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

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 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

Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review

“…Alfeo et al [ 50 ] presented a measuring device for the physical activity of older adults. The inputs were heart rate, wrist motion and pedometer, all of which were sensed by a wrist-wearable device.…”

“…sports (recreative or professional) and lifestyle—monitoring health parameters and training activities (as well as environmental parameters) to prevent potential injuries, to reach optimal fitness, help change lifestyle, assess sleep quality, and so on [ 10 , 12 , 15 , 16 , 17 , 27 , 28 , 29 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ].…”

Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases

“…Wearable sensor-based systems have been proposed as a convenient tool to monitor gait and other activities in uncontrolled environments [ 21 , 22 , 23 , 24 ], so as to enable pervasive healthcare [ 25 ]. In particular, researchers have explored the use of wearable accelerometers to reliably detect foot contact times within gait cycles and estimate spatio-temporal gait parameters.…”

Using Stigmergy to Incorporate the Time into Artificial Neural Networks

“…Assuming = −∞, and = ∞, Formula (7) and Formula (8) Fig. 5a shows a representation of y(0) and y(1), according to Formula (9) and Formula (12), in the (x(1), x(t+1)) space. Here, Formula (9) is represented by the dashed vertical line, whereas Formula (12) is represented by the two hyperbolas.…”