Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers

Abstract Awkward working postures are the main risk factor for work-related musculoskeletal disorders (WMSDs) causing non-fatal occupational injuries among construction workers. However, it remains a challenge to use existing risk assessment methods for detecting and classifying awkward working postures because these methods are either intrusive or rely on subjective judgment. Therefore, this study developed a novel and non-invasive method to automatically detect and classify awkward working postures based on foot plantar pressure distribution data measured by a wearable insole pressure system. Ten asymptomatic participants performed five different types of awkward working postures (i.e., overhead working, squatting, stooping, semi-squatting, and one-legged kneeling) in a laboratory setting. Four supervised machine learning classifiers (i.e., artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM)) were used for classification performance using a 0.32 s window size. Cross-validation results showed that the SVM classifier (i.e., the best classifier) obtained a classification performance with an accuracy of 99.70% and a sensitivity of each awkward working posture was above 99.00% at 0.32 s window size. The findings substantiated that it is feasible to use a wearable insole pressure system to identify risk factors for developing WMSDs, and could help safety managers to minimize workers' exposure to awkward working postures.

[1]  P Kivi,et al.  Analysis and improvement of work postures in the building industry: application of the computerised OWAS method. , 1991, Applied ergonomics.

[2]  Jun Qiu,et al.  Construction worker's awkward posture recognition through supervised motion tensor decomposition , 2017 .

[3]  Frédéric Bosché,et al.  Musculoskeletal disorders in construction: A review and a novel system for activity tracking with body area network. , 2016, Applied ergonomics.

[4]  Heng Li,et al.  Low-Cost Ergonomic Intervention for Mitigating Physical and Subjective Discomfort during Manual Rebar Tying , 2017 .

[5]  Jochen Teizer,et al.  Real-time construction worker posture analysis for ergonomics training , 2012, Adv. Eng. Informatics.

[6]  Muhammad Usman Ilyas,et al.  Activity recognition using smartphone sensors , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[7]  Youngho Kim,et al.  Lower extremity joint kinetics and lumbar curvature during squat and stoop lifting , 2009, BMC musculoskeletal disorders.

[8]  George Fulk,et al.  Wearable shoe-based device for rehabilitation of stroke patients , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[9]  Yantao Yu,et al.  Visualization technology-based construction safety management: A review , 2017 .

[10]  Julie R Steele,et al.  Gait, balance and plantar pressures in older people with toe deformities. , 2011, Gait & posture.

[11]  Tao Dong,et al.  A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time , 2017, Sensors.

[12]  Faicel Chamroukhi,et al.  Physical Human Activity Recognition Using Wearable Sensors , 2015, Sensors.

[13]  B. Jonsson,et al.  Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms. , 1987, Applied ergonomics.

[14]  Mohamed Al-Hussein,et al.  Ergonomic Analysis and the Need for Its Integration for Planning and Assessing Construction Tasks , 2012 .

[15]  Tomasz Jelonek,et al.  The prevalence of self-reported musculoskeletal symptoms among loggers in Poland , 2016 .

[16]  Thomas J. Armstrong,et al.  Motion Data-Driven Biomechanical Analysis during Construction Tasks on Sites , 2015 .

[17]  G. Borg.  Borg's Perceived Exertion and Pain Scales , 1998 .

[18]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[19]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[20]  Billur Barshan,et al.  Detecting Falls with Wearable Sensors Using Machine Learning Techniques , 2014, Sensors.

[21]  David J. Edwards,et al.  Effects of different weights and lifting postures on balance control following repetitive lifting tasks in construction workers , 2017 .

[22]  Jamie McGaha,et al.  Exploring physical exposures and identifying high-risk work tasks within the floor layer trade. , 2014, Applied ergonomics.

[23]  Fei Dai,et al.  Risk Assessment of Work-Related Musculoskeletal Disorders in Construction: State-of-the-Art Review , 2015 .

[24]  Hong Zhang,et al.  Development of ergonomic posture recognition technique based on 2D ordinary camera for construction hazard prevention through view-invariant features in 2D skeleton motion , 2017, Adv. Eng. Informatics.

[25]  L Punnett,et al.  PATH: a work sampling-based approach to ergonomic job analysis for construction and other non-repetitive work. , 1996, Applied ergonomics.

[26]  S Hignett,et al.  Rapid entire body assessment (REBA). , 2000, Applied ergonomics.

[27]  Enrique Valero,et al.  Analysis of construction trade worker body motions using a wearable and wireless motion sensor network , 2017 .

[28]  Heng Li,et al.  Identification of Biomechanical Risk Factors for the Development of Lower-Back Disorders during Manual Rebar Tying , 2017 .

[29]  Héctor Pomares,et al.  Window Size Impact in Human Activity Recognition , 2014, Sensors.

[30]  Kenneth Meijer,et al.  Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.

[31]  Jia Chen,et al.  Risk Factors Identification for Work-Related Musculoskeletal Disorders with Wearable and Connected Gait Analytics System , 2017, 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[32]  Dong-Eun Lee,et al.  Artificial Neural Network–Based Slip-Trip Classifier Using Smart Sensor for Construction Workplace , 2016 .

[33]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[34]  L McAtamney,et al.  RULA: a survey method for the investigation of work-related upper limb disorders. , 1993, Applied ergonomics.

[35]  David J. Edwards,et al.  Wearable Insole Pressure Sensors for Automated Detection and Classification of Slip-Trip-Loss of Balance Events in Construction Workers , 2018 .

[36]  W. Karwowski.  International encyclopedia of ergonomics and human factors , 2001 .

[37]  Maxwell Fordjour Antwi-Afari,et al.  Identification of potential biomechanical risk factors for low back disorders during repetitive rebar lifting , 2018 .

[38]  Yangsheng Xu,et al.  Intelligent shoes for abnormal gait detection , 2008, 2008 IEEE International Conference on Robotics and Automation.

[39]  Heng Li,et al.  Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers , 2017 .

[40]  Edward Sazonov,et al.  Identifying Activity Levels and Steps of People With Stroke Using a Novel Shoe-Based Sensor , 2012, Journal of neurologic physical therapy : JNPT.

[41]  Rezaul Begg,et al.  Overview of movement analysis and gait features , 2006 .

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

[43]  Amir H. Behzadan,et al.  Smartphone-based construction workers' activity recognition and classification , 2016 .

[44]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[45]  Howard Chen,et al.  A comparison of instrumentation methods to estimate thoracolumbar motion in field-based occupational studies. , 2015, Applied ergonomics.

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

[47]  G. Szeto,et al.  The prevalence of musculoskeletal symptoms in the construction industry: a systematic review and meta-analysis , 2018, International Archives of Occupational and Environmental Health.

[48]  Anasua Bhattacharya,et al.  Costs of occupational musculoskeletal disorders (MSDs) in the United States , 2014 .

[49]  R W McGorry,et al.  Power grip strength as a function of tool handle orientation and location , 2007, Ergonomics.

[50]  Hong Zhang,et al.  Wearable IMU-based real-time motion warning system for construction workers' musculoskeletal disorders prevention , 2017 .

[51]  Chris D. Nugent,et al.  Biomedical Sensors for Ambient Assisted Living , 2010 .

[52]  D. E. Gyi,et al.  Building healthy construction workers: Their views on health, wellbeing and better workplace design. , 2016, Applied ergonomics.

[53]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[54]  Chin-Feng Lai,et al.  Detection of Cognitive Injured Body Region Using Multiple Triaxial Accelerometers for Elderly Falling , 2011, IEEE Sensors Journal.

[55]  SangUk Han,et al.  A vision-based motion capture and recognition framework for behavior-based safety management , 2013 .

[56]  Heng Li,et al.  Automated detection and classification of construction workers’ awkward working postures using wearable insole pressure sensors , 2018 .

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