A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback

Abstract

Work-related musculoskeletal disorders are a major concern globally affecting societies, companies, and individuals. To address this, a new sensor-based system is presented: the Smart Workwear System, aimed at facilitating preventive measures by supporting risk assessments, work design, and work technique training. The system has a module-based platform that enables flexibility of sensor-type utilization, depending on the specific application. A module of the Smart Workwear System that utilizes haptic feedback for work technique training is further presented and evaluated in simulated mail sorting on sixteen novice participants for its potential to reduce adverse arm movements and postures in repetitive manual handling. Upper-arm postures were recorded, using an inertial measurement unit (IMU), perceived pain/discomfort with the Borg CR10-scale, and user experience with a semi-structured interview. This study shows that the use of haptic feedback for work technique training has the potential to significantly reduce the time in adverse upper-arm postures after short periods of training. The haptic feedback was experienced positive and usable by the participants and was effective in supporting learning of how to improve postures and movements. It is concluded that this type of sensorized system, using haptic feedback training, is promising for the future, especially when organizations are introducing newly employed staff, when teaching ergonomics to employees in physically demanding jobs, and when performing ergonomics interventions.

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

“…No included study reported post-intervention monitoring to assess the retention of improvements following WIST feedback. Four types of feedback prompts were identified throughout the included studies: auditory [ 68 , 71 , 73 , 74 , 75 , 80 ]; vibrotactile (haptic) [ 69 , 72 , 74 , 76 , 77 , 78 , 79 ]; visual [ 67 , 70 , 71 , 72 , 74 , 75 , 78 ] and summary feedback (visual) [ 74 ]. The most common multimodal feedback interaction was auditory and visual [ 71 , 74 , 75 ].…”

“…The most common multimodal feedback interaction was auditory and visual [ 71 , 74 , 75 ]. Most studies applied concurrent bandwidth feedback [ 67 , 69 , 73 , 75 , 76 , 77 , 79 , 80 ] (i.e., a feedback prompt when a movement variable exceeds a pre-determined set-point (feedback trigger) during the activity/task [ 131 , 132 ]; and in conjunction with a pre-determined time period [ 68 , 71 , 72 , 74 , 78 ] ( Table 4 ); the remaining studies used terminal bandwidth feedback (feedback post-activity) [ 70 ] and summary feedback in addition to visual, auditory and vibrotactile feedback [ 74 ].…”

“…A slight risk increase (RULA/LUBA) was observed for arms during tasks 2–4 using visual/vibrotactile feedback [ 78 ] (though the results were confounded by participants reaching for the chair during these tasks). However, [ 79 ] identified less accumulated time and angles for arms during the simulated mail sorting tasks. Visual feedback increased step counts during office tasks [ 67 ].…”

Development of a Digital Video-Based Occupational Risk Assessment Method

“…To meet the demands of a comprehensive risk assessment, the greatest advantage is advancements in technology which allow for even further possibilities than we have proposed. Risk assessment systems or tools might also take multimodal input (i.e., auditory, visual, physiological) through sensor systems built specifically for risk assessment ( 36 ). The information from these systems could then be added to the Work Characteristics matrix derived from the video recordings.…”

Effects of Sensor Types and Angular Velocity Computational Methods in Field Measurements of Occupational Upper Arm and Trunk Postures and Movements

“…The number of studies using integrated sensors in IMUs increased after 2016 ( Table 1 ). Given the increase in the use of IMUs, this information is also needed when comparing new IMU-based measurements as in, e.g., Lind et al [ 42 ], Lind et al [ 43 ], to the recommended threshold limit values based on accelerometer-based measurements [ 14 ]. Moreover, this information can also be used for merging results from older and more recent studies to increase the precision on the associations between inclinations and angular velocities of arms and trunk, and MSDs.…”