Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
IEEE Transactions on Pattern Analysis and Machine Intelligence
A compact, high-speed, wearable sensor network for biomotion capture and interactive media
Proceedings of the 6th international conference on Information processing in sensor networks
An automatic segmentation technique in body sensor networks based on signal energy
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Feature selection and activity recognition from wearable sensors
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Pattern Recognition Letters
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Wireless sensor networks (WSNs) are becoming more and more attractive because of their flexibility. In particular, WSNs are being applied to a user body in order to monitor and detect some activities of daily living (ADL) performed by the user (e.g., for medical purposes). This class of WSNs are typically denoted as body sensor networks (BSNs). In this paper, we present a simple, yet accurate and robust, BSN-based activity classification algorithm that can detect and classify a sequence of activities, chosen from a limited set of fixed known activities, by observing the outputs generated by accelerometers and gyroscopes at the sensors placed over the body. This approach has led us to win the first BSN contest [1] and the presented results refer to the experimental data (publicly available) provided in this contest.