Modelling and Validation of Wearable Sensor-based Gait Parameters in Parkinson's Disease Patients with Cognitive Impairment

Guo Hong^1,2,3Fengju Mao^1,2,3Mingming Zhang⁴Fei Zhang^5,6Xiangcheng Wang⁷Kang Ren^5,6Zhonglue Chen^5,6*Xiaoguang Luo^8*

• ¹Department of Neurology, Second Clinical Medicine College, Jinan University, Shenzhen, China
• ²Department of Neurology, Shenzhen People's Hospital, Jinan University, Shenzhen, Guangdong Province, China
• ³First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China
• ⁴Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
• ⁵GYENNO SCIENCE CO., LTD.,, Shenzhen, Guangdong, China
• ⁶HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, , Wuhan, Hubei 430074, China
• ⁷Department of Nuclear Medicine, Shenzhen People's Hospital （The Second Clinical Medical College，Jinan University；The First Affiliated Hospital, Southern University of Science and Technology）, Shenzhen, China
• ⁸Department of Neurology, Shenzhen People’s Hospital(The Second Clinical Medical College ,Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518000, China

Background:Cognitive impairment is a common non-motor symptom of PD that significantly impacts patients’ quality of life and disease progression.Despite its clinical importance,the underlying mechanisms linking motor and cognitive dysfunction in PD remain poorly understood.Wearable sensor technology offers an innovative approach to quantifying gait parameters and exploring their relationship with cognitive decline,providing a non-invasive,objective method to identify individuals at risk of cognitive impairment.Objective:This study aimed to develop and validate a diagnostic model using gait parameters derived from wearable sensors to predict cognitive impairment in PD patients.Additionally,it sought to integrate these findings with machine learning methods to enhance prediction accuracy.Methods:A cross-sectional study was conducted on early-to-mid-stage PD patients,with approximately 28.8% diagnosed with cognitive impairment.A total of 38 clinically relevant variables were collected,including demographic data,medical history,cognitive scale scores,and gait data captured by wearable sensors.Baseline comparisons,univariate,and multivariate logistic regression analyses were performed to identify independent risk factors for cognitive impairment.Selected variables were used to train and evaluate six machine-learning models.The models' predictive performance was comprehensively assessed using ROC curves,AUC values,DCA, PR curves,and forest plots.SHAP analysis was also employed to enable personalized risk assessment.Finally,correlations between cognitive scores and key gait parameters were analyzed.Results:Among the 38 clinical variables,seven were identified as independent risk factors for cognitive impairment in PD,including Duration of PD,UPDRS-III score,Step Length,Walk speed,Stride time,Peak arm angular velocity,Peak angular velocity during steering.The logistic regression model demonstrated superior predictive performance (test set AUC:0.957),outperforming other machine learning algorithms.SHAP analysis revealed that Step Length,UPDRS-III score,Duration of PD,and Peak angular velocity during steering were the most influential predictors in the logistic regression model.Additionally,correlation analysis showed a significant association between lower cognitive scores and deteriorating gait parameters.Conclusion:This study highlights the potential of gait parameters derived from wearable sensors as biomarkers for cognitive impairment in PD patients.It also underscores the intricate interplay between motor and cognitive dysfunction in PD.The integration of gait analysis with machine learning models,particularly logistic regression,provides a robust,non-invasive,and scalable approach for early identification and risk stratification of cognitive decline in PD.By leveraging wearable technology,this work paves the way for innovative diagnostic strategies to enhance clinical decision-making and improve patient outcomes.