Ghaffari Arash, Clasen Pernille Damborg, Boel Rikke Vindberg, Kappel Andreas, Jakobsen Thomas, Rasmussen John, Kold Søren, Rahbek Ole
Interdisciplinary Orthopaedics, Aalborg University Hospital, Aalborg, Denmark.
Department of Materials and Production, Aalborg University, Aalborg East, Denmark.
Heliyon. 2024 Aug 23;10(17):e36825. doi: 10.1016/j.heliyon.2024.e36825. eCollection 2024 Sep 15.
Hip and knee osteoarthritis (OA) patients demonstrate distinct gait patterns, yet detecting subtle abnormalities with wearable sensors remains uncertain. This study aimed to assess a predictive model's efficacy in distinguishing between hip and knee OA gait patterns using accelerometer data.
Participants with hip or knee OA underwent overground walking assessments, recording lower limb accelerations for subsequent time and frequency domain analyses. Logistic regression with regularization identified associations between frequency domain features of acceleration signals and OA, and k-nearest neighbor classification distinguished knee and hip OA based on selected acceleration signal features.
We included 57 knee OA patients (30 females, median age 68 [range 49-89], median BMI 29.7 [range 21.0-45.9]) and 42 hip OA patients (19 females, median age 70 [range 47-89], median BMI 28.3 [range 20.4-37.2]). No significant difference could be found in the time domain's averaged shape of acceleration signals. However, in the frequency domain, five selected features showed a diagnostic ability to differentiate between knee and hip OA. Using these features, a model achieved a 77 % accuracy in classifying gait cycles into hip or knee OA groups, with average precision, recall, and F1 score of 77 %, 76 %, and 78 %, respectively.
The study demonstrates the effectiveness of wearable sensors in differentiating gait patterns between individuals with hip and knee OA, specifically in the frequency domain. The results highlights the promising potential of wearable sensors and advanced signal processing techniques for objective assessment of OA in clinical settings.
髋部和膝部骨关节炎（OA）患者表现出独特的步态模式，但使用可穿戴传感器检测细微异常仍不确定。本研究旨在评估一种预测模型在利用加速度计数据区分髋部和膝部OA步态模式方面的有效性。
髋部或膝部OA患者进行地面行走评估，记录下肢加速度以进行后续的时域和频域分析。带正则化的逻辑回归确定了加速度信号的频域特征与OA之间的关联，k近邻分类基于选定的加速度信号特征区分膝部和髋部OA。
我们纳入了57名膝部OA患者（30名女性，中位年龄68岁[范围49 - 89岁]，中位BMI 29.7[范围21.0 - 45.9]）和42名髋部OA患者（19名女性，中位年龄70岁[范围47 - 89岁]，中位BMI 28.3[范围20.4 - 37.2]）。在加速度信号的时域平均形状方面未发现显著差异。然而，在频域中，五个选定特征显示出区分膝部和髋部OA的诊断能力。使用这些特征，一个模型在将步态周期分类为髋部或膝部OA组时达到了77%的准确率，平均精确率、召回率和F1分数分别为77%、76%和78%。
该研究证明了可穿戴传感器在区分髋部和膝部OA个体的步态模式方面的有效性，特别是在频域中。结果突出了可穿戴传感器和先进信号处理技术在临床环境中对OA进行客观评估的广阔前景。
