Ota Megumi
Faculty of Health Sciences, Kyorin University, Japan.
Phys Ther Res. 2025;28(2):77-84. doi: 10.1298/ptr.R0037. Epub 2025 Jul 3.
Motion analysis is essential for physical therapists and athletic trainers to understand the motor function of their patients or athletes. Although marker-based motion analysis systems have been widely utilized in research, they are expensive and demand significant time and effort for measurement and analysis, which can complicate their application in clinical practice. In recent years, markerless motion analysis technologies have emerged as affordable and portable alternatives. These technologies include inertial measurement unit (IMU) sensors, depth cameras, manual digitization, and posture-tracking algorithms. IMU sensors detect motion using accelerometers and gyro sensors and can be worn on body parts. Depth cameras use infrared or laser technology to capture three-dimensional (3D) motion without requiring markers. Manual digitization enables semiautomatic identification of joint positions from images, allowing joint angle measurement without using specific cameras or markers. Posture-tracking algorithms use artificial intelligence to approximate joint positions from standard camera images, enabling automated motion analysis. Despite the enhanced accessibility of these technologies, limitations remain, particularly in analyzing detailed joint movements or individuals with structural abnormalities, and their accuracy depends on the environment and motion task. However, with further development, these technologies are expected to become increasingly reliable and provide physical therapists and athletic trainers with valuable, cost-effective, and easy-to-use tools for assessing movement in clinical and sports settings.
运动分析对于物理治疗师和运动训练师了解其患者或运动员的运动功能至关重要。尽管基于标记的运动分析系统已在研究中广泛使用,但它们价格昂贵,且测量和分析需要大量时间和精力,这可能使其在临床实践中的应用变得复杂。近年来,无标记运动分析技术已成为经济实惠且便于携带的替代方案。这些技术包括惯性测量单元(IMU)传感器、深度相机、手动数字化和姿势跟踪算法。IMU传感器利用加速度计和陀螺仪传感器检测运动,可佩戴在身体部位。深度相机使用红外或激光技术来捕捉三维(3D)运动,无需标记。手动数字化可从图像中半自动识别关节位置,无需使用特定相机或标记即可进行关节角度测量。姿势跟踪算法利用人工智能从标准相机图像中估算关节位置,实现自动运动分析。尽管这些技术的可及性有所提高,但仍存在局限性,尤其是在分析详细的关节运动或有结构异常的个体时,其准确性取决于环境和运动任务。然而,随着进一步发展,这些技术有望变得越来越可靠,并为物理治疗师和运动训练师提供有价值、经济高效且易于使用的工具,用于在临床和运动环境中评估运动。