E-happiness physiological indicators of construction workers' productivity: A machine learning approach

Abstract

Worker productivity is a major concern for the construction industry. Many studies assessed the effect of various factors, such as the work environment and worker health, on productivity. Nevertheless, the extent to which an automatic productive assessment can benefit from wearable electronic-based sensor technologies for physiological and psychological tracking purposes has not yet been fully investigated. This work assesses the ability of capturing the effect of construction workers' happiness on their productivity using physiological signals collected via wearable sensors. Data from both a traditional tracking process (human annotators) and an automated worker physiological signal tracking process that was designed for the purposes of this study were compiled. By considering the traditional tracking process as the baseline for the comparison, this study evaluated the effectiveness of automating happiness tracking as a leading indicator of construction workers' productivity. The physiological signal data collected included blood volume pulse (BVP), respiration rate (RR), heart rate (HR), galvanic skin response (GSR), and skin temperature (TEMP). These data were obtained from a 4-day field study conducted at a prefabricated stone construction factory. The study concluded that a moderate positive correlation exists between a worker's emotional status and his productivity exists, with a p-value = 5.5 × 10-8 and a Pearson's coefficient of 0.43.

Simultaneous piezoelectric noninvasive detection of multiple vital signs

“…The physiological multi vital signs based emotion recognition system combined with statistical analysis, the system was able to recognize emotion on three basic emotional states 46 . It also can be employed in developing an automatic productive assessment systems to assess the ability of capturing the effect of construction workers' happiness on their productivity using physiological signals 47 .…”

Using machine learning to analyze and predict construction task productivity

“…For the specific scenario to deal with the problem associated with productivity prediction, there are many ML approaches that have been proposed. Some approaches include artificial neural network (ANN) (Alaloul et al., 2018; Badawy et al., 2019; Golnaraghi et al., 2019; Heravi & Eslamdoost, 2015; Khaled et al., 2017; Moselhi & Khan, 2012; Nasirzadeh et al., 2020; Portas & AbouRizk, 1997; Tsehayae & Fayek, 2016), computational intelligence (Dissanayake et al., 2005), neurofuzzy (Boussabaine, 2001; Mirahadi & Zayed, 2016), self‐organizing maps (Oral et al., 2016), random forest (Awada et al., 2021; Ebrahimi et al., 2021; Liu et al., 2018; Momade et al., 2020), ML classifiers (Jassmi et al., 2019), and SVM (Momade et al., 2020). Determining the factors that affect the productivity of construction labor is often the first step in establishing ML models.…”

“…The performance of these models greatly depends on the input factors. Factors include contract and delivery method (Alaloul et al., 2018), management and supervision (leadership and competency, trust in foreman, fairness in review) (Khaled et al., 2017; Momade et al., 2020), external conditions (temperature, humidity, wind speed, precipitation) (Dissanayake et al., 2005; Golnaraghi et al., 2019), site conditions (equipment, floor level, work type, workload, complexity of task, congestion, interruptions) (Dissanayake et al., 2005; Ebrahimi et al., 2021; Golnaraghi et al., 2019; Khaled et al., 2017), and workers characteristics (age, experience, skill, crew size, team spirit, happiness (Alaloul et al., 2018; Jassmi et al., 2019; Khaled et al., 2017; Oral et al., 2016).…”

Spatialising happiness economics: Global metrics, urban politics, and embodied technologies

“…In the UAE there is a long-running government commitment to synthesising happiness economics and smart cities agendas that frames citizens as consumers of public services aimed at maximising their happiness (Bin Bishr, 2019). Also in the UAE, machine learning has been used to infer or "automatically detect" emotions from physiological signals in order to assess the correlation between happiness and productivity among construction workers (Al Jassmi et al, 2019). These examples are indicative of the varied uses of happiness sensing and emotion measurement, and their potential deployment in surveillance and coercive forms of control, which suggests that more attention should be paid to their political geographies and implications for citizenship and governance.…”