![](/style/images/tocco.png)
Sarah Sacco
Automatic classification of fragile subjects using sEMG signals recorded with REMO®.
Rel. Gabriella Olmo, Paolo Ariano. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
|
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview |
Abstract: |
The concept of fragility has proven to be a challenging one to define with precision. One of the most significant challenges in this regard is the lack of consensus concerning the precise nature of the phenomenon. Frailty is one of the leading causes of morbidity and premature mortality in older people. An international standard definition has yet to be established. A number of different measurement methods have been developed, including: The Fried frailty phenotype, the Tilburg Frailty Indicator (TFI) and the Frailty Index. The objective of this study is to analyse muscle activity in order to determine whether it is possible to detect frail subjects by means of simple forearm exercises. Indeed, surface electromyography (sEMG) may be employed as an additional method for evaluating frailty, as one of the defining characteristics of frailty is a reduced ability to perform exercise and motor activity. In particular, eight-channel surface electromyography was extracted from the forearm of elderly subjects using the wearable armband Recognition Movement (REMO®), developed by Morecognition s.r.l. The subjects were previously classified into two categories (frail/not frail) using the Fried frailty phenotype and Tilburg Frailty Indicator. The objective is to develop an automatic classifier to demonstrate the feasibility of identifying frailty through the use of sEMG, with a view to establishing an automatic measurement technique. |
---|---|
Relatori: | Gabriella Olmo, Paolo Ariano |
Anno accademico: | 2023/24 |
Tipo di pubblicazione: | Elettronica |
Numero di pagine: | 70 |
Soggetti: | |
Corso di laurea: | Corso di laurea magistrale in Ingegneria Biomedica |
Classe di laurea: | Nuovo ordinamento > Laurea magistrale > LM-21 - INGEGNERIA BIOMEDICA |
Aziende collaboratrici: | Morecognition Srl |
URI: | http://webthesis.biblio.polito.it/id/eprint/32182 |
![]() |
Modifica (riservato agli operatori) |