  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 towards
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.
