Abstract：
Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains.
Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used.
Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies.
The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.
Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used.
Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies.
The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.
摘要：
背景：尽管存在解决生物力学缺陷的循证康复策略，在70%的急性踝关节扭伤患者中,踝关节问题的复发持续存在,突出了这一问题的未解决性质.人工智能（AI）成为一种有前途的工具，可以确定踝关节扭伤的确切预测因素。本文旨在总结AI在研究健康和踝关节扭伤受试者的踝关节生物力学中的应用。
方法：从五个电子数据库中检索了2010年至2023年之间发表的文章。包括59篇论文进行分析，涉及：i)。测试的运动类型(功能与有目的的踝关节运动）；ii）测量的生物力学参数类型（动力学与运动学）；iii）使用的传感器系统类型（基于实验室的与基于场的）；以及，iv)使用的人工智能技术。
结果：大多数研究（83.1％）检查了功能运动过程中的生物力学。单一运动学参数,特别是脚踝的活动范围，在识别伤害状态时，可以获得高达100%的准确率。可穿戴传感器在实验室和现场/临床设置中都表现出高可靠性。AI算法主要利用肌电图和关节角度信息作为输入数据。支持向量机是最常用的监督学习算法(18.64%)，而人工神经网络在8项研究中表现出最高的准确性。
结论：采用基于现场的设备，远程患者监护的潜力是显而易见的。然而，基于AI的传感器在检测扭伤风险的脚踝运动方面未得到充分利用。我们确定了三个关键挑战：传感器设计，人工智能模型的可控性，以及人工智能传感器模型的集成，为未来的研究提供有价值的见解。
方法：从五个电子数据库中检索了2010年至2023年之间发表的文章。包括59篇论文进行分析，涉及：i)。测试的运动类型(功能与有目的的踝关节运动）；ii）测量的生物力学参数类型（动力学与运动学）；iii）使用的传感器系统类型（基于实验室的与基于场的）；以及，iv)使用的人工智能技术。
结果：大多数研究（83.1％）检查了功能运动过程中的生物力学。单一运动学参数,特别是脚踝的活动范围，在识别伤害状态时，可以获得高达100%的准确率。可穿戴传感器在实验室和现场/临床设置中都表现出高可靠性。AI算法主要利用肌电图和关节角度信息作为输入数据。支持向量机是最常用的监督学习算法(18.64%)，而人工神经网络在8项研究中表现出最高的准确性。
结论：采用基于现场的设备，远程患者监护的潜力是显而易见的。然而，基于AI的传感器在检测扭伤风险的脚踝运动方面未得到充分利用。我们确定了三个关键挑战：传感器设计，人工智能模型的可控性，以及人工智能传感器模型的集成，为未来的研究提供有价值的见解。
