Objective assessment of gait and posture symptoms in Parkinson's disease using wearable sensors and machine learning.

Research overview

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ObjectiveGait and posture symptoms-such as gait impairments, postural instability, and posture deformations-are common in Parkinson's disease (PD) and closely linked to falls. Traditional assessments using clinical scales are time-consuming and prone to subjective bias. This study aims to predict the severity of gait and posture symptoms using data collected from wearable sensors during a single laboratory-based walking assessment, providing an objective, efficient, and automated evaluation approach.MethodsSensor-based gait parameters were collected from 225 PD participants (mean age 63.15 ± 10.46 years) through a standardized walking assessment. The dataset was randomly split into a training set (80%) and an independent test set (20%) with balanced age, sex, and PD duration. Two machine learning models-extreme gradient boosting (XGBoost) and support vector machine (SVM)-were trained to predict scores for five gait and posture items (#3.9-3.13) from the MDS-UPDRS III.ResultsXGBoost was chosen as the final model due to its better performance than SVM. Across all five gait and posture items, the models achieved over 80% acceptable accuracy. For items #3.9-#3.11, absolute accuracy surpassed 70%, and macro-F1 scores were above 0.60 in leave-one-out cross-validation (LOOCV). The model's performance on the independent test set matched LOOCV results, confirming robustness. A total of 35, 35, 30, 30, and 40 gait features were selected for the predictive models of items #3.9-#3.13, respectively. Among these, key features with significant clinical relevance were identified. For example, Effective Trial Duration (R = 0.522, p Shank-Swing RoM-mean (max) (R = -0.629, p 180° Turn-Steps - mean (R = 0.482, p ConclusionThis study demonstrates the feasibility of using wearable sensor data to objectively assess gait and posture symptoms in PD. Though conducted in a clinical setting, the approach may support clinicians through consistent assessments and more frequent monitoring, with potential for future home-based use to enable longitudinal symptom tracking.

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