Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields

• Main Authors: Alfeo, A. L, Cimino, M. G. C. A, Vaglini, G
• Format: Journal Article
• Language: English
• Published: 02-01-2019
• Subjects: Computer Science - Computers and Society; Computer Science - Human-Computer Interaction
• DOI: 10.48550/arxiv.1901.00552

INSTICC The 6th International Conference on Pattern Recognition
Applications and Methods (ICPRAM 2017), pp. 724-730, Porto, Portugal, 24-26
February 2016 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 algorithm. 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.