Conditioning and processing of biological signals represent interesting challenges for wearable electronics in health applications. Information gathering from these signals requires complex hardware circuitry and dedicated computation resources. The design of innovative analog front-end integrated circuits, combined with efficient signal processing algorithms, allows the development of platforms for monitoring, activity and gesture recognition based on embedded real-time systems. This paper describes an Electromyography pattern recognition system based on the combination of low cost passive sensors, an innovative analog front-end and a low power microcontroller. The performance of the proposed system matches state-of-the-art high-end active sensors, opening the way to the development of affordable and accurate wearable devices.

Wearable devices for monitoring vital signs such as heart-rate, respiratory rate and blood pressure are demonstrating to have an increasing role in improving quality of life and in allowing prevention for chronic cardiac diseases. However, the design of a wearable system without reference to ground potential requires multi-level strategies to remove noise caused from power lines. This paper describes a bio-potential acquisition embedded system designed with an innovative analog front-end, showing the performance in EEG and ECG applications and the comparison between different noise reduction algorithms. We demonstrate that the proposed system is able to acquire bio-potentials with a signal quality equivalent to state-of-the-art bench-top biomedical devices and can be therefore used for monitoring purpose, with the advantages of a low-cost low-power wearable devices.

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The live demonstration of the modular medical instrumentation platform consists of two parts: A disassembled prototype device giving an insight in the modular design of the presented hardware and a demonstration of a sample system for ECG and pulse oximetry acquisition.