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Context Impacts in Accelerometer-Based Walk Detection and Step Counting

1
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
School of Information, Renmin University of China, Beijing 100872, China
3
School of Software, Tsinghua University, Beijing 100084, China
4
Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
5
School of Software, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3604; https://doi.org/10.3390/s18113604
Received: 31 August 2018 / Revised: 28 September 2018 / Accepted: 10 October 2018 / Published: 24 October 2018
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Abstract

Walk detection (WD) and step counting (SC) have become popular applications in the recent emergence of wearable devices. These devices monitor user states and process data from MEMS-based accelerometers and optional gyroscope sensors. Various algorithms have been proposed for WD and SC, which are generally sensitive to the contexts of applications, i.e., (1) the locations of sensor placement; (2) the sensor orientations; (3) the user’s walking patterns; (4) the preprocessing window sizes; and (5) the sensor sampling rates. A thorough understanding of how these dynamic factors affect the algorithms’ performances is investigated and compared in this paper. In particular, representative WD and SC algorithms are introduced according to their design methodologies. A series of experiments is designed in consideration of different application contexts to form an experimental dataset. Different algorithms are then implemented and evaluated on the dataset. The evaluation results provide a quantitative performance comparison indicating the advantages and weaknesses of different algorithms under different application scenarios, giving valuable guidance for algorithm selection in practical applications. View Full-Text
Keywords: walk detection; step counting; gait analysis; machine learning; signal processing walk detection; step counting; gait analysis; machine learning; signal processing
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Ao, B.; Wang, Y.; Liu, H.; Li, D.; Song, L.; Li, J. Context Impacts in Accelerometer-Based Walk Detection and Step Counting. Sensors 2018, 18, 3604.

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