Does artificial intelligence feedback result in different kinematic and muscle excitation patterns compared to physiotherapist feedback during lower-limb rehabilitation exercises?

IF 1.4 3区医学 Q4 ENGINEERING, BIOMEDICAL

Clinical Biomechanics Pub Date : 2025-08-10 DOI:10.1016/j.clinbiomech.2025.106648

Devon Amos , Isobel Godfrey , Sam Tehranchi , Stuart Miller , Simon Lack

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引用次数: 0

Abstract

Background

Lower-limb rehabilitation exercises often require supervised feedback to ensure correct technique and muscle engagement. Artificial intelligence systems could provide an alternative to physiotherapy supervision, offering real-time feedback. This study aimed to compare effects of artificial intelligence-based feedback with physiotherapy feedback on kinematic and electromyographic outcomes during lower-limb exercises in healthy participants.

Methods

A repeated-measures design was employed, with uninjured participants performing four hip and knee exercises under physiotherapy and artificial intelligence (Merlin Ltd) feedback conditions. Kinematic data of the hip and knee were collected using an active infrared marker system. Muscle excitation was measured using surface electromyography for seven lower-limb muscles. Paired t-tests and two one-sided t-tests were used to assess differences and equivalence between conditions.

Findings

Among 11 participants (45 % females, mean age 24.1 years ±4.1, height 173.0 cm ± 9.2, mass 69.3 kg ± 13.7, Tegner Activity Scale 5.7 ± 1.4) no consistent significant differences were observed between physiotherapy and artificial intelligence feedback across exercises for kinematic and electromyographic outcomes. Equivalence in range of motion was observed for 58 % of all hip angles and 67 % of all knee angles; however, significant variability existed for minimum and maximum joint angles. Peak and root mean square amplitudes were mostly non-equivalent between conditions.

Interpretation

While artificial intelligence feedback demonstrated potential for guiding rehabilitation exercises, it lacked consistency with physiotherapy feedback for certain electromyographic and kinematic parameters due to limitations in evaluating multi-planar movements. Despite these limitations, artificial intelligence could serve as a supplementary tool, enhancing adherence and technique between physiotherapy sessions.

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在下肢康复训练中，与物理治疗师的反馈相比，人工智能反馈是否会导致不同的运动学和肌肉兴奋模式？

背景：下肢康复训练通常需要监督反馈，以确保正确的技术和肌肉参与。人工智能系统可以提供物理治疗监督的替代方案，提供实时反馈。本研究旨在比较基于人工智能的反馈与物理治疗反馈对健康参与者下肢运动时运动学和肌电图结果的影响。方法：采用重复测量设计，未受伤的参与者在物理治疗和人工智能（Merlin Ltd）反馈条件下进行四次髋关节和膝关节运动。使用主动红外标记系统收集髋关节和膝关节的运动学数据。用表面肌电图测量下肢7块肌肉的兴奋程度。使用配对t检验和两个单侧t检验来评估条件之间的差异和等效性。结果：在11名参与者中（45%为女性，平均年龄24.1岁±4.1岁，身高173.0 cm±9.2，体重69.3 kg±13.7，Tegner活动量表5.7±1.4），物理治疗和人工智能反馈在运动和肌电结果方面没有一致的显著差异。58%的髋关节角度和67%的膝关节角度的运动范围相等；然而，最小和最大关节角存在显著的变异性。峰值和均方根振幅在不同条件下大多不相等。解释：虽然人工智能反馈显示了指导康复训练的潜力，但由于评估多平面运动的局限性，它与某些肌电图和运动学参数的物理治疗反馈缺乏一致性。尽管存在这些限制，但人工智能可以作为辅助工具，在物理治疗期间增强依从性和技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Biomechanics 医学-工程：生物医学

CiteScore

3.30

自引率

5.60%

发文量

189

审稿时长

12.3 weeks

期刊介绍： Clinical Biomechanics is an international multidisciplinary journal of biomechanics with a focus on medical and clinical applications of new knowledge in the field. The science of biomechanics helps explain the causes of cell, tissue, organ and body system disorders, and supports clinicians in the diagnosis, prognosis and evaluation of treatment methods and technologies. Clinical Biomechanics aims to strengthen the links between laboratory and clinic by publishing cutting-edge biomechanics research which helps to explain the causes of injury and disease, and which provides evidence contributing to improved clinical management. A rigorous peer review system is employed and every attempt is made to process and publish top-quality papers promptly. Clinical Biomechanics explores all facets of body system, organ, tissue and cell biomechanics, with an emphasis on medical and clinical applications of the basic science aspects. The role of basic science is therefore recognized in a medical or clinical context. The readership of the journal closely reflects its multi-disciplinary contents, being a balance of scientists, engineers and clinicians. The contents are in the form of research papers, brief reports, review papers and correspondence, whilst special interest issues and supplements are published from time to time. Disciplines covered include biomechanics and mechanobiology at all scales, bioengineering and use of tissue engineering and biomaterials for clinical applications, biophysics, as well as biomechanical aspects of medical robotics, ergonomics, physical and occupational therapeutics and rehabilitation.