Smart sensor architectures for embedded biosignal analysis

In many sports and medical applications small and wearable sensors are used that capture motion and physiological signals. Yet, commonly such sensor are only capable of acquiring raw data, which is afterwards transmitted to a smartphone or other mobile computing device where processing and classification is carried out. This results in limited usability because another device has to be worn. Moreover, high power consumption due to continuous transmission is a main disadvantage. Therefore, we propose a smart sensor approach that can alleviate these problems by carrying out the processing on the sensor itself. To house the processing schemes in the sensor node we developed a new computer architecture utilizing an FPGA and an ASIC. To show the benefits of the in-sensor processing, we chose two representative biosensor applications: a movement and fall detection system for the elderly and a swimming style recognition system for professional athletes. Compared to conventional approaches the same classification rate can be achieved while saving space, power, weight and setup costs.