Volltext-Downloads (blau) und Frontdoor-Views (grau)

Machine Learning and Data Fusion Techniques Applied to Physical Activity Classification Using Photoplethysmographic and Accelerometric Signals

  • The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.

Export metadata

Additional Services

Share in Twitter Search Google Scholar


Author:Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Edoardo Focante, Natividad Martínez Madrid, Ralf Seepold, Claudio Turchetti
Parent Title (English):24rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2020), 16 - 18 September 2020, Verona, Italy, virtual; (Procedia Computer Science)
Document Type:Conference Proceeding
Year of Publication:2020
Release Date:2021/01/07
Tag:Data fusion; Machine learning; PPG; Photoplethysmography
First Page:3103
Last Page:3111
Institutes:Fakultät Informatik
Open Access?:Ja
Relevance:Peer reviewed Publikation in Liste der AG4
Licence (English):License LogoLizenzbedingungen Elsevier
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International