Personalized Monitoring in Home Healthcare: An Assistive System for Post Hip Replacement Rehabilitation

Alaa Kryeem, Shmuel Raz, Dana Eluz, Dorit Itah, Hagit Hel-Or, Ilan Shimshoni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The rehabilitation process for hip replacement surgery relies on supervised exercises recommended by medical authorities. However, limitations in therapist availability, budget constraints, and evaluation inconsistencies have prompted the need for a more accessible and user-friendly solution. In this paper, we propose a scalable, user-friendly, and cost-effective vision-based human action recognition system utilizing machine learning (ML) and 2D cameras. By providing personalized monitoring, our solution aims to address the limitations of traditional rehabilitation methods and support productive home-based healthcare. A key component of our work involves the use of deep learning (DL) method to align time-series exercise data, which ensures accurate analysis and assessment. Additionally, we introduce the concept of a Golden Feature, which plays a critical role in the framework by providing valuable insights into exercise execution and contributing to overall system accuracy. Furthermore, our framework goes beyond predicting exercise scores and focuses on predicting comments for partially successful cases using a multi-label ML model. This allows for a deeper understanding of the clinical reasons behind partial success, such as the patient's physical condition and their execution of the exercise. By identifying and analyzing these factors, our framework provides meaningful feedback and guidance to support effective rehabilitation. When evaluated on multiple exercises, the system achieved an accuracy level of 80% or higher on predicting execution score, and 72% on predicting the execution feedback.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1860-1869
Number of pages10
ISBN (Electronic)9798350307443
DOIs
StatePublished - 2023
Event2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 - Paris, France
Duration: 2 Oct 20236 Oct 2023

Publication series

NameProceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023

Conference

Conference2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
Country/TerritoryFrance
CityParis
Period2/10/236/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Assistive System
  • Deep learning
  • Home Healthcare
  • Machine learning
  • multi label ML
  • Personalized Monitoring
  • Rehabilitation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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