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Accepted for/Published in: JMIR Biomedical Engineering

Date Submitted: Jul 23, 2020
Date Accepted: Feb 8, 2021

The final, peer-reviewed published version of this preprint can be found here:

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

Kappattanavar A, Steckhan N, Sachs JP, Arnrich B, Böttinger E

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

JMIR Biomed Eng 2021;6(1):e21105

DOI: 10.2196/21105

PMID: 38907372

PMCID: 11041431

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Monitoring of sitting postures with sensor networks in controlled and free-living environments: A systematic review

  • Arpita Kappattanavar; 
  • Nico Steckhan; 
  • Jan Philipp Sachs; 
  • Bert Arnrich; 
  • Erwin Böttinger

ABSTRACT

Background:

Background:

Prolonged sitting postures have been reported to increase the probability of developing low back pain. Moreover, the majority of employees in the industrial world work ninety percent of their time in a seated position.

Objective:

This review focuses on the technologies and algorithms that have been used to classify seating postures on a chair with respect to spine and limb movements.

Methods:

Three electronic literature databases have been surveyed to identify the studies classifying sitting posture in adults. Fourteen articles have been finally shortlisted. These articles were categorized into low, medium, and high quality. Most of the articles were categorized as medium quality (12/14).

Results:

The majority of the studies used pressure sensors (13/14) to classify sitting postures. Neural Networks were the most frequently (6/14) used approaches for classifying sitting postures.

Conclusions:

Based on the current study the classification of sitting posture is still in the nascent stage and hence, we would suggest personalized sitting posture analysis. Furthermore, the review emphasizes identifying at least five basic postures along with different limb and spine movements in a free-living environment. It is essential to annotate the data set with ground truths for subsequent training of the classifier to solve the sitting posture classification problem.


 Citation

Please cite as:

Kappattanavar A, Steckhan N, Sachs JP, Arnrich B, Böttinger E

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

JMIR Biomed Eng 2021;6(1):e21105

DOI: 10.2196/21105

PMID: 38907372

PMCID: 11041431

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