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EnglishWe propose a deep-learning method for feature extraction from gait data of Parkinson’s disease patients. Our goal is to verify whether a fine classification of gait between similar groups can be achieved. To this end, we refer as a case study to the Freezing of Gait (FOG), and we measure gait data from two groups of patients, which exhibit (respectively, do not exhibit) this symptom. Wearable inertial sensors are employed, and data are collected during activities similar to those performed by patients during their daily living. Moreover, most patients are in daily on state, hence the two groups are difficult to classify, as their gait does not exhibit evident differences. Whereas classical Machine Learning methods are not sufficiently robust to perform such a fine classification, if they are fed with features extracted by means of a deep network, the results are satisfactory also when a large dataset is not available and data present a mild degree of heterogeneity
Deep learning for Parkinson's disease: a case study on Freezing of Gait / Borzì, Luigi; Varrecchia, M; Olmo, G.. - ELETTRONICO. - (2019), pp. 1-4. ((Intervento presentato al convegno AIBEC 2019 - Austria International Biomedical Conference tenutosi a Vienna nel October 2019.
| Titolo: | Deep learning for Parkinson's disease: a case study on Freezing of Gait | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Abstract: | We propose a deep-learning method for feature extraction from gait data of Parkinson’s disease p...atients. Our goal is to verify whether a fine classification of gait between similar groups can be achieved. To this end, we refer as a case study to the Freezing of Gait (FOG), and we measure gait data from two groups of patients, which exhibit (respectively, do not exhibit) this symptom. Wearable inertial sensors are employed, and data are collected during activities similar to those performed by patients during their daily living. Moreover, most patients are in daily on state, hence the two groups are difficult to classify, as their gait does not exhibit evident differences. Whereas classical Machine Learning methods are not sufficiently robust to perform such a fine classification, if they are fed with features extracted by means of a deep network, the results are satisfactory also when a large dataset is not available and data present a mild degree of heterogeneity | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 2019-AIBEC Deep learning for PD.pdf | articolo principale | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
| 09280223.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2844434
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