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Long-term activity recognition relies on wearable sensors that log the physical actions of the wearer, so that these can be analyzed afterwards. Recent progress in this field has made it feasible to log high-resolution inertial data, resulting in increasingly large data sets. We propose the use of piecewise linear approximation techniques to facilitate this analysis. This paper presents a modified version of SWAB to approximate human inertial data as efficiently as possible, together with a matching algorithm to query for similar subsequences in large activity logs. We show that our proposed algorithms are faster on human acceleration streams than the traditional ones while being comparable in accuracy to spot similar actions, benefitting post-analysis of human activity data.

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Abstract. Activity recognition with a wearable accelerometer is a common investigated research topic and enables the detection of basic activities like sitting, walking or standing. Recent work in this area adds different sensing modalities to the inertial data to collect more information of the user's environment to boost activity recognition for more challenging activities. This work presents a sensor prototype consisting of an accelerometer and a capacitive proximity sensor that senses the user's activities based on the combined sensor values. We show that our proposed approach of combining both modalities significantly improves the recognition rate for detecting activities of daily living.

Many sensor network applications observe trends over an area by regularly sampling slow-moving values such as humidity or air pressure (for example in habitat monitoring). Another well-published type of application aims at spotting sporadic events, such as sudden rises in temperature or the presence of methane, which are tackled by detection on the individual nodes. This paper focuses on a zone between these two types of applications, where phenomena that cannot be detected on the nodes need to be observed by relatively long sequences of sensor samples. An algorithm that stems from data mining is proposed that abstracts the raw sensor data on the node into smaller packet sizes, thereby minimizing the network traffic and keeping the essence of the information embedded in the data. Experiments show that, at the cost of slightly more processing power on the node, our algorithm performs a shape abstraction of the sensed time series which, depending on the nature of the data, can extensively reduce network traffic and nodes' power consumption.

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