This paper makes two scientific contributions to the field of exoskeleton-based action and movement recognition. First, it presents a novel machine learning and pattern recognition-based framework that can detect a wide range of actions and movements - walking, walking upstairs, walking downstairs, sitting, standing, lying, stand to sit, sit to stand, sit to lie, lie to sit, stand to lie, and lie to stand, with an overall accuracy of 82.63%. Second, it presents a comprehensive comparative study of different learning approaches - Random Forest, Artificial Neural Network, Decision Tree, Multiway Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Decision Stump, AutoMLP, Linear Regression, Vector Linear Regression, Random Tree, Naïve Bayes, Naïve Bayes (Kernel), Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Deep Learning applied to this framework. The performance of each of these learning approaches was boosted by using the AdaBoost algorithm, and the Cross Validation approach was used for training and testing. The results show that in boosted form, the k-NN classifier outperforms all the other boosted learning approaches and is, therefore, the optimal learning method for this purpose. The results presented and discussed uphold the importance of this work to contribute towards augmenting the abilities of exoskeleton-based assisted and independent living of the elderly in the future of Internet of Things-based living environments, such as Smart Homes. As a specific use case, we also discuss how the findings of our work are relevant for augmenting the capabilities of the Hybrid Assistive Limb exoskeleton, a highly functional lower limb exoskeleton.
Characterizing the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton
Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin（查拉德电气与计算机工程系，德克萨斯大学奥斯汀分校）；Department of Neurology, The University of Texas at Austin（神经病学系，德克萨斯大学奥斯汀分校）；Walker Department of Mechanical Engineering, The University of Texas at Austin（沃克机械工程系，德克萨斯大学奥斯汀分校）
Intelligent upper-limb exoskeleton integrated with soft wearable bioelectronics and deep-learning for human intention-driven strength augmentation based on sensory feedback
Dongguk University（东国大学）；Georgia Institute of Technology（佐治亚理工学院）；Yonsei University（延世大学）；Emory University School of Medicine（埃默里大学医学院）
Heidelberg University（海德堡大学）；Interdisciplinary Center for Scientific Computing（跨学科科学计算中心）
Designing an exoskeleton to reduce the risk of low-back injury during lifting is challenging. Computational models of the human-robot system coupled with predictive movement simulations can help to simplify this design process. Here, we present a study that models the interaction between a human model actuated by muscles and a lower-back exoskeleton. We provide a computational framework for identifying the spring parameters of the exoskeleton using an optimal control approach and forward-dynamics simulations. This is applied to generate dynamically consistent bending and lifting movements in t…
A Multi-Layer Sim-to-Real Framework for Gaze-Driven Assistive Neck Exoskeletons
Kahlert School of Computing, University of Utah（犹他大学计算机学院）；Dept. of Mechanical Engineering, University of Utah（犹他大学机械工程系）；Robotics Center, University of Utah（犹他大学机器人中心）
Department of Mechanical Engineering, Northeastern University（东北大学机械工程系）；College of Engineering, Northeastern University（东北大学工程学院）；Khoury College of Computer Sciences, Northeastern University（东北大学计算机科学学院）；Department of Electrical & Computer Engineering, Khoury College of Computer Sciences, and the Institute for Experiential Robotics, Northeastern University（计算机科学学院电子与计算机工程系及体验机器人研究所，东北大学）；Woodruff School of Mechanical Engineering and the Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（沃德夫机械工程学院及机器人与智能机器研究所，佐治亚理工学院）；Department of Mechanical Engineering, the Department of Physical Therapy, Movement, and Rehabilitation Science, and the Institute for Experiential Robotics, Northeastern University（机械工程系、物理治疗、运动与康复科学系及体验机器人研究所，东北大学）
Real-Time Decoding of Movement Onset and Offset for Brain-Controlled Rehabilitation Exoskeleton
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（麻省理工学院电气工程与计算机科学系）；Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校查兰电气与计算机工程系）；Department of Neurology, The University of Texas at Austin（德克萨斯大学奥斯汀分校神经病学系）；Walker Department of Mechanical Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校沃克机械工程系）；Meta Reality Labs Research, Redmond, WA, USA（微软现实实验室，华盛顿州雷德蒙德）
University of Heidelberg（海德堡大学）；University of Genoa（热那亚大学）；University Niccolò Cusano（尼可洛·库萨诺大学）
Bimanual object manipulation involves multiple visuo-haptic sensory feedbacks arising from the interaction with the environment that are managed from the central nervous system and consequently translated in motor commands. Kinematic strategies that occur during bimanual coupled tasks are still a scientific debate despite modern advances in haptics and robotics. Current technologies may have the potential to provide realistic scenarios involving the entire upper limb extremities during multi-joint movements but are not yet exploited to their full potential. The present study explores how hands…
National University of Singapore (NUS)（新加坡国立大学）；Advanced Robotics Center (ARC) at NUS（新加坡国立大学先进机器人中心）
The shoulder is responsible for the movement of the entire upper limb. It is capable of articulating in three degrees-of-freedom (DOF), enabling the arm to perform manipulation actions with incomparable dexterity. Exoskeletons targeting the shoulder must be able to emulate its complex kinematics. While typical architectures have proven to be useful, they employ complex and usually inadequate techniques to match the mobility of the shoulder. A new 2-DOF soft robotic shoulder exoskeleton is presented. Modular soft robotic actuators with separated inflation modules on the exoskeleton are able to …
Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning
Neuro AI and Robotics Group（神经人工智能与机器人组）；Cajal International Neuroscience Center（卡贾尔国际神经科学中心）；Spanish National Research Council（西班牙国家研究理事会）；Rey Juan Carlos University (URJC)（雷奥恩卡洛斯大学（URJC））
The City University of New York, City College（纽约市立大学城市学院）
Pediatric exoskeletons offer great promise to increase mobility for children with crouch gait caused by cerebral palsy. A lightweight, compliant and user-specific actuator is critical for maximizing the benefits of an exoskeleton to users. To date, pediatric exoskeletons generally use the same actuators as adult exoskeletons, which are heavy and resistive to natural movement. There is yet no easy way for robotic exoskeletons to accommodate the changes in design requirements that occur as a child ages. We developed a lightweight (1.65 kg unilateral mass) and compliant pediatric knee exoskeleton…
NC State University（北卡罗来纳州立大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Arizona State University（亚利桑那州立大学）；Hubei University of Technology（湖北工业大学）
Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human movement still is a major challenge. This paper aims at developing a novel data-driven solution framework based on reinforcement learning (RL), without first modeling the human-robot dynamics, to provide optimal and adaptive personalized torque assistance for reducing human efforts during walking. Our automatic personalization solution framework includes the assistive torque profile with two control timing parameters (pe…
To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can adaptively support large repertoires of movements. We propose a new human-machine interface to simultaneously drive bilateral ankle exoskeletons during a range of 'unseen' walking conditions and transitions that were not used for establishing the control interface. The proposed approach used person-specific neuromechanical models to estimate biological ankle joint torques in real-time from measured electromyograms (EMGS) and joint angles. A low-level controller based on a di…
Human-like motion is a primary goal for many robotic assistive devices. Emulating the strategy of the human neuromuscular system may aid the control of such powered devices, yet many challenges remain. In this study, we investigated the potential for using the winding filament model (WFM) of muscle to predict the net muscle moment of the ankle. The long-term goal is to use this model to improve ankle control of a commercial powered exoskeleton. The innovation aspects of this study are: First, there have been no commercialized active ankle exoskeletons available in the market. All the available…
