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Proceedings chapter
English

Gait Recognition with Smart Devices Assisting Postoperative Rehabilitation in a Clinical Setting

Presented at Laguna Hills (CA, USA), 26-28 Sept. 2018
PublisherIEEE
Publication date2018
Abstract

Postoperative rehabilitation is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. This kind of rehabilitation is led by physiotherapists who assess each situation and prescribe appropriate exercises. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions, and therefore literature on this topic is still in its infancy. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns including walking with crutches with various levels of weight-bearing, walking with different frames, limping and walking normally. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. A gait detection accuracy of 94.9% was achieved among nine different gait classes, as these were labeled by professional physiotherapists.

Keywords
  • Sensors
  • Legged locomotion
  • Feature extraction
  • Machine learning
  • Monitoring
  • Web servers
  • Wheels
Citation (ISO format)
KYRITSIS, Athanasios et al. Gait Recognition with Smart Devices Assisting Postoperative Rehabilitation in a Clinical Setting. In: 2018 First International Conference on Artificial Intelligence for Industries (AI4I). Laguna Hills (CA, USA). [s.l.] : IEEE, 2018. p. 60–64. doi: 10.1109/AI4I.2018.8665677
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Proceedings chapter (Published version)
accessLevelRestricted
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ISBN978-1-5386-9209-7
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