autonomic nervous system because the sweat glands are innervated by the sympathetic nervous activities (Poh et al., 2010; Kappeler-Setz et al., 2013). The sympathetic arousal stimulated by external stressors could be reﬂected by a higher EDA. In this sense, EDA has been used to understand an individual’s mental status related to sympathetic arousal (e.g., stress, attention, risk perception, etc.) in various situations such as occupational setting, human-computer interaction, traﬃc and automation, and marketing and product evaluation (Boucsein 2012). Speciﬁcally, EDA has been widely used to understand the perceived risk in safety research because the perceived risk stimulates activities in the sympathetic branch of the autonomic nervous system (Schmidt-Daﬀy 2012; Kinnear et al. 2013; Herrero-Fernández et al., 2016). As aforementioned, risk as feeling is the results of the feedback from the autonomic nervous system (Epstein 1994). Therefore, if the autonomous nervous system is aroused by the perceived risk (Zou et al. 2017), the sympathetic branch of the autonomous nervous system innervates the sweat gland activities that result in changes in electronic properties of the skin (i.e., EDA). In addition, EDA could be a more useful index of the perceived risk than other physiological signals such as heart rate, respiration rate, and skin temperature because EDA is the only autonomic physiological variable that is not contaminated by the parasympathetic branch of the autonomic nervous system (Braithwaite et al., 2013; Picard et al., 2016). The time series of EDA can be categorized into two components: tonic (i.e., electrodermal level - EDL) and phasic components (i.e., electrodermal response - EDR) that have diﬀerent time scales and relationships to external stimuli (Boucsein, 2012; Cacioppo et al., 2007). The EDL denotes slow drifts of the baseline EDA and spontaneous ﬂuctuation in EDA (Greco et al., 2016). The EDL is thought to reﬂect general changes in sympathetic arousal and can vary substantially across individuals (Braithwaite et al., 2013; Kappeler-Setz et al., 2013). The EDR refers to rapid changing element of EDA and reﬂects shorttime responses to external stimuli (Benedek and Kaernbach, 2010; Greco et al., 2016). Typically, EDR shows “a steep incline to the peak and a slow decline to the baseline” patterns (Benedek and Kaernbach, 2010). Both components are important and rely on diﬀerent neural mechanisms (Dawson et al., 2007; Nagai et al., 2004). For example, the EDL has been regarded as a suitable measure of sympathetic activity induced by long-term stress (Poh et al., 2010). On the other hand, EDRs are evoked by attention-grabbing stimuli (e.g., hazard during the work)
2. Background 2.1. Risk perception Considering that workers’ safety behaviors are responses to potential risk during their work, risk perception is a critical step in a worker’s safety decision-making process. As such, risk perception has been included in many theoretical models of risk-taking behaviors. Endsley (1995) described how people process hazardous situations using three major steps including “detection of hazardous signals, perception and comprehension of risks, and projection of the consequences associated with decision options.” Deery (1999) explained risk-taking behaviors in a dangerous environment as four major steps including “hazard detection, risk perception/acceptance, self-assessment, and behavior.” Shin et al. (2014) suggested a mental process model of construction workers’ safety behaviors consisting of ﬁve phases: risk perception, attitude, intention, behavior, and outcome. According to the risk homeostasis theory (Wilde, 1982, 1988), perceived risk and acceptable risk are two main determinants of an individual’s risk-taking behaviors. An individual takes the risk (i.e., unsafe behaviors) when the perceived risk is lower than his/her acceptable risk. Also, a number of previous studies have empirically supported the relationship between risk perception and risk-taking behaviors (Hunter, 2006; Lam, 2003; Mills et al., 2008; Wang et al., 2016). Traditionally, risk perception has been understood as a process of analysis. In this approach, perceived risk has been deﬁned as an individual’s subjective assessment of the severity and likelihood of an accident that the risk can induce (Baradan and Usmen, 2006; Choe and Leite, 2016; Esmaeili and Hallowell, 2013; Hallowell, 2010). Based on this approach, researchers have conducted surveys and interviews to measure construction workers’ perceived risk in construction projects. On the other hand, researchers have recently understood risk as feeling (Peters et al., 2006). Risk as feeling refers to “individuals’ instinctive and intuitive reactions to danger” (Slovic and Peters, 2006). These affective reactions to external stimuli are the results of feedback from the autonomic and somatic nervous system (Epstein, 1994) which is separate from the analytic processing system (Kahneman and Frederick, 2002). The aﬀective response has been used as a predominant method by which individuals evaluate risk (Slovic et al., 2004; Slovic and Peters, 2006). In this sense, risk as analysis is more related to a logical, deliberate, and slow risk decision-making process (e.g., ﬁnancial risk analysis) whereas risk as feeling is closer to autonomic and immediate responses that involve little or no conscious attention (Kinnear et al., 111
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on respondents’ ability to recall their experiences during the work (Guo et al., 2017; Kocielnik et al., 2013). However, an individual's memory does not reﬂect his/her experience equally and is not immune from biases of subjective judgment (Choe and Leite, 2016; Wang et al., 2017). Therefore, it is necessary to develop an objective and noninvasive method for continuous assessment of workers’ perceived risk. To ﬁll the gaps, this study proposes to use EDA data collected from wearable sensors to understand workers' perceived risk during their ongoing work. Since the sympathetic nervous system aroused by perceived risk results in substantial changes in EDA, EDA could be used for assessing perceived risk. Also, the recent advancement in wearable technologies allows us to overcome the hardware limitations of traditional EDA sensors and continuously collect construction workers EDA data without interrupting their ﬁeld jobs. Despite this potential, the feasibility of using EDA acquired from wearable sensors to understand construction workers’ perceived risk has not been well studied. With this background, this paper investigates the feasibility of using EDA that is collected from wearable sensors to understand construction workers’ perceived risk during their ongoing work. Speciﬁcally, this study examines the distinguishing power of EDA in detecting workers’ perceived risk during their ongoing work.
and attention-demanding task (Poh et al., 2010; VaezMousavi et al., 2007). 2.3. Wearable technologies Notwithstanding EDA’s potential to understand risk perception as feeling, it has been mostly applied and tested in controlled experimental settings. As such, there has been a notable lack of research applying EDA to assess risk perception in the ﬁeld including construction sites. Hardware limitations of traditional EDA sensors (e.g., wired connection and electrodes attached to palm or ﬁnger) make it very challenging to continuously measure construction workers’ EDA without interrupting their ﬁeld jobs. Also, physiological signals acquired from construction sites can be easily contaminated by diverse sources of noises such as extreme work conditions and workers' excessive body movements (Gatti et al., 2013; Hwang et al., 2016; Jebelli et al. 2018a, 2018b, 2018c). Recent advancements in wearable technologies allow us to overcome the limitations of traditional EDA sensors and open a new door toward assessing risk as feeling at construction sites. Wearable sensors (e.g., oﬀ-the-shelf wristband-type wearable sensor) can continuously collect workers’ physiological signals with minimal interruption of their ongoing work (Choi et al., 2018; Jebelli et al., 2017a). Since the physiological signals (e.g., HR, EDA, ST, etc.) are the results of individuals' physical and mental responses to the environment, physiological sensory data collected from wearable sensors can be used to understand workers’ physical and mental status. In the same vein, a few studies have attempted to use wearable sensors to understand construction workers' physical and mental status such as workload, (Gatti et al., 2014a, 2014b; Hwang and Lee, 2017; Lee et al., 2017; Lee and Migliaccio, 2016), physical fatigue (Aryal et al., 2017), emotion (Hwang et al. 2018), and stress (Jebelli et al., 2018a,b,c,d,e). Despite the potential of wearable sensors and EDA, there is a notable paucity of research to use EDA collected from wearable sensors to understand construction workers' perceived risk during their ongoing work.
4. Methods 4.1. Subject A ﬁeld data collection was designed to collect construction workers’ EDA during their ongoing tasks. The data collection protocol was approved by the institutional review board (IRB) at the University of Michigan. In this study, EDA data were collected from seven on-site construction workers (two carpenters, one ﬂoorer, and four electricians) and one on-site foreman (electrician foremen). Each worker participated in the data collection during half of their working day either morning (ﬁve workers) or afternoon (three workers). The subjects were working at a hospital retroﬁt project located in Gary, Indiana. The data were collected from February 21, 2017 to February 23, 2017. All participants were male, age between 20 and 50 years (M = 32.37, SD = 8.57), and had three to twenty-eight years of work experience (M = 10.25, SD = 6.72) in the construction industry. All subjects reported no clinical conditions that could aﬀect their physical and mental ability to execute their daily tasks. Table 1 describes the demographic information of subjects and duration of the collected data.
3. Knowledge gaps and research objective As aforementioned, previous studies mainly relied on survey or interviews to measure an individual’s perceived risk based on the concept of risk as analysis. Although these survey-based measures have greatly contributed to extending our understanding of workers’ perceived risk, they have several notable limitations. First, these post hoc methods are limited to understanding changes in perceived risk over time during workers’ ongoing work. Unlike other industries that have stable work conditions, work conditions in construction sites show complex and dynamic changes (e.g., temporal structures, weather, and equipment of use) (Fredericks et al., 2005). As such, continuous measurement of perceived risk is particularly meaningful in construction sites. In addition, since the survey-based method requires workers’ time and eﬀort to answer the questions with care and precision, it can be cumbersome and interfere with workers’ ongoing work (Aryal et al., 2017; Hwang et al., 2018). Furthermore, the reliability of questionnaires mainly relies
4.2. Data collection procedure Before the data collection, all subjects were provided and signed an informed consent form that explains the conﬁdentiality of collected data and participants’ rights. Once the consent was received, all subjects were asked to provide their demographic information including age, gender, body-size (i.e., height, weight, and waist size), trade, and work experience. In order to exclude unhealthy subjects, potential participants were also asked whether they have any experience of health problems (e.g., cardiovascular disease): no subjects reported any
Table 1 Description of subject information and collected data. Subject Index
Working Experience (years)
Data Amount (h)
S1 S2 S3 S4 S5 S6 S7 S8
33 20 50 31 38 27 35 25
188 173 186 183 189 183 183 167
93 77 98 88 81 99 109 66
10 3 25 11 12 5 13 3
Electrician Floorer Carpenter Electrician foremen Carpenter Electrician Electrician Electrician
3.82 3.78 2.59 2.61 4.68 4.68 4.56 2.22
Morning Morning Afternoon Afternoon Morning Morning Morning Afternoon
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Subjects Wearing Wristband
Wristband with EDA Sensor
Fig. 1. EDA data collection.
Physiological signals collected in construction sites may include an even larger number of artifacts because of extreme work conditions at construction sites and workers’ excessive body movements (Jebelli et al. 2018a, 2018b, 2018c). An EDA sensor consists of two electrodes and measures EDA by passing a minuscule amount of current between the two electrodes in contact with the skin (Boucsein, 2012). Therefore, measurement of EDA could be vulnerable to several types of noises such as electrodes popping, excessive movement, and adjustment of sensors (Taylor et al., 2015). To remove artifacts recorded in the signal, a second-order high-pass ﬁlter (Hamming window, cut-oﬀ frequency fc = 0.05 Hz) was applied to smooth the EDA signals (Braithwaite et al., 2013; Kappeler-Setz et al. 2013; Jebelli et al. 2018a). While a low-pass ﬁlter is widely used to remove the most common artifacts (i.e., environmental artifacts, sensor motion artifacts, muscle movement artifacts) recorded in physiological signals, it is limited to remove large magnitude artifacts related to excessive pressure on electrodes and excessive body movements (Taylor et al., 2015). To eliminate this type of artifacts, a rolling ﬁlter of four data points (i.e., the number of data points per second) per block was applied to smooth the EDA signals further. Rolling ﬁlter showed a high performance to eliminate most common signal artifacts from electrodermal signals (Jovanovic et al., 2009; Kocielnik et al. 2013; Fitzpatrick and Kuo 2016; Jebelli et al. 2018a). The EDA signals before and after applying the ﬁlters are represented in Fig. 2 for one subject (S1) as an example.
history of these problems. As shown in Fig. 1, all subjects were asked to wear an oﬀ-the-shelf wristband typed sensor (E4 wristband manufactured by Empatica Inc, Cambridge, MA, U.S.) to collect their EDA. Before wearing the sensor, each subject’s skin in the sensor area was cleaned to eliminate any dirt that could potentially obstruct the sensor’s electrode. Then, the research team checked if the sensor was properly located and asked subjects if all sensors were ﬁtted properly. The wristband sensor records subjects’ EDA with a sampling rate of 4 Hz. The wristband sensor is programmed to store the data into its internal storage and to upload the data to the online server in the real time. With EDA data, the sensor used in this study also provides information on heart rate which can be used to infer subjects’ physical demands. Subjects were asked to perform their daily tasks in their usual work area during half of their working hours. Except for the time for preparing data collection, data collection duration for each subject range from 2.22 to 4.68 h (Table 1). In addition, all subjects’ activities were recorded using a hand-held video camera, allowing for further analysis regarding the relationship between EDA and perceived risk.
5. Data processing 5.1. Artifacts removal Physiological signals, even in an experimental setting, include a large number of artifacts. An artifact is deﬁned as “any undesired variation in the measured signal due to sources external to the parameter of interest” (Sweeney et al., 2012). If these artifacts remain in the signal, the signal can be easily misinterpreted and skew the analysis (Hwang et al., 2018; Jebelli et al., 2017b; Sweeney et al., 2012).
5.2. EDA decomposition As aforementioned, EDA can be decomposed into a tonic, slow changing component (i.e., EDL) and a phasic, rapid changing
Fig. 2. Example of artifacts removal (S1). 113
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Fig. 3. Example of cvxEDA decomposition (S4).
which are easier to diﬀerentiate. If EDA acquired from wearable sensors can distinguish low and moderately high-risk activities, it would have a better distinguishing power between low and more dangerous activities. Also, the collected EDA data were downsampled to decrease the frequency from the original 4 Hz to 1 Hz which is the frequency of video labeling. The downsampling enables us to prevent overestimation of the signiﬁcance in the statistical analysis. If the video labeled data were unsampled from 1 Hz to 4 Hz, the sample size of the statistical analyses will become four times larger than the original data which will result in the overestimation of the signiﬁcance.
component (i.e., EDR). The tonic component includes slow drift of the baseline skin conductance, and the phasic component reﬂects shortterm responses to external stimuli. A convex-optimization-based EDA model (cvxEDA) developed by Greco et al. (2016) is applied to decompose EDA in this study. The cvxEDA describes EDA as the sum of the tonic component (i.e., EDL), phasic component (i.e., EDR), and additive white Gaussian noise term. CvxEDA represents the EDR as the output of the convolution between a sparse nonnegative SMNA (sudomotor nerve activity) phasic driver and an impulse response function (IRF) shaped like a biexponential Bateman function (Greco et al., 2016, 2017). CvxEDA models the IRF as an inﬁnite impulse response (IIR) function using autoregressive moving average (ARMA) model. Before conducting decomposition, each subject’s EDA is standardized using zscore to oﬀset individual variations of the baseline of EDA (Braithwaite et al., 2013) as Greco et al. (2016) recommended. Fig. 3 illustrates an example of the decomposition of EDA using cvxEDA for one subject (S4).
6. Results Long-term assessment of EDA revealed patterns in the subjects’ sympathetic modulation over the data collection. Fig. 5 describes variations in EDA for all participants of the morning session. EDA increases as the data collection progresses at the beginning of the data collection. After that, EDA shows various patterns based on the workers’ activities. However, there are notable basins between 09:00 and 09:30 for all the subjects except for S6. This is probably due to the rest time during the morning session. All the participants took a rest at that time, which decreases the general sympathetic arousal and subsequently results in the decrease of EDA. In the case of S6, EDA starts to decrease from 08:30. This is probably because S6 went to the site oﬃce and discussed the drawings with a project engineer at that time. In other words, S6 experienced neither excessive physical activities nor signiﬁcant risk from 08:30 to 09:00. Fig. 6 shows changes in EDA for all participants of the afternoon session. Similar to the patterns in the morning session, increases in EDA are found at the beginning of the data collection. After the rise, the signal shows variations based on each subject's activities. S3 and S4 show gradual decreases in the signal from around 13:45 and S8 shows an additional peak around 14:15. This could be explained their activity patterns. S3 and S4 completed their primary task of the day around 13:45 and performed some activities for preparing the next day (e.g., clean the site and paperwork). On the other hand, S8 continuously performed his task until 14:30, at which time he wrapped up his immediate work while continuing to prepare for the next day. Table 2 represents the descriptive statistics of each subject’s EDR and EDL in low and high-risk activities. As shown in Table 2, the mean of each subject’s EDR range from 0.047 to 0.877 in low-risk activities and 0.197 to 0.889 in high-risk activities. While ﬁve subjects (i.e., S2, S4, S5, S6, and S7) show signiﬁcant mean diﬀerences in EDR between low and high-risk activities, other three subjects (i.e., S1, S3, and S8) show insigniﬁcant mean diﬀerences. Also, the mean of all the subjects’ EDR is 0.401 in low-risk activities and 0.581 in high-risk activities. Because of the small sample size, which makes it diﬃcult to satisfy the assumptions required for the parametric test (e.g., normality
5.3. Data labeling and downsampling The objective of this study is to examine the feasibility of EDA collected from wearable sensors to distinguish construction workers’ high-risk perception and low-risk perception during their ongoing work. To do this, all subjects’ activities during the data collection were categorized into high-risk activities and low-risk activities using the recorded video. The labeled data allow us to compare subjects’ EDL and EDR between high and low-risk activities and examine the distinguishing power of the collected data in detecting high-risk activities. The categorization was completed by two research team members who have ﬁeld experience in construction sites and are familiar with potential risk in construction projects. The two individuals separately labeled the subjects’ activities every second using their experience and expertise (i.e., frequency = 1 Hz). If the labeling results were not consistent between the two observers, the activities were excluded in the analysis. The common high-risk activity for electricians was working on the ladder or ceiling to ﬁnish electrical (e.g., installing light ﬁxtures, electrical equipment, pulling the wiring, etc.) (a in Fig. 4). The carpenters were also working on the ladder to place the plasterboard to the ceiling. They also used dangerous tools (e.g., plasterboard saw and cutter) to cut plasterboards and plastic boards. Floor ﬁnish worker also involved high-risk activities such as chipping the concrete slab to install drains on the ﬂoor. While there were variations in the type of high-risk activities between diﬀerent trades, low-risk activities were similar to each other such as working on the ground, walking around, talking with coworkers, and resting (b in Fig. 4). This study focused on moderately high-risk activities (e.g., working on the ladder or ceiling) rather than highly hazardous activities such as working on a beam or scaﬀolding 114
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(a) High Risk Activities
(b) Low Risk Activities
Fig. 4. Examples of activity labeling.
diﬀerences in EDR between low and high-risk activities across the subjects as shown in Fig. 8. The term HLM is used interchangeably with Multi-Level Model (MLM) and is used to analyze data with a hierarchical structure (Snijders, 2011). HLM enables us to investigate the eﬀects of group-level characteristics on the dependent variable. Since this study collected data multiple times from each subject, the data can be grouped by each subject. Therefore, each data point can be deﬁned as level 1, and each subject can be deﬁned as level 2 (i.e., hierarchical structure). For these reasons, HLM with the two levels (i.e., Level 1 – each data, Level 2 – each subject) is conducted using the SPSS Mixed Procedure (Heck et al., 2013) in order to examine the eﬀect of high-risk activities on EDR. In the model, high-risk activity is included using dummy variable (i.e., 0: low-risk activity, 1: high-risk activity). Also, % Heart Rate Reserve (%HRR), which is an index of individuals’ physical demands (Hwang and Lee, 2017), is included as a control variable because EDA could be aﬀected by physical activities (Poh et al., 2010). EDL is also included as a control variable because EDR and EDL are estimated from the same source (i.e., EDA). The results of HLM are represented in Table 3. First, the unconditional model that could be described by the following equation: EDRij = β0 + uj + eij is used for testing the eﬀect of the subject on EDR. In this equation, uj represents Level 2 (i.e., subject) residuals that refer to the diﬀerences between subject j’s mean and the overall mean. Level 1 (i.e., data) residual eij represents the diﬀerences between the value of data i and the mean of subject j. The variance partition coeﬃcient (VPC) is 0.048/(0.118 + 0.048) = 0.41, which indicates that 41% of the variance in EDR can be attributed by diﬀerences between subjects. Then, a random intercept model (Model 1) which could be described by the following equation: EDRij = β0 + β1High_Riskij + uj + eij is tested. In the model, the overall relationship between EDR and high-risk activities is represented by a linear line with intercept β0 and slope β1. The intercept for a given subject j is β0 + uj, but the slope of the line is assumed to be ﬁxed across subjects. In other words, the eﬀect of high-risk
assumption), the Wilcoxon singled-rank test is used to examine the diﬀerences. The result indicates that EDR in high-risk activities is signiﬁcantly higher than in low-risk activities (z = 2.100, p = .039). On the other hand, the mean of each subject’s EDL range from −0.782 to 0.027 in low-risk activities and from −0.971 to 0.152 in high-risk activities. While four subjects (i.e., S4, S6, S7, and S8) show positive signiﬁcant mean diﬀerences between low and high-risk activities, two subjects (i.e., S3 and S5) show negative signiﬁcant mean diﬀerences. The other two subjects (i.e., S1 and S2) do not show signiﬁcant differences in EDL between low and high-risk activities. The result of the Wilcoxon single-ranked test indicates that there is no signiﬁcant difference in EDL between high and low-risk activities (z = 1.12, p = .312). Furthermore, Fig. 7 represents the distribution of all subjects’ EDR and EDL in low and high-risk activities. The graphs show consistent results from the results of the Wilcoxon single-ranked test on the diﬀerences in EDL and EDR between low and high-risk activities. To examine the eﬀects of high-risk activities on workers’ EDR during their ongoing work, the mean of EDR between low and high-risk activities was compared using an Analysis of Variance (ANOVA). Since the duration of the data collection is not the same across subjects (Table 2), the number of data for each subject is not equal in this study. This disproportionate number of data can lead to a misinterpretation of the results because the impacts of each subject are determined by the number of data that the subject has. In other words, a subject with a large number of data has a greater impact on the results and vice versa. To prevent this problem, the equal number of data for each subject (i.e., one-minute data for high-risk activity and one-minute data for low-risk activity) were randomly sampled before conducting the analysis. The results of ANOVA show that there was a signiﬁcant eﬀect of high-risk activity on EDR [F(1, 658) = 6.644, p = 0.01]. Besides, a Hierarchical Linear Modeling (HLM) was performed to consider individual diﬀerences in EDR. As shown in Table 2, the mean of each subject’s EDR is unique. Also, there are variations in the
Fig. 5. Changes in each subject's standardized EDA (morning session). 115
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Fig. 6. Changes in each subject's standardized EDA (afternoon session).
could be a consequence of the coincidence in the sampling process. In order to validate the results of HLM, the authors repeated the resampling process and HLM analysis using Monte Carlo methods. The number of repetition is based on the statistical power analysis using G*power 3 (Faul et al., 2007). A prior power analysis based on obtaining the desired power of 0.95 in mean diﬀerences analysis with one group sample, with an alpha 0.5 and medium eﬀect size of 0.50 indicates that reasonable sample size is 54. An independent samples t-test was performed to examine the signiﬁcance of the mean of the regression coeﬃcient (i.e., β1 in Model 3 in Table 3) and its p-value. The results indicate that the mean of regression coeﬃcient (M = 0.166, SD = 0.021 is signiﬁcantly greater than 0.0, t(53) = 58.198, p ≈ .000 and the mean of p-value (M = 0.029, SD = 0.018) is also signiﬁcantly lower than 0.05., t(53) = −8.433, p ≈ .000. The results of the t-tests conﬁrm the validity of HLM analysis.
activities on EDR is assumed to be the same for all subjects. As shown in Table 3, high-risk activity is a signiﬁcant predictor of EDR (β1 = 0.205, t = 9.664, p ≈ .000). The result implies that the eﬀect of high-risk activity is to increase the predicted EDR by 0.205. The VPC in Model 1 is 0.048/(0.108 + 0.048) = 0.31 that implies that after accounting highrisk activity, 31% of the unexplained variance in EDR is due to diﬀerences between subjects. After that, a random slope model (Model 2) that allows the slope to vary across subjects is tested. The random slope model could be represented by the following equation: EDRij = β0 + β1High_Riskij + uoj + u1jHigh_Riskij + eij. In this model, the slope of the average regression line is β1, and the slope of the subject j is β1 + u1j. The result shows that eﬀects of high-risk activity on EDR is statistically signiﬁcant (β1 = 0.205, t = 3.710, p = .006). The likelihood ratio test statistics (i.e., LR = −2logL1 − (−2logL2)) comparing Model 1 and Model 2 is 619.820 − 587.305 = 32.515 which is compared to a chi-squared distribution on two degrees of freedom. The pvalue for the test is < .001 which implies that the eﬀect of high-risk activity on EDR varies across subjects. As the last step, the ﬁnal model that includes all the control variables (i.e., %HRR and EDL) is tested. Since %HRR and EDL are also involved with a hierarchical data structure, the model also includes level 2 residuals of them (i.e., u2j for %HRR and u3j for EDL). As shown in Model 3 in Table 3, high-risk activity has signiﬁcant eﬀects on EDR (β1 = 0.195, t = 4.058, p = .004). Speciﬁcally, the value of regression coeﬃcient implies that high-risk activity is associated with a 0.195 increase in EDR as compared with low-risk activity. However, the result shows that %HRR (β2 = 0.164, t = 0.628, p = .547) and EDL (β3 = 0.072, t = 1.700, p = .133) are not signiﬁcant predictors of EDR. Also, the likelihood ratio test statistics comparing Model 2 and Model 3 is 587.305 − 399.446 = 187.859 on nine degrees of freedom. (p < .001). Since the HLM analyses are based on random sampling, the results
7. Discussion Advancement in wearable technologies does not necessarily promise that wearable sensors can be applied to improve safety and health in construction sites. It is imperative to understand the characteristics of construction tasks, acquire quality data in the ﬁeld, properly process the collected data, and interpret the data in a meaningful way (Lee et al., 2017). In this regard, this study provides new venues for a better understanding of workers’ safety behaviors and may have a large impact on construction safety management. The results of this study present the potential of physiological sensory data collected from wearable sensors to understand workers’ perceived risk during their ongoing work by showing diﬀerences in EDR between low and high-risk activities (Table 2) and the eﬀects of high-risk activities on EDR (Table 3). Workers’ EDR during the high-risk activities was signiﬁcantly higher than EDR during the low-risk activities. However, there were no
Table 2 Each subject’s descriptive statistics (EDR and EDL). Subject
S1 S2 S3 S4 S5 S6 S7 S8 Mean
0.303 0.640 0.877 0.047 0.336 0.377 0.291 0.338 0.401
0.158 0.275 0.425 0.074 0.296 0.434 0.257 0.231 0.234
0.312 0.889 0.837 0.197 0.626 0.859 0.586 0.340 0.581
0.182 0.558 0.354 0.187 0.328 0.523 0.286 0.195 0.254
0.009 0.248 −0.040 0.150 0.290 0.482 0.295 0.002 0.180
−0.171 −0.654 −0.782 −0.721 −0.236 −0.398 0.027 −0.212 −0.382
0.697 0.709 0.676 0.376 0.886 0.768 0.456 0.665 0.276
−0.126 −0.545 −0.971 0.088 −0.545 −0.174 0.152 0.104 −0.252
0.654 0.873 0.556 0.273 0.780 1.103 0.550 0.486 0.373
0.044 0.019 −0.189 0.809 −0.309 0.224 0.125 0.315 0.130
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Fig. 7. Distribution of all subjects’ EDR and EDL in low & high-risk activities.
Fig. 8. Distribution of each subject's EDR in low-risk activities and high-risk activities.
Table 3 Result of hierarchical linear modeling. EDRij = β0 + β1High_Riskij + β2%HRRij + β3EDLij + uoj + u1jHigh_Riskij + u2j%HRRij + u3jEDLij + eij Parameters
Model 1 (Random Intercept)
Model 2 (Random) Slope
Model 3 (Final)
Regression coeﬃcient (ﬁxed eﬀects) Intercept (β0) High Risk (β1, 0 = low risk, 1 = high risk) %HRR (β2) EDL (β3)
0.480 (0.078)** – – –
0.378 (0.079)** 0.205 (0.021)** – –
0.378 (0.077)** 0.205 (0.055)**
0.384 0.195 0.164 0.072
0.118 (0.005) 0.048 (0.024)
0.108 (0.005) 0.048 (0.024)
0.103 (0.005) 0.046 (0.024) 0.021 (0.012)
0.082 (0.004) 0.063 (0 3 6) 0.015 (0.009) 0.467 (0.270) 0.012 (0.008) 0.001 (0.013) −0.031 (0.071) 0.025 (0.015) −0.033 (0.037) 0.026 (0.006) −0.017 (0.031)
Variance components (random eﬀects) Residual (σe2) Intercept (σu02) Slope – High Risk (σu12) Slope – %HRR (σu22) Slope – EDL (σu32) Subject & High-Risk Covariance (σu01) Subject & %HRR Covariance (σu02) Subject & EDL Covariance (σu03) High Risk & %HRR Covariance (σu12) High Risk & EDL Covariance (σu13) %HRR & EDL Covariance (σu23) Model summary −2 Log Likelihood Number of estimated parameters
Parameter estimate standard errors listed in parentheses. ** p < .01. 117
(0.094)** (0.048)** (0.261) (0.042)
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in order to improve safety in construction sites. For example, activity information from BIM could be useful to understand the causes of the perceived risk from wearable sensors. Safety managers will be able to identify the causes of perceived risk based on the characteristics of activities at the hazardous area. Although this study successfully demonstrated the feasibility of using physiological sensory data acquired from wearable sensors to understand construction workers’ perceived risk during their ongoing work, several limitations should be acknowledged. First, this study did not include all types of task in construction sites. Speciﬁcally, subjects in this study did not engage in very dangerous activities such as working on a steel beam or working on a scaﬀold which are common in construction sites. However, the authors intentionally focused on moderately high-risk activities (e.g., working on the ladder, working in the ceiling. etc.) which are more challenging to diﬀerentiate. Since EDR in this study represents the distinguishing power between low and moderately high-risk activities (e.g., working on the ladder, working in the ceiling. etc.), it will be able to distinguish more dangerous activities in the ﬁeld. Although this study includes various trades and activities in construction sites, it does not include outdoor activities. Since EDA is the result of sweat gland activity, physically demanding activities in high-temperature outdoor environments may have an impact on an individual's EDA. To address this issue, future studies should include diverse outdoor activities in construction sites. Finally, this study classiﬁes high-risk and low-risk activities based on the risk assessment by the two research team members to compare EDA data. The risk assessment from the research team member may not be able to reﬂect each subject's perceived risk perfectly. Since diﬀerent workers could have diﬀerent perceived risk based on each worker's characteristics such as personality, and job experience, each subject's evaluation of the perceived risk can compensate the limitations of the risk assessment of the research team. However, each subject's evaluation also has limitations related to limited memory and subjective assessment. Therefore, the future study should combine both assessment methods to improve the reliability in the classiﬁcation of the risk levels.
signiﬁcant diﬀerences in the mean of EDL between low and high-risk activities. Also, workers’ EDR were signiﬁcantly aﬀected by high-risk activities after controlling the eﬀects of physical demands (i.e., %HRR) and EDL as well as variations in individual diﬀerences (Table 3). Considering that EDR is a phasic component of EDA that reﬂects short-term responses to external stimulus and risk perception is an immediate reaction to recognized hazard in construction sites, EDR would be more related to risk perception than EDL. On the other hand, EDL is a tonic component of EDA that includes slow drift of the baseline EDA. As such, EDL has been introduced by a relevant indicator of long-term stress (Boucsein, 2012; Healey and Picard, 2005; Picard et al., 2016; Poh et al., 2010). Compare to the current survey-based methods to measure workers’ perceived risk, EDR acquired from wearable sensors will allow us to develop objective and real-time mechanisms for understanding workers’ perceived risk in construction sites. Although this study presents the feasibility of physiological responses collected from workers’ wearable devices to understand their perceived risk, there is much room for exploring additional potential in the future. In addition to the mean value of EDR, various features of EDR such as standard deviation of EDR, the linearity of EDR, and the number of peak can deepen our understanding of workers’ perceived risk during their ongoing work. The relationships between these various features and workers’ perceived risk can be investigated in future studies. Also, other physiological variables such as heart rate (HR) heart rate variability (HRV), inter-beat interval (IBI) and skin temperature (ST) are worthwhile to study in the future. A sympathetic nervous system aroused by signiﬁcant risk innervates the heart which results in changes in physiological responses that are related to heart activity such as HR, HRV, and IBI (Doorley et al., 2015). Also, previous studies found that changes in sympathetic nervous activities are related to ST changes (Azevedo et al., 2017). In this sense, the relationships between various features related to these physiological responses and workers’ perceived risk can be examined in the future. This study has focused on EDA because these other physiological variables can be contaminated by parasympathetic nervous system activities. In the future, more advanced data analysis methods can be applied for using these physiological responses to understand sympathetic arousal caused by risk in construction sites. Physiological responses collected from wearable devices are expected to deepen our understanding of risk perception during workers’ ongoing work. Since wearable devices measure an individual’s physiological response to hazard during the work, it would be more related to one’s own perceived risk. This personalized measurement could oﬀer a strong foundation to explore new areas for risk perception studies. For example, we can investigate the eﬀects of individual factors (e.g., ages, work experience, accident experience, trade, etc.) and their interactions on workers’ risk perception by comparing the physiological responses in the same condition. Continuous measurement of workers’ physiological responses to improve our understanding of risk perception provides ample opportunities to improve construction safety management. Speciﬁcally, the integration of physiological responses and other contextual information has a great potential to improve construction safety management practice. For example, we will be able to ﬁnd hazardous areas in a construction project by integrating workers’ perceived risk as estimated from physiological responses and their location that can be identiﬁed using GPS or indoor GPS embedded in wearable devices. Based on the hazardous areas, safety managers will be able to recognize places where he/she needs to pay more attention to prevent accidents. On the other hand, safety managers can identify the deviations between workers’ perceived risk and actual risk to develop more personalized safety programs. For example, if workers who engage in high-risk activities show low-risk perception signals, safety managers can develop safety training programs to increase their risk awareness. Furthermore, the perceived risk estimated from physiological responses can be integrated with other information such as activity, workgroup, and schedule that can be extracted from building information model (BIM)
8. Conclusion This study investigates the potential of using physiological sensory data (i.e., EDR and EDL) collected from oﬀ-the-shelf wristband typed sensors to understand construction workers’ perceived risk during their ongoing work. To achieve the objective, 30 h of physiological sensory data were collected from eight construction workers during their ongoing work. The results indicate that: (1) there are signiﬁcant diﬀerences in EDR between low and high-risk activities; (2) high-risk activity signiﬁcantly aﬀects workers’ EDR during their ongoing work. The main contribution of this study is to show the feasibility of using wearable sensors to understand workers’ perceived risk in construction sites continuously. An attempt to understand construction workers’ risk perception using EDR is a meaningful step to better understanding their safety behaviors in the ﬁeld. Considering the complexity and dynamicity of workers’ task in construction sites, objective, continuous, and non-intrusive monitoring of workers’ physiological responses is expected to contribute to a more in-depth understanding of construction workers’ perceived risk. The physiological responses from wearable devices will allow us to measure workers’ perceived risk, which provides a great opportunity to identify and manage risk at construction sites. Acknowledgment The authors would like to acknowledge their industry partners for their help in data collection, as well as anonymous participants who participated in the data collection. Also, the authors wish to thank Chris Soto from Skanska USA and Jad Ibrahim from Shade Cooperation for their support in collecting the ﬁeld data and providing general feedback 118
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on workers’ safety behaviors. The ﬁrst author also wishes to acknowledge ﬁnancial support by the University of Michigan from a Rackham Pre-doctoral Fellowship. All opinions and ﬁndings in this paper are those of the authors and do not necessarily represent those of the University of Michigan.
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