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The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning with novel wearable devices. Machine learning models, however, suffer from being computationally demanding, which typically has resulted on the acquired data having to be transmitted to remote cloud servers for inference. This is not ideal from the system's requirements point of view. Recently, efforts to replace the cloud servers with an alternative inference device closer to the sensing platform, has given rise to a new area of research Tiny Machine Learning (TinyML). In this work, we investigate the different challenges and specifications trade-offs associated to existing hardware options, as well as recently developed software tools, when trying to use microcontroller units (MCUs) as inference devices for health and care applications. The paper also reviews existing wearable systems incorporating MCUs for monitoring, and management, in the context of different health and care intended uses. Overall, this work can be used as a kick-start for embedding machine learning models on MCUs, focusing on healthcare wearables.INDEX TERMS Edge ML, embedded machine learning, healthcare, microcontroller, TinyML, wearable.

This study presents a new architecture for a field programmable analog array (FPAA) for use in low‐frequency applications, and a generalized circuit realization method for the implementation of nth‐order elliptic filters. The proposed designs of both the FPAA and elliptic filters are based on the operational transconductance amplifier (OTA) used in implementing OTA‐C filters for biopotential signal processing. The proposed FPAA architecture has a flexible, expandable structure with direct connections between configurable analog blocks (CABs) that eliminates the use of switches. The generalized elliptic filter circuit realization provides a simplified, direct synthetic method for an OTA‐C symmetric balanced structure for even/odd‐nth‐order low‐pass filters (LPFs) and notch filters with minimum number of components, using grounded capacitors. The filters are mapped on the FPAA, and both architectures are validated with simulations in LTspice using 90‐nm complementary metal‐oxide semiconductor (CMOS) technology. Both proposed FPAA and filters generalized synthetic method achieve simple, flexible, low‐power designs for implementation of biopotential signal processing systems.