caa a22 4500 605468907 CHVBK 20210128100319.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1499-y doi (NATIONALLICENCE)springer-10.1007/s00500-014-1499-y Unsupervised template discovery in activity recognition using the Gamma Growing Neural Gas algorithm [Elektronische Daten] [Héctor Satizábal, Andres Perez-Uribe] Activity recognition is gaining a lot of interest given its direct use in applications like ambient assisted living and has been empowered by the increasing ubiquity of sensors (e.g., clothes, smartphones, watches). The machine learning approach to activity recognition consists on finding the signatures characterizing the activities to be recognized, with the hope of identifying them (pattern matching) within the stream of sensor data. The finding of those signatures can be very complex, thus many approaches deal with the streams of sensor data by segmenting them into sections or "time-windows”, before processing them by a feature extraction procedure. The problem then concerns the association of features to class labels. In this paper, we propose the use of the Gamma Growing Neural Gas algorithm to unsupervisely discover templates in a recording containing gestures performed by a person in a home environment. The system is able to do vector quantization from the time-series of data coming from one accelerometer, and finds salient patterns (e.g., templates) in the signal. These templates integrate information not only from single time-windows but do consider the recent history of the incoming signal (e.g., multiple time-windows). Those templates are then associated to activity classes by supervised learning. Our experiments show that the resulting performance is better than previous benchmarks of the same database. Springer-Verlag Berlin Heidelberg, 2014 Activity recognition nationallicence Growing Neural Gas nationallicence Time series nationallicence Template matching nationallicence Satizábal Héctor IICT, HEIG-VD, University of Applied Sciences Western Switzerland (HES-SO), Delémont, Switzerland aut Perez-Uribe Andres IICT, HEIG-VD, University of Applied Sciences Western Switzerland (HES-SO), Delémont, Switzerland aut Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2435-2445 1432-7643 19:9<2435 2015 19 500 https://doi.org/10.1007/s00500-014-1499-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1499-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Satizábal Héctor IICT, HEIG-VD, University of Applied Sciences Western Switzerland (HES-SO), Delémont, Switzerland aut NATIONALLICENCE 700 1- Perez-Uribe Andres IICT, HEIG-VD, University of Applied Sciences Western Switzerland (HES-SO), Delémont, Switzerland aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2435-2445 1432-7643 19:9<2435 2015 19 500