Clinical assessment of rigidity, a cardinal motor symptom of Parkinson’s disease (PD), is currently based on a manual task where the neurologist evaluates the subject’s resistance to a passive mobilization of a body segment around a joint and assigns a rating score accordingly. This measurement is subjective and highly dependent on the examiner’s experience. To achieve an objective method of rigidity evaluation in PD, a wearable system based on inertial and electromyographic sensors was developed to measure arm movement and muscular response during an experimental protocol characterized by four versions of flexion-extension exercises (i.e., active, passive, and passive with motor-motor or motor-cognitive dual tasks), performed on both elbow and wrist joints. The protocol was tested on 40 healthy participants, divided into two groups, based on age. Measured parameters after feature reduction were used as inputs for Support Vector Machine classifiers. Results showed the system can identify age-related differences in muscular response (average accuracy > 73% on wrist, and > 77% on elbow) comparing young adults against older adults. Also, the proposed system allowed discrimination of different exercise conditions. Muscular response differed in active condition compared to passive ones, including dual-task conditions (average F-score > 90% on wrist and > 72% on elbow both for young and older adults). Differently, it was not possible to discriminate among the three passive exercises. Finally, the system and related protocol could represent a promising solution for muscle rigidity evaluation even if further studies should be performed in clinical settings to investigate the actual muscular response of PD patients.
Study and Development of Novel Experimental Methods for Rigidity Assessment in Parkinson’s Disease Using sEMG and IMU Wearable Sensors / Francalanci, Beatrice; Rovini, Erika; Benvenuti, Pietro; Cavallo, Filippo. - ELETTRONICO. - (2024), pp. 136-152. (Intervento presentato al convegno 13th Italian Forum of Ambient Assisted Living, ForItAAL 2024 tenutosi a ita nel 2024) [10.1007/978-3-031-77318-1_10].
Study and Development of Novel Experimental Methods for Rigidity Assessment in Parkinson’s Disease Using sEMG and IMU Wearable Sensors
Francalanci, Beatrice;Rovini, Erika;Benvenuti, Pietro;Cavallo, Filippo
2024
Abstract
Clinical assessment of rigidity, a cardinal motor symptom of Parkinson’s disease (PD), is currently based on a manual task where the neurologist evaluates the subject’s resistance to a passive mobilization of a body segment around a joint and assigns a rating score accordingly. This measurement is subjective and highly dependent on the examiner’s experience. To achieve an objective method of rigidity evaluation in PD, a wearable system based on inertial and electromyographic sensors was developed to measure arm movement and muscular response during an experimental protocol characterized by four versions of flexion-extension exercises (i.e., active, passive, and passive with motor-motor or motor-cognitive dual tasks), performed on both elbow and wrist joints. The protocol was tested on 40 healthy participants, divided into two groups, based on age. Measured parameters after feature reduction were used as inputs for Support Vector Machine classifiers. Results showed the system can identify age-related differences in muscular response (average accuracy > 73% on wrist, and > 77% on elbow) comparing young adults against older adults. Also, the proposed system allowed discrimination of different exercise conditions. Muscular response differed in active condition compared to passive ones, including dual-task conditions (average F-score > 90% on wrist and > 72% on elbow both for young and older adults). Differently, it was not possible to discriminate among the three passive exercises. Finally, the system and related protocol could represent a promising solution for muscle rigidity evaluation even if further studies should be performed in clinical settings to investigate the actual muscular response of PD patients.File | Dimensione | Formato | |
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