Quantifying performance and joint kinematics in functional tasks crucial for anterior cruciate ligament rehabilitation using smartphone video and pose detection

IF 1.6 4区医学 Q3 ORTHOPEDICS

Knee Pub Date : 2024-11-26 DOI:10.1016/j.knee.2024.11.006

Nicolas Lambricht , Alexandre Englebert , Laurent Pitance , Paul Fisette , Christine Detrembleur

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引用次数: 0

Abstract

Background

The assessment of performance during functional tasks and the quality of movement execution are crucial metrics in the rehabilitation of patients with anterior cruciate ligament (ACL) injuries. While measuring performance is feasible in clinical practice, quantifying joint kinematics poses greater challenges. The aim of this study was to investigate whether smartphone video, using deep neural networks for human pose detection, can enable the clinicians not only to measure performance in functional tasks but also to assess joint kinematics.

Methods

Twelve healthy participants performed the forward reach of the Star Excursion Balance Test 10 times, along with 10 repetitions of forward jumps and vertical jumps, with simultaneous motion capture via a marker-based reference system and a smartphone. OpenPifPaf was utilized for markerless detection of anatomical landmarks in video recordings. The OpenPifPaf coordinates were scaled using anthropometric data of the thigh, and task performance and joint kinematics were computed for both the marker-based and markerless systems.

Results

Comparing results for marker-based and markerless systems revealed similar joint angles, with mean root mean square errors of 2.8° for the knee, 3.1° for the hip, and 3.9° for the ankle. Excellent agreement was observed for clinically pertinent parameters, i.e., the performance, the peak knee flexion, and the knee range of motion (intraclass correlation coefficient > 0.97).

Conclusion

The results underscore the feasibility of using markerless methods based on OpenPifPaf for assessing performance and joint kinematics in functional tasks crucial for ACL patients’ rehabilitation. The simplicity of this approach makes it suitable for integration into clinical practice.

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利用智能手机视频和姿势检测量化对前十字韧带康复至关重要的功能性任务中的表现和关节运动学特性

背景评估功能任务中的表现和动作执行的质量是前十字韧带（ACL）损伤患者康复的关键指标。虽然在临床实践中测量表现是可行的，但量化关节运动学却带来了更大的挑战。本研究的目的是探讨利用深度神经网络进行人体姿势检测的智能手机视频是否不仅能帮助临床医生测量功能性任务的表现，还能评估关节运动学。方法12名健康参与者进行了10次星际激增平衡测试的前伸动作，以及10次重复的前跳和垂直跳跃，并通过基于标记的参考系统和智能手机进行了同步动作捕捉。OpenPifPaf 用于在视频记录中无标记检测解剖地标。使用大腿的人体测量数据对 OpenPifPaf 坐标进行缩放，并计算基于标记系统和无标记系统的任务表现和关节运动学。临床相关参数，即运动表现、膝关节屈曲峰值和膝关节运动范围（类内相关系数为 0.97）的一致性非常好。这种方法简单易用，适合融入临床实践。

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

Knee 医学-外科

CiteScore

3.80

自引率

5.30%

发文量

171

审稿时长

6 months

期刊介绍： The Knee is an international journal publishing studies on the clinical treatment and fundamental biomechanical characteristics of this joint. The aim of the journal is to provide a vehicle relevant to surgeons, biomedical engineers, imaging specialists, materials scientists, rehabilitation personnel and all those with an interest in the knee. The topics covered include, but are not limited to: • Anatomy, physiology, morphology and biochemistry; • Biomechanical studies; • Advances in the development of prosthetic, orthotic and augmentation devices; • Imaging and diagnostic techniques; • Pathology; • Trauma; • Surgery; • Rehabilitation.