Telepresence for Physiotherapy

物理治疗的网真是移动计算医疗保健领域最近显著增长的一个领域。应用程序通常使用深度摄像头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学相关的反馈，以实现医疗结果。Sheridan College和Lusens-加拿大商业、零售和医疗应用交互、触摸和手势识别软件的领先者-寻求进行协作式应用研究和开发(R&D)，专注于改进他们现有的GoPhysio产品。GoPhysio使用专有技术分析和测量肌肉骨骼的位置和运动，为理疗、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。应用身体姿势估计作为软件在软件中评估、评论和评分患者的动作和手势的手段，需要专门的从相机到骨骼的计算、用于分析运动的算法，以及用于分析动作以进行预测的人工智能。卢森斯的目标是将研究成果应用于他们行业领先的身体姿势估计软件的开发中，谢里丹计算机科学专业的学生将从经验学习和实践中受益，并将相关技能应用于现实世界的创新挑战。

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.