ParaLabel: Autonomous Parameter Learning for Cross-Domain Step Counting in Wearable Sensors

Wearable step counters, also referred to as activity trackers, have been developed for health and activity monitoring, as well as for step tracking. These trackers, however, produce unreliable measurements during slow walking and when walking with assistive devices (i.e., aided walking). To address...

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Veröffentlicht in:	IEEE sensors journal Jg. 20; H. 23; S. 13867 - 13879
Hauptverfasser:	Alinia, Parastoo, Fallahzadeh, Ramin, Connolly, Christopher P., Ghasemzadeh, Hassan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerometers Accuracy Algorithms cross-subject transfer learning Domains Feature extraction frequency components K-nearest neighbor Learning Legged locomotion low-pass filter Low-pass filters Machine learning algorithms Nearest neighbor methods Parameters peak detection Sensors Step counting time-domain features Tracking devices Treadmills wearable sensors Wearable technology Wrist
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
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