A novel wearable system for the online assessment of risk for biomechanical load in repetitive efforts

Peppoloni, Lorenzo; Filippeschi, Alessandro; Ruffaldi, Emanuele; Avizzano, Carlo Alberto

doi:10.1016/j.ergon.2015.07.002

Cited by 108 publications

(106 citation statements)

References 16 publications

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“…For this application scenario, different solutions aiming at monitoring and improving occupational ergonomics are discussed (Cheng, Migliaccio, Teizer, & Gatti, 2013;Peppoloni, Filippeschi, Ruffaldi, & Avizzano, 2016;Valero, Sivanathan, Bosché, & Abdel-Wahab, 2016). For this application scenario, different solutions aiming at monitoring and improving occupational ergonomics are discussed (Cheng, Migliaccio, Teizer, & Gatti, 2013;Peppoloni, Filippeschi, Ruffaldi, & Avizzano, 2016;Valero, Sivanathan, Bosché, & Abdel-Wahab, 2016).…”

Section: Application Scenarios Of Physiolytics At the Workplacementioning

confidence: 99%

“…Lastly, health issues may also emanate from incorrectly carrying out heavy labour (eg, construction or assembly line work). For this application scenario, different solutions aiming at monitoring and improving occupational ergonomics are discussed (Cheng, Migliaccio, Teizer, & Gatti, 2013;Peppoloni, Filippeschi, Ruffaldi, & Avizzano, 2016;Valero, Sivanathan, Bosché, & Abdel-Wahab, 2016). In identifying risk factors, like bad work posture or poorly coordinated movement, many of the presented solutions rely on sensors for inertial measurement, location sensing, and motion detection algorithms together with instant feedback mechanisms.…”

Section: Application Scenarios Of Physiolytics At the Workplacementioning

confidence: 99%

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Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective

Mettler

Wulf

2018

Information Systems Journal

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Wearables paired with data analytics and machine learning algorithms that measure physiological (and other) parameters are slowly finding their way into our workplace. Several studies have reported positive effects from using such “physiolytics” devices and purported the notion that it may lead to significant workplace safety improvements or to increased awareness among employees concerning unhealthy work practices and other job‐related health and well‐being issues. At the same time, physiolytics may cause an overdependency on technology and create new constraints on privacy, individuality, and personal freedom. While it is easy to understand why organizations are implementing physiolytics, it remains unclear what employees think about using wearables at their workplace. Using an affordance theory lens, we, therefore, explore the mental models of employees who are faced with the introduction of physiolytics as part of corporate wellness or security programs. We identify five distinct user types each of which characterizes a specific viewpoint on physiolytics at the workplace: the freedom loving, the individualist, the cynical, the tech independent, and the balancer. Our findings allow for better understanding the wider implications and possible user responses to the introduction of wearable technologies in occupational settings and address the need for opening up the “user black box” in IS use research.

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Section: Application Scenarios Of Physiolytics At the Workplacementioning

confidence: 99%

Section: Application Scenarios Of Physiolytics At the Workplacementioning

confidence: 99%

Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective

Mettler

Wulf

2018

Information Systems Journal

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“…This enthusiasm is explained by the potential these technologies have to address the growing problem of work-related musculoskeletal disorders (MDSs) by reducing workers exposure to biomechanical MSDs risk factors [4], [5], [6]. Wearable sensors that monitor a person's physical state are another promising technology which may help decrease the prevalence of MSDs [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…Collaborative robots and exoskeletons directly help a human operator performing a task, either by providing physical assistance or by acting as a partner and performing part of the task [1], [2], [3]. Conversely, wearable sensors can provide feedback about the physical or physiological state of the operator to help her/him identify dangerous situations [7], [8]. Despite their different modes of action, these systems share a common goal: improving health at work.…”

Section: Introductionmentioning

confidence: 99%

Ethical and Social Considerations for the Introduction of Human-Centered Technologies at Work

Maurice

Allienne

Malaisé

et al. 2018

2018 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO)

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Human-centered technologies such as collaborative robots, exoskeletons, and wearable sensors are rapidly spreading in industry and manufacturing because of their intrinsic potential at assisting workers and improving their working conditions. The deployment of these technologies, albeit inevitable, poses several ethical and societal issues. Guidelines for ethically aligned design of autonomous and intelligent systems do exist, however we argue that ethical recommendations must necessarily be complemented by an analysis of the social impact of these technologies. In this paper, we report on our preliminary studies on the opinion of factory workers and of people outside this environment on humancentered technologies at work. In light of these studies, we discuss ethical and social considerations for deploying these technologies in a way that improves acceptance.

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“…In another experiment to investigate the risk of developing WMSDs among bicycle repair workers, [12] used still photography and video photography to collect the data needed for the risk assessment. These methods, though easy to use, are time consuming, unreliable [13], and do not give the 3D information as well as accurate joint information of workers in congested workplaces. In order to find a more suitable and more convenient method for data collection on the Shop floor, [5] compared the use of Kinect for data collection with the use of observation methods during an ergonomic assessment of postural load using OWAS.…”

Section: Literature Review Of Camera-based Data Collection For Industmentioning

confidence: 99%

Gesture Detection Towards Real-Time Ergonomic Analysis for Intelligent Automation Assistance

Mgbemena

Oyekan

Tiwari

et al. 2016

Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future

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Abstract. Manual handling involves transporting of load by hand through lifting or lowering and operators on the manufacturing shop floor are daily faced with constant lifting and lowering operations which leads to Work-Related Musculoskeletal Disorders. The trend in data collection on the Shop floor for ergonomic evaluation during manual handling activities has revealed a gap in gesture detection as gesture triggered data collection could facilitate more accurate ergonomic data capture and analysis. This paper presents an application developed to detect gestures towards triggering real-time human motion data capture on the shop floor for ergonomic evaluations and risk assessment using the Microsoft Kinect. The machine learning technology known as the discrete indicator -precisely the AdaBoost Trigger indicator was employed to train the gestures. Our results show that the Kinect can be trained to detect gestures towards real-time ergonomic analysis and possibly offering intelligent automation assistance during human posture detrimental tasks.

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A novel wearable system for the online assessment of risk for biomechanical load in repetitive efforts

Cited by 108 publications

References 16 publications

Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective

Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective

Ethical and Social Considerations for the Introduction of Human-Centered Technologies at Work

Gesture Detection Towards Real-Time Ergonomic Analysis for Intelligent Automation Assistance

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