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.