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1. 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)

   This 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.

   Human-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.

   The 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.

   In 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.

   The 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.

   The 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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2. Spine-Inspired Continuum Soft Exoskeleton for Stoop Lifting Assistance

   https://doi.org/10.1109/LRA.2019.2935351  

   Yang, Xiaolong ; Huang, Tzu-Hao ; Hu, Hang ; Yu, Shuangyue ; Zhang, Sainan ; Zhou, Xianlian ; Carriero, Alessandra ; Yue, Guang ; Su, Hao ( August 2019 , IEEE Robotics and Automation Letters)

   Back injuries are the most prevalent work-related musculoskeletal disorders and represent a major cause of disability and socio-economic problems. Although innovations in wearable robots aim to alleviate this hazard, the majority of existing exoskeletons are obtrusive because the rigid linkage design limits natural movement, thus causing ergonomic risk. Moreover, these existing systems are typically only suitable for one type of movement assistance, not ubiquitous for a wide variety of activities. To fill in this gap, this paper presents a new wearable robot design approach continuum soft exoskeleton. This wearable robot is unobtrusive and assists both squat and stoops while not impeding walking motion. To tackle the challenge of the unique anatomy of spine, our robot is conformal to human anatomy and it can reduce multiple types of forces along the human spine such as the spinae muscle force, shear, and compression force of the lumbar vertebrae. We derived kinematics and kinetics models of this mechanism and established an analytical biomechanics model of human-robot interaction. Quantitative analysis of disc compression force, disc shear force and muscle force was performed in simulation. We further developed a virtual impedance control strategy to deliver force control and compensate hysteresis of Bowden cable transmission. The feasibility of the prototype was experimentally tested on three healthy subjects. The root mean square error of force tracking is 6.63 N (3.3 % of the 200N peak force) and it demonstrated that it can actively control the stiffness to the desired value. This continuum soft exoskeleton represents a feasible solution with the potential to reduce back pain for multiple activities and multiple forces along the human spine. 

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3. Merging fNIRS-EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinson’s Disease

   https://doi.org/10.1109/TNSRE.2020.2987888  

   Abtahi, Mohammadreza ; Borgheai, Seyed Bahram ; Jafari, Roohollah ; Constant, Nicholas ; Diouf, Rassoul ; Shahriari, Yalda ; Mankodiya, Kunal ( January 2020 , IEEE Transactions on Neural Systems and Rehabilitation Engineering)

   Functional connectivity between the brain and body kinematics has largely not been investigated due to the requirement of motionlessness in neuroimaging techniques such as functional magnetic resonance imaging (fMRI). However, this connectivity is disrupted in many neurodegenerative disorders, including Parkinson’s Disease (PD), a neurological progressive disorder characterized by movement symptoms including slowness of movement, stiffness, tremors at rest, and walking and standing instability. In this study, brain activity is recorded through functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and body kinematics were captured by a motion capture system (Mocap) based on an inertial measurement unit (IMU) for gross movements (large movements such as limb kinematics), and the WearUp glove for fine movements (small range movements such as finger kinematics). PD and neurotypical (NT) participants were recruited to perform 8 different movement tasks. The recorded data from each modality have been analyzed individually, and the processed data has been used for classification between the PD and NT groups. The average changes in oxygenated hemoglobin (HbO2) from fNIRS, EEG power spectral density in the Theta, Alpha, and Beta bands, acceleration vector from Mocap, and normalized WearUp flex sensor data were used for classification. 12 different support vector machine (SVM) classifiers have been used on different datasets such as only fNIRS data, only EEG data, hybrid fNIRS/EEG data, and all the fused data for two classification scenarios: classifying PD and NT based on individual activities, and all activity data fused together. The PD and NT group could be distinguished with more than 83% accuracy for each individual activity. For all the fused data, the PD and NT groups are classified with 81.23%, 92.79%, 92.27%, and 93.40% accuracy for the fNIRS only, EEG only, hybrid fNIRS/EEG, and all fused data, respectively. The results indicate that the overall performance of classification in distinguishing PD and NT groups improves when using both brain and body data. 

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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. 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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