Abstract
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
Highlights
Wearable devices can monitor various human body symptoms ranging from heart, respiration, movement, to brain activities
The main reason for this is, that the development of machine learning algorithms has been strongly influenced by the development of powerful mainframe computers that perform learning offline in big server farms only eventually sending back results to the user
We presented the state-of-the-art core elements that enable the development of wearable devices for healthcare and biomedical applications with extreme edge adaptive computing capability
Summary
Wearable devices can monitor various human body symptoms ranging from heart, respiration, movement, to brain activities. Instead, can have time constants on the order of tens of milliseconds to seconds (Jo et al, 2015; Wang et al, 2017; Wang et al, 2019; Wang et al, 2019c; Yang et al, 2017; Covi et al, 2019), being able to emulate biological time constants This feature is especially useful to implement spatiotemporal recognition (Wang et al, 2021) or to enable brain inspired algorithms which need to keep trace of the recent neural activity. This review discusses the challenges to undertake for designing extreme edge computing wearable devices for healthcare and biomedical applications in four different categories: (i) the state-of-the-art wearable sensors and main restrictions toward low-power and high performance learning capabilities; (ii) different algorithms for modeling biologically plausible continual learning; (iii) CMOS-based neuromorphic processors and signal processing techniques enabling low-power local edge computing strategies; (iv) emerging memristive devices for more efficient and scalable embedded intelligent systems.
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