Roggio Federico, Trovato Bruno, Sortino Martina, Musumeci Giuseppe
Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Catania, Italy.
Research Center on Motor Activities (CRAM), University of Catania, Catania, Italy.
Heliyon. 2024 Oct 30;10(21):e39977. doi: 10.1016/j.heliyon.2024.e39977. eCollection 2024 Nov 15.
The accurate measurement and analysis of human movement are essential in fields ranging from rehabilitation and neuroscience to sports science and ergonomics. Traditional methods, though precise, are often constrained by cost, accessibility, and controlled environments. The advent of machine learning (ML) pose estimation models (PEMs) offers an alternative solution, enabling detailed motion analysis using low-cost imaging systems in various settings. The aim of this review is to evaluate ML PEMs and their impact on human movement sciences, focusing on recent advancements in machine learning and computer vision for accurate, non-invasive motion analysis using low-cost imaging systems. A narrative review was conducted by searching electronic databases, including PubMed and Google Scholar, using key terms such as "machine learning," "pose estimation models," and "human movement sciences." Thematic analysis identified key advancements, applications, and challenges in ML PEMs across clinical, sports, and ergonomic contexts. The review highlights the development, capabilities, and applications of models such as OpenPose, PoseNet, AlphaPose, DeepLabCut, HRNet, MediaPipe Pose, BlazePose, EfficientPose, and MoveNet, emphasizing their potential for non-invasive, cost-effective assessments. In clinical settings, these models enable objective gait and posture analysis, aiding in diagnosing musculoskeletal disorders and tracking rehabilitation progress. In sports, ML PEMs enhance performance analysis and injury prevention by providing real-time feedback and detailed biomechanical data. In ergonomics, they offer proactive solutions for workplace injury prevention through real-time posture and movement analysis. While promising, the implementation of ML PEMs faces challenges in accuracy, data quality, and integration into existing practices. Establishing standardized protocols and frameworks is crucial for ensuring reliable, interdisciplinary applications. This review can be useful for coaches, healthcare professionals, and researchers in evaluating and implementing ML PEMs, ultimately advancing the field of human movement sciences.
人体运动的精确测量和分析在从康复、神经科学到运动科学和人体工程学等诸多领域都至关重要。传统方法虽然精确，但往往受到成本、可及性和环境控制的限制。机器学习（ML）姿态估计模型（PEM）的出现提供了一种替代解决方案，能够在各种环境中使用低成本成像系统进行详细的运动分析。本综述的目的是评估ML PEM及其对人体运动科学的影响，重点关注机器学习和计算机视觉在使用低成本成像系统进行精确、非侵入性运动分析方面的最新进展。通过搜索包括PubMed和谷歌学术在内的电子数据库，使用“机器学习”、“姿态估计模型”和“人体运动科学”等关键词进行了叙述性综述。主题分析确定了ML PEM在临床、运动和人体工程学背景下的关键进展、应用和挑战。该综述突出了OpenPose、PoseNet、AlphaPose、DeepLabCut、HRNet、MediaPipe Pose、BlazePose、EfficientPose和MoveNet等模型的发展、能力和应用，强调了它们在非侵入性、经济高效评估方面的潜力。在临床环境中，这些模型能够进行客观的步态和姿势分析，有助于诊断肌肉骨骼疾病和跟踪康复进展。在体育领域，ML PEM通过提供实时反馈和详细的生物力学数据来增强性能分析和预防损伤。在人体工程学中，它们通过实时姿势和运动分析为预防工作场所损伤提供主动解决方案。尽管前景广阔，但ML PEM的实施在准确性、数据质量以及与现有实践的整合方面面临挑战。建立标准化协议和框架对于确保可靠的跨学科应用至关重要。本综述对于教练、医疗保健专业人员和研究人员评估和实施ML PEM可能有用，最终推动人体运动科学领域的发展。
