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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Bibliographic Details
Published in:IEEE sensors journal Vol. 20; no. 23; pp. 13867 - 13879
Main Authors: Alinia, Parastoo, Fallahzadeh, Ramin, Connolly, Christopher P., Ghasemzadeh, Hassan
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 this challenge, in this article, we introduce, ParaLabel , a filter-based step counting algorithm that is reliable against various walking velocities and intensities. ParaLabel addresses this problem by learning a filter cut-off frequency autonomously in a new domain without the need for collecting sensor data and manually tuning the algorithm parameter for a different velocity and/or pattern of walking. We formulate this problem as a transfer learning problem in which the new filter cut-off frequency is transferred from a bank containing previously fine-tuned parameters from different domain(s). Our extensive analysis using real data collected from 15 participants while wearing an accelerometer sensor on their chest, wrist, or left pocket demonstrates the superiority of ParaLabel to two commercially available trackers worn on the same body location, and state-of-the-art techniques. ParaLabel achieves 96.3% − 99.9% accuracy during walking on a treadmill at three different velocities, 98.2% − 99.9% accuracy during walking with a shopping cart, and 89.3% − 97.3% accuracy while walking with the aid of a walker.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3009231