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Li X, Zeng H, Zhang J, Song A. Engagement Enhancement Based on Bayesian Optimization for Adaptive Assist-as-Needed Controller. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3118473] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Myoelectric control and neuromusculoskeletal modeling: Complementary technologies for rehabilitation robotics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Wang J, Wang W, Ren S, Shi W, Hou ZG. Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation. Front Neurorobot 2020; 14:596019. [PMID: 33304263 PMCID: PMC7693715 DOI: 10.3389/fnbot.2020.596019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 09/22/2020] [Indexed: 11/13/2022] Open
Abstract
Enhancing patients' engagement is of great benefit for neural rehabilitation. However, physiological and neurological differences among individuals can cause divergent responses to the same task, and the responses can further change considerably during training; both of these factors make engagement enhancement a challenge. This challenge can be overcome by training task optimization based on subjects' responses. To this end, an engagement enhancement method based on human-in-the-loop optimization is proposed in this paper. Firstly, an interactive speed-tracking riding game is designed as the training task in which four reference speed curves (RSCs) are designed to construct the reference trajectory in each generation. Each RSC is modeled using a piecewise function, which is determined by the starting velocity, transient time, and end velocity. Based on the parameterized model, the difficulty of the training task, which is a key factor affecting the engagement, can be optimized. Then, the objective function is designed with consideration to the tracking accuracy and the surface electromyogram (sEMG)-based muscle activation, and the physical and physiological responses of the subjects can consequently be evaluated simultaneously. Moreover, a covariance matrix adaption evolution strategy, which is relatively tolerant of both measurement noises and human adaptation, is used to generate the optimal parameters of the RSCs periodically. By optimization of the RSCs persistently, the objective function can be maximized, and the subjects' engagement can be enhanced. Finally, the performance of the proposed method is demonstrated by the validation and comparison experiments. The results show that both subjects' sEMG-based motor engagement and electroencephalography based neural engagement can be improved significantly and maintained at a high level.
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Affiliation(s)
- Jiaxing Wang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Weiqun Wang
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Shixin Ren
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Weiguo Shi
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zeng-Guang Hou
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Beijing, China
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Oliveira ACD, Rose CG, Warburton K, Ogden EM, Whitford B, Lee RK, Deshpande AD. Exploring the Capabilities of Harmony for Upper-Limb Stroke Therapy. IEEE Int Conf Rehabil Robot 2019; 2019:637-643. [PMID: 31374702 DOI: 10.1109/icorr.2019.8779558] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Harmony is a bimanual upper-limb exoskeleton designed for post-stroke rehabilitation. It moves the subject's shoulders and arms through their entire ranges of motion while maintaining natural coordination, is capable of force/torque control of each joint, and is equipped with sensors to measure motions and interaction forces. With these capabilities Harmony has the potential to assess motor function and create individualized therapy regimens. As a first step, five stroke survivors underwent rehabilitation sessions practicing multijoint movements with the device. Each participant performed a total of 1130 motions over seven hours of therapy with no adverse effects reported by participants or the attending therapist, supporting the suitability of Harmony for use in a clinical setting. Donning and doffing time averaged 3.5 minutes and decreased with therapist experience. Reported levels of stress, anxiety, and pain indicate that the Harmony safely assisted in the completion of the trained movements and has great potential to motivate and engage patients. We developed a novel methodology for assessing coordination capability and results from the study indicate that Harmony can enable therapists to identify neuromuscular weakness and maladaptive coordination patterns and develop targeted interventions to address these aspects of upper-limb function. The results suggest Harmony's feasibility and show promising improvements, motivating future study to gain statistical support.
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