UCLA

This dissertation investigates the limitation and challenges of wrist-worn sensing in activity state recognition including posture and gait quality inference. In spite of all the benefits that come with wrist-worn sensing systems, there are many challenges in inferring activities using them. The most important challenge is the high rate of false positive due to unwanted hand motion when the system is worn on the wrist. Another important challenge is the limitation on the power budget and processing capabilities of a wrist-worn device. Being far from the Center of Mass (COM), inferring posture and gait seem to be a very challenging problem using wrist data. This dissertation tries to provide a deep investigation on these challenges and propose ways to mitigate them. Different time series analysis methods are employed to serve for improvement in both accuracy and power consumption of activity state recognition. Along the way, we introduce some of the systems and algorithms that were built around these ideas and how they can be helpful in tackling the challenges of wrist-worn sensing systems, when it comes to activity state recognition.