Ward, Jamie A.; (2006) Activity monitoring: continuous recognition and performance evaluation. Doctoral thesis (D.Sc), ETH Zurich.

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Abstract

Wearable computers promise the ability to access information and computing resources directly from miniature devices embedded in our clothing. The problem lies in how to access the most relevant information without disrupting whatever task it is we are doing. Most existing interfaces, such as keyboards and touch pads, require direct interaction. This is both a physical and cognitive distraction. The problem is particularly acute for the mobile maintenance worker who mu st access information, such as on-line manuals or schematics, quickly and with minimal distraction. One solution is a wearable computer that monitors the user’s ‘context’ - information such as activity, location and environment. Being ‘context aware’, the wearable would be better placed to offer relevant information to the user as and when it is needed. In this work we focus on recognising one of the most important parts of context: user activity. The contributions of the thesis are twofold. First, we present a method for recognising hand activities from a sequence using body-worn sensors. Second, in evaluating this method, we present a generalised strategy for characterising the performance of activity recognition systems. We define a set of typical hand and tool activities in a woodwork assembly scenario. We evaluate two methods for detecting and recognising these activities using a combination of body -worn microphones and accelerometers. The first method uses two separately placed microphones on the user’s arm to locate the source of an activity. When ever a sound is made close to the wrist, an interesting activity is assumed and classification is carried out using both acceleration and sound. The second method requires only wrist-worn sensors. It recognises activities by classifying sound and acceleration over a sliding window. The classifications from each of the sensor types are then compared, and a final result is given depending on how well they agree. In the second part of the thesis we introduce a strategy for evaluating the performance of continuous activity recognition systems. Like any area of scientific research, activity recognition requires standard methods and measures of performance. These are the tools with which researchers can compare and evaluate different systems, thus allowing the field to advance. Continuous activity recognition, however, has a number of performance issues which existing evaluation strategies - borrowed from related fields such as speech recognition - fail to capture. We explore these issues in depth and propose a new strategy of performance evaluation based on the complete characterisation of error types common to the activity recognition problem. Finally, we bring the two main topics of the thesis together. Using the results from our continuous recognition work we show the improvements of the proposed performance evaluation strategy over existing approaches.

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