Quotation Choi, Jungyeon, Youn, Jong-Hoon, Haas, Christian. 2019. Machine Learning Approach for Foot-side Classification using a Single Wearable Sensor. In International Conference on Information Systems (ICIS) 2019, Hrsg. AIS, 1-8. Munich, Germany: None.


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Abstract

Gait analysis is a common technique used to identify problems related to movement and posture in people with injuries, and foot-side detection is one of its important challenges. As many commercial sensors only provide limited information and traditional lab-based gait analysis is expensive, the aim of this study is to discriminate between left and right foot steps based on acceleration data from a single chest-worn accelerometer. To achieve this goal, an experimental study was conducted with 25 participants wearing an accelerometer on their chest and walking in a static environment. Several machine learning (ML) classifiers were trained to detect a foot-side from collected acceleration data. All machine learning classifiers achieved high classification accuracy, with Random Forest providing the best results. This result shows that ML-based foot-side classification using a single sensor is achievable and can contribute to develop an efficient health monitoring system to track lower limb’s problems.

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Publication's profile

Status of publication Published
Affiliation External
Type of publication Contribution to conference proceedings
Language English
Title Machine Learning Approach for Foot-side Classification using a Single Wearable Sensor
Title of whole publication International Conference on Information Systems (ICIS) 2019
Editor AIS
Page from 1
Page to 8
Location Munich, Germany
Year 2019
URL https://www.researchgate.net/publication/338344695_Machine_Learning_Approach_for_Foot-side_Classification_using_a_Single_Wearable_Sensor
Open Access N

Associations

People
Haas, Christian (Details)
External
Choi, Jungyeon (University of Nebraska - Omaha, United States/USA)
Youn, Jong-Hoon (University of Nebraska - Omaha, United States/USA)
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