Literature DB >> 29059831

Real-time gait analysis with accelerometer-based smart shoes.

R Delgado-Gonzalo, J Hubbard, Ph Renevey, A Lemkaddem, Q Vellinga, D Ashby, J Willardson, M Bertschi.   

Abstract

In this paper, we present the evaluation of a new smart shoe capable of performing gait analysis in real time. The system is exclusively based on accelerometers which minimizes the power consumption. The estimated parameters are activity class (rest/walk/run), step cadence, ground contact time, foot impact (zone, strength, and balance), forward distance, and speed. The different parameters have been validated with a customized database of 26 subjects on a treadmill and video data labeled manually. Key measures for running analysis such as the cadence is retrieved with a maximum error of 2%, and the ground contact time with an average error of 3.25%. The classification of the foot impact zone achieves a precision between 72% and 91% depending of the running style. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.

Mesh:

Year:  2017        PMID: 29059831     DOI: 10.1109/EMBC.2017.8036783

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques.

Authors:  Stephanie R Moore; Christina Kranzinger; Julian Fritz; Thomas Stӧggl; Josef Krӧll; Hermann Schwameder
Journal:  Sensors (Basel)       Date:  2020-11-25       Impact factor: 3.576

Review 2.  Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review.

Authors:  Hari Prasanth; Miroslav Caban; Urs Keller; Grégoire Courtine; Auke Ijspeert; Heike Vallery; Joachim von Zitzewitz
Journal:  Sensors (Basel)       Date:  2021-04-13       Impact factor: 3.576

3.  Shoe-Integrated, Force Sensor Design for Continuous Body Weight Monitoring.

Authors:  Shahzad Muzaffar; Ibrahim Abe M Elfadel
Journal:  Sensors (Basel)       Date:  2020-06-12       Impact factor: 3.576

4.  Context Impacts in Accelerometer-Based Walk Detection and Step Counting.

Authors:  Buke Ao; Yongcai Wang; Hongnan Liu; Deying Li; Lei Song; Jianqiang Li
Journal:  Sensors (Basel)       Date:  2018-10-24       Impact factor: 3.576

  4 in total

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