Objective: In order to evaluate Parkinson disease patients' response to therapeutic interventions, sources of information are mainly patient reports and clinicians' assessment of motor functions. However, these sources can suffer from patient's subjectivity and from inter/intra rater's score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment. Methods: Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification. Results: According to our findings, levodopa-based therapy alters the patient's conditions in general, ameliorating something (e.g., bradykinesia), leaving unchanged others (e.g., tremor), but with poor correlation to the levodopa dose. Conclusion: A technology-based approach can objectively assess levodopa-based therapy effectiveness. Significance: Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports.

Ricci, M., Lazzaro, G.d., Errico, V., Pisani, A., Giannini, F., Saggio, G. (2022). The Impact of Wearable Electronics in Assessing the Effectiveness of Levodopa Treatment in Parkinson's Disease. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022), Online [10.1109/JBHI.2022.3160103].

The Impact of Wearable Electronics in Assessing the Effectiveness of Levodopa Treatment in Parkinson's Disease

Pisani, Antonio;Giannini, Franco;Saggio, Giovanni
2022-07-01

Abstract

Objective: In order to evaluate Parkinson disease patients' response to therapeutic interventions, sources of information are mainly patient reports and clinicians' assessment of motor functions. However, these sources can suffer from patient's subjectivity and from inter/intra rater's score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment. Methods: Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification. Results: According to our findings, levodopa-based therapy alters the patient's conditions in general, ameliorating something (e.g., bradykinesia), leaving unchanged others (e.g., tremor), but with poor correlation to the levodopa dose. Conclusion: A technology-based approach can objectively assess levodopa-based therapy effectiveness. Significance: Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports.
International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022)
Online
2022
15
Rilevanza internazionale
contributo
2022
lug-2022
Settore ING-INF/01 - ELETTRONICA
English
Medical treatment; Task analysis; Wearable computers; Legged locomotion; Angular velocity; Turning; Diseases; Biomedical signal processing;
inertial sensors ; machine learning ; motion analysis ; Parkinson's disease ; wearable technology.
Intervento a convegno
Ricci, M., Lazzaro, G.d., Errico, V., Pisani, A., Giannini, F., Saggio, G. (2022). The Impact of Wearable Electronics in Assessing the Effectiveness of Levodopa Treatment in Parkinson's Disease. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022), Online [10.1109/JBHI.2022.3160103].
Ricci, M; Lazzaro, Gd; Errico, V; Pisani, A; Giannini, F; Saggio, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/320887
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