Activity Recognition On Unmodified Consumer Smartphones Via Active Ultrasonic Sensing

Sensor miniaturisation and streaming classification techniques can be used to recognize human behaviours and contexts. This is extremely valuable to realize smart environments, e.g. to support healthy and independent living. The most important parameters to sense include indoor location, gestures, or emergencies like falls. Up to now, activity recognition systems face a number of sensitive drawbacks. For example, camera-based systems induce privacy issues and are costly to deploy. Body-worn systems are inconvenient to wear over long periods of time. Highly visible systems may introduce social stigma and modify the well-known living environment. In this project, we explore the possibility for the use of a new, unobtrusive, physical principle to sense and recognize human activities using off-the-shelf smart-phone. A person's smart-phone is a cornucopia of information. The huge variety of sensors in today's mobile phones makes these devices a prime target for human activity recognition. Our novel approach is to develop a novel activity recognizing system using an unmodified smart-phone. We profit from integrated microphones and loudspeakers without additional hardware components needed. The advantage of this system is therefore that it can be easily installed on a smart-phone and put into action. An android application has already been developed which is able to send a high frequency sound in the near ultrasound range, e.g. 20 kHz. Using the received echo from the microphone, the information caused by movement in midair around the device will be extracted. In this thesis we intend to improve the performance of the existing system with respect to noise cancellation and other classification schemes. In this thesis, we present an android application called Trainer for complex activity recognition. It is built on ultrasense [8], a mobile application that capitalizes the characters of ultrasound to inspect the surrounding environment. The application is able to send a high frequency signal in the near ultrasound range, e.g. 20 kHz. Using the received echo from the microphone, the information caused by movement in midair around the device will be extracted. Complex activities tagged under home exercises are evaluated using micro-Doppler signatures [mD-signatures]. We propose an algorithm to classify a set of exercises carried out by the user and show that using the Support vector machine classifier we are able to obtain an accuracy of 85% using Principal component analysis and a signature feature introduced in this thesis as a feature.