TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Biagetti, Giorgio
A1  - Crippa, Paolo
A1  - Falaschetti, Laura
A1  - Focante, Edoardo
A1  - Martínez Madrid, Natividad
A1  - Seepold, Ralf
A1  - Turchetti, Claudio
T1  - Machine Learning and Data Fusion Techniques Applied to Physical Activity Classification Using Photoplethysmographic and Accelerometric Signals
T2  - 24rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2020), 16 - 18 September 2020, Verona, Italy, virtual; (Procedia Computer Science)
N2  - 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.
KW  - Machine learning
KW  - Photoplethysmography
KW  - Data fusion
KW  - PPG
Y1  - 2020
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2020.09.178
DO  - https://doi.org/10.1016/j.procs.2020.09.178
VL  - 176
SP  - 3103
EP  - 3111
PB  - Elsevier
CY  - Amsterdam u.a.
ER  -