IEEE journal of translational engineering in health and medicine · 2026
Analyzing Gait Pattern Associated With Neuropsychiatric Symptoms in Parkinson's Disease by a Comprehensive Approach.
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Russo Michela, Ricciardi Carlo, Mestizia Martina, Filippo Federico Di, Pisani Noemi, de Biasi Giuseppe, Ponsiglione Alfonso M, Barone Paolo, Amboni Marianna, Romano Maria
Аннотация
OBJECTIVE: Parkinson's disease (PD) is characterized by motor and non-motor symptoms. Among non-motor symptoms, neuropsychiatric symptoms (NPS), including depression, anxiety, cognitive decline and psychosis, are common and affect quality of life. The present study investigates gait patterns in PD patients with and without NPS, focusing on kinematic, kinetic, and spatio-temporal variables extracted from gait analysis. Notably, the assessments were carried out in a real-world outpatient clinical environment, which strengthens the translational value of the findings.
METHOD: Overall, 104 PD patients were assessed using an optoelectronic system to record spatio-temporal parameters, kinematic and kinetic signals during a single task and two different dual tasks. First, a preliminary statistical analysis was carried out and then, several machine learning algorithms were applied to classify patients with and without NPS based on gait data.
RESULTS: Statistical analyses revealed that PD patients with NPS had slower gait, greater instability and reduced movement control. Decision Tree and Support Vector Machine classifiers showed promising results, particularly for kinematic and kinetic features (with an accuracy of 91.3% and 80.3%, respectively). The most recurrent features on which the classifiers were trained almost entirely matched the significance found by statistical analyses.
CONCLUSION: The present findings further expand the relationship between motor and non-motor symptoms in PD highlighting that the relationship between gait and mental symptoms may extend beyond cognitive status. Clinical Impact-The study highlights the value of gait analysis as a promising, non-invasive surrogate biomarker for the early identification of a distinct clinical phenotype characterized by an increased burden of neuropsychiatric symptoms in Parkinson's disease. The high accuracy achieved by machine learning models trained on gait features for distinguishing Parkinsonian patients with and without neuropsychiatric symptoms supports the development of assistive diagnostic tools that can enhance clinical decision-making, reduce subjectivity in patient assessments, and enable personalized monitoring of disease progression. Since the analyses were carried out in a real-world outpatient setting, the proposed approach naturally lends itself to integration within multidisciplinary clinical workflows, fostering synergistic collaboration among neurologists, physiotherapists and biomedical engineers, and contributing to a more personalized, precise, and proactive model of care.
METHOD: Overall, 104 PD patients were assessed using an optoelectronic system to record spatio-temporal parameters, kinematic and kinetic signals during a single task and two different dual tasks. First, a preliminary statistical analysis was carried out and then, several machine learning algorithms were applied to classify patients with and without NPS based on gait data.
RESULTS: Statistical analyses revealed that PD patients with NPS had slower gait, greater instability and reduced movement control. Decision Tree and Support Vector Machine classifiers showed promising results, particularly for kinematic and kinetic features (with an accuracy of 91.3% and 80.3%, respectively). The most recurrent features on which the classifiers were trained almost entirely matched the significance found by statistical analyses.
CONCLUSION: The present findings further expand the relationship between motor and non-motor symptoms in PD highlighting that the relationship between gait and mental symptoms may extend beyond cognitive status. Clinical Impact-The study highlights the value of gait analysis as a promising, non-invasive surrogate biomarker for the early identification of a distinct clinical phenotype characterized by an increased burden of neuropsychiatric symptoms in Parkinson's disease. The high accuracy achieved by machine learning models trained on gait features for distinguishing Parkinsonian patients with and without neuropsychiatric symptoms supports the development of assistive diagnostic tools that can enhance clinical decision-making, reduce subjectivity in patient assessments, and enable personalized monitoring of disease progression. Since the analyses were carried out in a real-world outpatient setting, the proposed approach naturally lends itself to integration within multidisciplinary clinical workflows, fostering synergistic collaboration among neurologists, physiotherapists and biomedical engineers, and contributing to a more personalized, precise, and proactive model of care.