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Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classific...

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Detalles Bibliográficos
Autores principales: Biagetti, Giorgio, Crippa, Paolo, Falaschetti, Laura, Turchetti, Claudio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164365/
https://www.ncbi.nlm.nih.gov/pubmed/30158443
http://dx.doi.org/10.3390/s18092850
Descripción
Sumario:The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.