Advances in technology have led to the development of wearable sensing, computing and communication devices, enabling a large variety of new applications in several domains, including wellness and health care. Monitoring human movements and motor functions perhaps is considered one of the most important applications. Despite their tremendous potential to impact our lives, such systems face a number of hurdles to become a reality. The enabling sensors often demand a large amount of energy, requiring sizable batteries. This creates challenges for further miniaturization. The goal of this research is to enable ultra-low power sensors and DSP's for wearable computers operating with a very small power budget enabling weeks and months of battery lifetime. The proposed research will empower a large set of applications in health care and wellness domains including gait analysis, fall prevention and monitoring physical exercise. This project will ideally reduce the size and weight of wearable computers significantly, enabling many ubiquitous health monitoring applications. It can dramatically improve the quality of health monitoring practice and medical research, empowering more applications that are not currently feasible. This project targets a very important health care application for gait monitoring. Considering the importance of wearable gait monitoring applications and our efforts in reducing the form factor of the sensors that will justify their true ubiquitous use, semiconductor companies will produce billions of chips for wearable computers.
Technical The proposed research takes advantage of novel electromechanical designs and state of the art micromachining technologies to fabricate contact-based (full or tunneling) inertial sensors with overall dimensions in the hundreds of microns to a few millimeters. Such devices are essentially comprised of a number of acceleration switches requiring a small bias voltage of around 1V and no steady current flow to operate. The output of the sensor is turned ON/OFF by connecting/disconnecting the bias voltage to the device output electrode depending on the accelerations and/or rotation rates the device observes. This is contrary to the existing inertial sensors that provide an analog output requiring significant further processing in the analog domain with a power budget of > 1mW which turns out to be the bottleneck. The proposed new class of devices can be directly interfaced with digital readout/control electronics. The proposed research will also enable a new set of methodologies that co-jointly perform the signal processing and optimize the power of sensors and digital circuitry by controlling the sampling frequency and bit resolution of sensors in real-time. The proposed research will be validated in the context of an important health monitoring application: gait analysis using wrist-worn and shoe-worn sensors.
