Authors:
Antonio L. Alfeo
;
Mario G. C. A. Cimino
and
Gigliola Vaglini
Affiliation:
Università di Pisa, Italy
Keyword(s):
Elderly Monitoring, Smartwatch, Physical Activity, Stigmergy, Neural Receptive Field.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
Abstract:
Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capacity, and
prevent diseases. It is known that human annotations of PAL can be affected by subjectivity and inaccuracy.
Recently developed smart devices can allow a non-invasive, analytic, and continuous gathering of
physiological signals. We present an innovative computational system fed by signals of heartbeat rate, wrist
motion and pedometer sensed by a smartwatch. More specifically, samples of each signal are aggregated by
functional structures called trails. The trailing process is inspired by stigmergy, an insects’ coordination
mechanism, and is managed by computational units called stigmergic receptive fields (SRFs). SRFs, which
compute the similarity between trails, are arranged in a stigmergic perceptron to detect a collection of
micro-behaviours of the raw signal, called archetypes. A SRF is adaptive to subjects: its structural
parameters are tuned by a differential evolution algor
ithm. SRFs are used in a multilayer architecture,
providing further levels of processing to realize macro analyses in the application domain. As a result, the
architecture provides a daily PAL, useful to detect behavioural shift indicating initial signs of disease or
deviations in performance. As a proof of concept, the approach has been experimented on three subjects.
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