Low back pain (LBP) is no stranger to office workers. In Japan, 1 out of 10 otherwise healthy office workers suffer from LBP. Stretching and exercise help alleviate the pain, but workers often do this when it is too late. But what if our chairs could alert us before the pain worsens?
Now, researchers at Tohoku University have developed a new prediction method that employs pressure sensors installed on a conventional office chair. The sensors detect workers' movements on the chair dynamically and quantitatively.
The "smart chair" was tested in a real-life setting outside of the lab. Amassing data from 22 study participants over a period of three months, the research group combed through the information to investigate the dynamics of sitting behavior and identify a predictable LBP progression.
Further aided by various machine learning methods, the researchers discovered a common motif present in the sitting behavior of most participants. They pinpointed small motions in the body trunk that prevent the fixation of vertebral joints, therefore avoiding LBP's progression. The frequency of this motif could be used to predict the worsening of LBP throughout the day when compared to a morning reference state.
The research group hopes to apply the technology to other areas of the body. "Although the current method focused on LBP, we hope to collect data relating to the head and neck regions to be able to predict and prevent stiff necks and headaches," said paper coauthor Ryoichi Nagatomi.
The study was published in the journal Frontiers in Physiology.
- Publication Details:
Title: Low Back Pain Exacerbation Is Predictable Through Motif Identification in Center of Pressure Time Series Recorded During Dynamic Sitting
Authors: Ziheng Wang, Keizo Sato, Saida Salima Nawrin, Namareq Salah Widatalla, Yoshitaka Kimura and Ryoichi Nagatomi
Journal: Frontiers in Physiology
Contact:Ryoichi NagatomiTohoku University Graduate School of Biomedical Engineering
Email: nagatomimed.tohoku.ac.jpWebsite: http://www.sports.med.tohoku.ac.jp/english/publication.html