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1. Vision-Based Ergonomic Risk Assessment of Back-Support Exoskeleton for Construction Workers in Material Handling Tasks

   https://doi.org/10.1061/9780784485248.040  

   Liu, Yizhi; Ojha, Amit; Jebelli, Houtan (January 2024, Computing in Civil Engineering 2023)

   Work-related musculoskeletal disorders (WMSDs) are a leading cause of injury for workers who are performing physically demanding and repetitive construction tasks. With recent advances in robotics, wearable robots are introduced into the construction industry to mitigate the risk of WMSDs by correcting the workers’ postures and reducing the load exerted on their body joints. While wearable robots promise to reduce the muscular and physical demands on workers to perform tasks, there is a lack of understanding of the impact of wearable robots on worker ergonomics. This lack of understanding may lead to new ergonomic injuries for worker swearing exoskeletons. To bridge this gap, this study aims to assess the workers’ ergonomic risk when using a wearable robot (back-support exoskeleton) in one of the most common construction tasks, material handling. In this research, a vision-based pose estimation algorithm was developed to estimate the pose of the worker while wearing a back-support exoskeleton. As per the estimated pose, joint angles between connected body parts were calculated. Then, the worker’s ergonomic risk was assessed from the calculated angles based on the Rapid Entire Body Assessment (REBA) method. Results showed that using the back-support exoskeleton reduced workers’ ergonomic risk by 31.7% by correcting awkward postures of the trunk and knee during material handling tasks, compared to not using the back-support exoskeleton. The results are expected to facilitate the implementation of wearable robots in the construction industry. 

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2. Improving Workers’ Musculoskeletal Health During Human-Robot Collaboration Through Reinforcement Learning

   https://doi.org/10.1177/00187208231177574  

   Xie, Ziyang; Lu, Lu; Wang, Hanwen; Su, Bingyi; Liu, Yunan; Xu, Xu (May 2023, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   ObjectiveThis study aims to improve workers’ postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method. BackgroundHuman-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers. MethodsThe proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers’ continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers’ CAP score by adjusting the positions and orientations of the robot end effector. ResultsIn an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants. ConclusionThe proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture. ApplicationThe proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints. 

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3. Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors

   https://doi.org/10.1115/DMD2019-3266  

   Yang, Jianfu; Huang, Tzu-Hao; Yu, Shuangyue; Yang, Xiaolong; Su, Hao; Spungen, Ann M.; Tsai, Chung-Ying (April 2019, 2019 Design of Medical Devices Conference)

   This paper presents a portable inertial measurement unit (IMU)-based motion sensing system and proposed an adaptive gait phase detection approach for non-steady state walking and multiple activities (walking, running, stair ascent, stair descent, squat) monitoring. The algorithm aims to overcome the limitation of existing gait detection methods that are time-domain thresholding based for steady-state motion and are not versatile to detect gait during different activities or different gait patterns of the same activity. The portable sensing suit is composed of three IMU sensors (wearable sensors for gait phase detection) and two footswitches (ground truth measurement and not needed for gait detection of the proposed algorithm). The acceleration, angular velocity, Euler angle, resultant acceleration, and resultant angular velocity from three IMUs are used as the input training data and the data of two footswitches used as the training label data (single support, double support, swing phase). Three methods 1) Logistic Regression (LR), 2) Random Forest Classifier (RF), and 3) Artificial Neural Network (NN) are used to build the gait phase detection models. The result shows our proposed gait phase detection with Random Forest Classifier can achieve 98.94% accuracy in walking, 98.45% in running, 99.15% in stair-ascent, 99.00% in stair-descent, and 99.63% in squatting. It demonstrates that our sensing suit can not only detect the gait status in any transient state but also generalize to multiple activities. Therefore, it can be implemented in real-time monitoring of human gait and control of assistive devices. 

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4. A deep learning-based RULA method for working posture

   https://doi.org/10.1177/1071181319631174  

   Li, Li; Xu, Xu (October 2019, Proceedings of the Human Factors and Ergonomics Society 2019 Annual Meeting)

   Musculoskeletal disorders (MSDs) represent one of the leading cause of injuries from modern industries. Previous research has identified a causal relation between MSDs and awkward working postures. Therefore, a robust tool for estimating and monitoring workers’ working posture is crucial to MSDs prevention. The Rapid Upper Limb Assessment (RULA) is one of the most adopted observational methods for assessing working posture and the associated MSDs risks in industrial practice. The manual application of RULA, however, can be time consuming. This research proposed a deep learning-based method for realtime estimating RULA from 2-D articulated pose using deep neural network. The method was trained and evaluated by 3-D pose data from Human 3.6, an open 3-D pose dataset, and achieved overall Marginal Average Error (MAE) of 0.15 in terms of RULA grand score (or 3.33% in terms of percentage error). All the data and code can be found at the first author’s GitHub (https://github.com/LLDavid/RULA_machine) 

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5. A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities

   https://doi.org/10.1038/s41597-023-02840-6  

   Scherpereel, Keaton; Molinaro, Dean; Inan, Omer; Shepherd, Max; Young, Aaron (December 2023, Scientific Data)

   Abstract Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and electromyography (EMG) were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors. 

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