Telepresence for Physiotherapy

用于物理治疗的远程呈现是医疗保健领域中用于移动的计算的最近显著增长的领域。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医学相关的反馈，以实现医疗结果。谢里丹学院和Lusens -一个加拿大的领导者在互动，触摸和手势识别软件的商业，零售和医疗应用-寻求进行合作应用研究和开发（R&D），重点是改善他们现有的GoPhysio产品。GoPhysio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。拟议的研究将专注于通过机器学习开发身体姿势估计和深度和RGB相机评分的创新解决方案。身体姿势估计作为软件评估、评论和评分患者运动和姿势的手段的应用需要专门的相机到骨架计算、用于分析运动的算法以及用于分析运动以用于预测目的的人工智能。Lusens旨在将研究成果应用于其行业领先的身体姿势估计软件的开发，而参与该项目的谢里丹计算机科学专业的学生将从体验式学习和应用于现实世界创新挑战的相关技能实践中受益。

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.