Ergonomic analysis of construction worker's body postures using wearable mobile sensors.

Construction jobs are more labor-intensive compared to other industries. As such, construction workers are often required to exceed their natural physical capability to cope with the increasing complexity and challenges in this industry. Over long periods of time, this sustained physical labor causes bodily injuries to the workers which in turn, conveys huge losses to the industry in terms of money, time, and productivity. Various safety and health organizations have established rules and regulations that limit the amount and intensity of workers' physical movements to mitigate work-related bodily injuries. A precursor to enforcing and implementing such regulations and improving the ergonomics conditions on the jobsite is to identify physical risks associated with a particular task. Manually assessing a field activity to identify the ergonomic risks is not trivial and often requires extra effort which may render it to be challenging if not impossible. In this paper, a low-cost ubiquitous approach is presented and validated which deploys built-in smartphone sensors to unobtrusively monitor workers' bodily postures and autonomously identify potential work-related ergonomic risks. Results indicates that measurements of trunk and shoulder flexions of a worker by smartphone sensory data are very close to corresponding measurements by observation. The proposed method is applicable for workers in various occupations who are exposed to WMSDs due to awkward postures. Examples include, but are not limited to industry laborers, carpenters, welders, farmers, health assistants, teachers, and office workers.

[1]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[2]  Brian D Lowe,et al.  Accuracy and validity of observational estimates of wrist and forearm posture , 2004, Ergonomics.

[3]  Jose Antonio Diego-Mas,et al.  Using Kinect™ sensor in observational methods for assessing postures at work. , 2014, Applied ergonomics.

[4]  Paul J. M. Havinga,et al.  A Survey of Online Activity Recognition Using Mobile Phones , 2015, Sensors.

[5]  Liyun Yang,et al.  Development and validation of a novel iOS application for measuring arm inclination , 2015 .

[6]  S E Mathiassen,et al.  Assessment of physical work load in epidemiologic studies: concepts, issues and operational considerations. , 1994, Ergonomics.

[7]  Jack P. Callaghan,et al.  Determining the optimal size for posture categories used in video-based posture assessment methods , 2009, Ergonomics.

[8]  Hanghang Tong,et al.  Activity recognition with smartphone sensors , 2014 .

[9]  Franck Multon,et al.  Pose Estimation with a Kinect for Ergonomic Studies: Evaluation of the Accuracy Using a Virtual Mannequin , 2015, Sensors.

[10]  E. Viikari-Juntura,et al.  Validity of self-reported physical work load in epidemiologic studies on musculoskeletal disorders. , 1996, Scandinavian journal of work, environment & health.

[11]  Koshy Varghese,et al.  Accelerometer-Based Activity Recognition in Construction , 2011, J. Comput. Civ. Eng..

[12]  B. Lowe,et al.  Observation-based posture assessment : review of current practice and recommendations for improvement , 2014 .

[13]  Peter H. Veltink,et al.  Measuring orientation of human body segments using miniature gyroscopes and accelerometers , 2005, Medical and Biological Engineering and Computing.

[14]  G. David Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders. , 2005, Occupational medicine.

[15]  Francesca Odone,et al.  Feature selection for high-dimensional data , 2009, Comput. Manag. Sci..

[16]  Svend Erik Mathiassen,et al.  Cost-efficient measurement strategies for posture observations based on video recordings. , 2013, Applied ergonomics.

[17]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[18]  Patricia L Weir,et al.  The effect of posture category salience on decision times and errors when using observation-based posture assessment methods , 2012, Ergonomics.

[19]  Yeh-Liang Hsu,et al.  A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring , 2010, Sensors.

[20]  Changbum R. Ahn,et al.  The Validation of Gait-Stability Metrics to Assess Construction Workers' Fall Risk , 2014 .

[21]  Å. Kilbom,et al.  Assessment of physical exposure in relation to work-related musculoskeletal disorders--what information can be obtained from systematic observations? , 1994, Scandinavian journal of work, environment & health.

[22]  Amir H. Behzadan,et al.  Wearable sensor-based activity recognition for data-driven simulation of construction workers' activities , 2015, 2015 Winter Simulation Conference (WSC).

[23]  Lianne Brito Integrated Mobile Sensor-Based Activity Recognition of Construction Equipment and Human Crews , 2015 .

[24]  John Rosecrance,et al.  Reliability of assessing upper limb postures among workers performing manufacturing tasks. , 2009, Applied ergonomics.

[25]  J. Winkel,et al.  Self-assessed and directly measured occupational physical activities--influence of musculoskeletal complaints, age and gender. , 2004, Applied ergonomics.

[26]  Roger O. Smith,et al.  Feature Extraction Method for Real Time Human Activity Recognition on Cell Phones , 2011 .

[27]  Kay Teschke,et al.  Measuring posture for epidemiology: Comparing inclinometry, observations and self-reports , 2009, Ergonomics.

[28]  Nicolas Vuillerme,et al.  Performance Evaluation of Smartphone Inertial Sensors Measurement for Range of Motion , 2015, Sensors.

[29]  Abdullatif Alwasel,et al.  Sensing Construction Work-Related Musculoskeletal Disorders (WMSDs) , 2011 .

[30]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[31]  Gabriele Bleser,et al.  Innovative system for real-time ergonomic feedback in industrial manufacturing. , 2013, Applied ergonomics.

[32]  J. Kaufman,et al.  Comparison of self-report, video observation and direct measurement methods for upper extremity musculoskeletal disorder physical risk factors , 2001, Ergonomics.