Liew Bernard X W, Rügamer David, Mei Qichang, Altai Zainab, Zhu Xuqi, Zhai Xiaojun, Cortes Nelson
School of Sport, Rehabilitation, and Exercise Sciences, University of Essex, Colchester, United Kingdom.
Department of Statistics, Ludwig-Maximilians-Universität München, Munich, Germany.
Front Bioeng Biotechnol. 2023 Jul 3;11:1208711. doi: 10.3389/fbioe.2023.1208711. eCollection 2023.
Alterations in joint contact forces (JCFs) are thought to be important mechanisms for the onset and progression of many musculoskeletal and orthopaedic pain disorders. Computational approaches to JCFs assessment represent the only non-invasive means of estimating forces; but this cannot be undertaken in free-living environments. Here, we used deep neural networks to train models to predict JCFs, using only joint angles as predictors. Our neural network models were generally able to predict JCFs with errors within published minimal detectable change values. The errors ranged from the lowest value of 0.03 bodyweight (BW) (ankle medial-lateral JCF in walking) to a maximum of 0.65BW (knee VT JCF in running). Interestingly, we also found that over parametrised neural networks by training on longer epochs (>100) resulted in better and smoother waveform predictions. Our methods for predicting JCFs using only joint kinematics hold a lot of promise in allowing clinicians and coaches to continuously monitor tissue loading in free-living environments.
关节接触力（JCFs）的改变被认为是许多肌肉骨骼和骨科疼痛疾病发生和发展的重要机制。评估JCFs的计算方法是估计力的唯一非侵入性手段；但这无法在自由生活环境中进行。在这里，我们使用深度神经网络训练模型来预测JCFs，仅将关节角度作为预测因子。我们的神经网络模型通常能够预测JCFs，误差在已发表的最小可检测变化值范围内。误差范围从最低的0.03体重（BW）（步行时踝关节内外侧JCF）到最高的0.65BW（跑步时膝关节VT JCF）。有趣的是，我们还发现，通过在更长的轮次（>100）上进行训练，过度参数化的神经网络会产生更好、更平滑的波形预测。我们仅使用关节运动学来预测JCFs的方法在允许临床医生和教练在自由生活环境中持续监测组织负荷方面具有很大的前景。
