Real-Time Range of Motion Measurement of Physical Therapeutic Exercises

• 作者: John McInroy Lori Franc Mark McKenna Raghu Raj Prasanna Kumar Suresh Muknahallipatna
• 来源期刊: 电脑和通信(英文)
• 学科: 医学
• 关键词: COMPUTER VISION THERAPEUTIC EXERCISE GPU PARALLEL PROGRAMMING
• 年，卷（期）: 2017,（9）
• 页码范围: 19-42
• 页数: 24页
• 分类号: R73

摘要

Physical therapeutic exercise (PTE) is the planned process of performing bodily movements, postures, or physical activities to provide a patient with the ability to remediate or prevent impairments at a minimum. The efficacy of the PTE involves measuring accurately the range of motion (ROM) of joint functions and parameters that indicate the onset of fatigue, jerky motion, and muscle/joint resistance to the PTE. A physical therapist (PT) typically determines the efficacy of a PTE by measuring joint angles in clinical diagnosis to assess the ROM using the simple device Goniometer since motion capture systems are generally expensive, difficult to use, and currently not suited for real-time operations. The joint angle measurement using Goniometer suffers from low accuracy, low reliability and subjective. Furthermore, a patient when performing PTE by themselves at remote locations like their home or community centers cannot use a Goniometer to determine the efficacy. In this study, we present the approach of using an inexpensive, simple human motion capture system (HMCS) consisting of a single camera and a graphical processing unit (GPU) to perform the efficacy of the PTE in real-time. The approach involves the use of general purpose graphic processing unit (GPGPU) computer vision technique to track and record human motion and relate the tracked human motion to the prescribed physical therapy regimen in real-time. We have developed a tracking algorithm derived from the Klein’s algorithm known as the Modified Klein’s algorithm (MKA) capable of tracking human body parts while the original Klein’s algorithm was only capable of tracking objects with sharp edges. The MKA algorithm is further modified for parallel execution on a GPU to operate in real-time. Using the GPU, we are able to track multiple markers in a high definition (HD) frame of the HD video in 1.77 msecs achieving near real-time capability of ROM measurements. Furthermore, the error in the ROM measurements in comparison to Goniometer measurements is i