Motivation to perform movement therapy and caregiver burnout are major challenges to post-stroke life. Serious games have been shown to support therapeutic tasks in people with stroke, but there are few activities that simultaneously support informal caregiver health, which is also impacted post-stroke. Here, we present a collaborative, mutually beneficial, serious game designed to support therapy for persons with stroke and also exercise for their informal caregivers. One player performs rehabilitative wrist movements - useful to people with stroke - and the other performs a seated march exercise - useful to informal caregivers - via pedals or a keyboard to control their avatar. We present a pilot study with 6 healthy dyads to evaluate how exercise-based input of one player, the Pseudo Caregiver (PCG), impacts motivation and emotional experience in both the PCG and Pseudo Person with Stroke (PPS). While not statistically significant, we find that PCGs Interest subscale scores trended higher when using a pedal (the exercised-based input) compared to a keyboard, regardless of game play mode. PPSs' positive affect scale scores and Competence subscale scores trended higher when their partner played collaboratively with a pedal compared to a keyboard. These trends encourage future work toward incorporating an exercise-based device, such as a pedal, to enhance the emotional and motivational experience of rehabilitative serious games for people with different movement ability levels.
Validation of Consumer-grade Digital Camera-based Human Activity Evaluation for Upper Limb Exercises and Development of a Therapist-guided, Automated Telerehabilitation Framework and Platform for Stroke Rehabilitation
Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
Carnegie Mellon University（卡内基梅隆大学）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等理工学院）；Madeira Interactive Technology Institute, University of Madeira（马德拉大学马德拉交互技术研究所）
OGA-AID: Clinician-in-the-loop AI Report Drafting Assistant for Multimodal Observational Gait Analysis in Post-Stroke Rehabilitation
Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore（新加坡康复研究中心，南洋理工大学，新加坡）；Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore（李光前医学院，南洋理工大学，新加坡）；The Grainger College of Engineering, University of Illinois Urbana-Champaign, United States（伊利诺伊大学厄巴纳-香槟分校格雷格学院，美国）；Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong（香港理工大学康复科学系，香港）；VinUni-Illinois Smart Health Center, VinUniversity, Vietnam（Vin大学Vin-伊利诺伊智能健康中心，越南）；Institute of Rehabilitation Excellence, Tan Tock Seng Hospital, NHG Health, Singapore（卓越康复研究所，坦托克桑格医院，NHG健康，新加坡）
A Soft High Force Hand Exoskeleton for Rehabilitation and Assistance of Spinal Cord Injury and Stroke Individuals
Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Grove School of Engineering（格罗夫工程学院）；City College of New York（纽约城市学院）；Moss Rehabilitation Research Institute（莫斯康复研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches
Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones（通信技术发展与创新大学研究所）；Universidad de Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）
Development of Rehabilitation System (ReHabgame) through Monte-Carlo Tree Search Algorithm
计算智能在计算机游戏中扮演着重要的角色，它可以模拟现实生活中的各种问题。实时决策游戏中的CI可以为树搜索算法的检验提供一个平台。在本文中，我们提出了一个康复严重的游戏（ReHabgame）中，蒙特卡罗树搜索（MCTS）算法的使用。该游戏旨在对抗中风后/脑损伤伤员的身体障碍，以改善上肢运动。通过ReHabgame的过程，玩家根据他/她的移动能力通过上肢选择路径到达虚拟目标对象。系统通过MCTS根据玩家的活动质量调整游戏难度。它从玩家所做的动作中学习，并生成更多的后续对象以供收集。该系统收集方向、肌肉和关节活动数据，并利用它们做出决策。玩家数据通过Kinect Xbox One和Myo Armband收集。结果表明，MCTS在ReHabgame中的有效性，从高度可实现的路径进展到较难实现的路径，从而配置和个性化康复过程。
An AI-Driven Multimodal Smart Home Platform for Continuous Monitoring and Assistance in Post-Stroke Motor Impairment
University of Cambridge（剑桥大学）；Beihang University（北京航空航天大学）；KAUST（阿卜杜拉国王科技大学）；Beijing New Guoxin Software Evaluation Technology Co ltd（北京新国信软件评测技术有限公司）；Beijing Tsinghua Changgung Hospital, Tsinghua University（清华大学北京清华长庚医院）；University College London（伦敦大学学院）；Beijing Tongren Hospital, Capital Medical University（首都医科大学附属北京同仁医院）
