Telepresence for Physiotherapy

物理治疗的远程呈现是移动计算医疗保健中最近显著增长的一个领域。应用程序通常使用深度相机来估计用户的身体姿势，因此机器智能可以评估并提供与医疗相关的反馈，以实现医疗结果。Sheridan College和Lusens（一家加拿大商业、零售和医疗应用的互动、触摸和手势识别软件的领导者）寻求开展合作应用研究和开发（R&D），专注于改进他们现有的gphysio产品。gphysio使用专有技术来分析和测量肌肉骨骼的位置和运动，为物理治疗、康复和健身提供远程帮助。拟议的研究将侧重于通过机器学习为深度和RGB相机开发身体姿势估计和评分的创新解决方案。将身体姿势估计作为软件评估、批评和评分患者运动和手势的一种手段，需要专门的相机到骨骼计算、运动分析算法和人工智能来分析运动以达到预测目的。Lusens的目标是将研究成果应用于其行业领先的身体姿态估计软件的开发中，参与拟议项目的谢里登计算机科学专业的学生将受益于体验式学习和应用于现实世界创新挑战的相关技能的实践。

Telepresence for physiotherapy is an area of significant recent growth within healthcare for mobile computing. Applications typically use depth cameras to estimate the body pose of the user, so machine intelligence can assess and provide medically relevant feedback to achieve medical outcomes. Sheridan College and Lusens - a Canadian leader in interactive, touch, and gesture recognition software for commercial, retail and medical applications - seek to conduct collaborative applied research and development (R&D) focused on improving their existing GoPhysio product. GoPhysio uses proprietary technology to analyze and measure musculoskeletal positions and movements to provide remote assistance for physical therapy, rehabilitation and fitness. The proposed research will focus on development of innovative solutions for body pose estimation and scoring for both depth and RGB cameras via Machine Learning. The application of body pose estimation as a means for software to assess, critique and score patient movements and gestures in software requires specialized camera-to-skeleton calculations, algorithms for analyzing motion, and Artificial Intelligence to analyze the movements for predictive purposes. Lusens aims to apply the research results in the development of their industry-leading body pose estimation software and Sheridan Computer Science students engaged in the proposed project will benefit from experiential learning and practice of relevant skills applied to real-world innovation challenges.