基于多模态机器学习方法整合生物力学与肌电数据提升ACL重建术后再伤恐惧分类效能
简介
本研究纳入72例ACL重建术后患者（中位术后13个月），根据Tampa恐动症量表第9项分为高恐惧和低恐惧两组。受试者完成单腿标准反弹侧跳测试，同步采集三维运动捕捉、测力台和股部屈伸肌肌电数据。采用10种机器学习算法，分别基于单模态（运动学/动力学或肌电）和多模态融合数据进行分类。结果显示：融合运动学/动力学与肌电数据可提升分类准确率，极端梯度提升模型在融合数…
英文摘要
Fear of re-injury after anterior cruciate ligament (ACL) rupture often hinders return-to-sport and has been linked to movement patterns associated with increased injury risk. Identifying fearful individuals is thus critical. Self-reported questionnaires are commonly used but may be affected by underreporting in sporting and clinical contexts. We aimed to classify individuals with ACL reconstruction (ACLR) into HIGH-FEAR and LOW-FEAR groups, using machine learning (ML) models trained on unimodal or multimodal time-series movement data from side hop landings. Seventy-two participants (median: 13.0 months post-ACLR; interquartile range: 15.8 months) were dichotomized into HIGH-FEAR/LOW-FEAR groups using a single item (statement 9) from the Tampa Scale for Kinesiophobia. Participants performed one-leg standardized rebound side hops while kinematics and kinetics were recorded using 3D motion capture and force plates, respectively, and electromyography (EMG) was registered from flexor and extensor thigh muscles. We extracted time-series features from each modality and ten ML algorithms were trained and evaluated using leave-one-participant-out and grouped 3-fold cross-validation. Integrating kinematic/kinetic and EMG data improved classification accuracy compared to single modality datasets. The extreme gradient boosting model achieved the highest accuracy for fused data (86%) using the top 40 ranked features, including trunk tilt, pelvic obliquity, knee rotation, and flexor and extensor muscle activations. Kinematic/kinetic data alone achieved 83% accuracy per participant, while EMG data alone yielded 85%. This study demonstrates the potential of integrating different movement-related data to enhance the accuracy of ML models in classifying fear of re-injury post-ACLR, supporting identification of patterns associated with fear and guiding treatment.