Paper Info
Title
A Classification System to Assess Low Back Muscle Endurance and Activity Using mHealth Technologies.
Abstract
Low back pain remains a major cause of absenteeism in the world. In addition to its socio-economic impact, the age at which the first symptoms appear is decreasing. Consequently, there are more experts who start incorporating prevention plans for the lumbar area in their work routines. In addition, the continued market growth of wearable sensors and the potential opened up by wearable technology allows experts to obtain a precise feedback from improvements in their patients in a daily basis. For this reason, this work wants to continue with the development and verification of the usefulness of mDurance, a novel mobile health system aimed at supporting specialists in the functional assessment of trunk endurance and muscle activity by using wearable and mobile devices. This work presents an extension of this system to classify low back muscle activity in the low back. mDurance has been tested into a professional football team. Clustering and data mining are applied in a new dataset of endurance and muscle activity data collected through mDurance. In addition, these results are cross-related with a questionnaire created to evaluate how the football players perceive themselves physically and mentally. The results show a clear correlation between the perception participants have about their low back endurance and the objective measurements conducted through mDurance. The results obtained through mDurance and the football players answers show a 68.3% of accuracy and 83.8% of specificity in the first approach to build a classifier to assess low back muscle endurance and activity using mDurance system.
Year
DOI
Venue
2017
10.1007/978-3-319-59147-6_60
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II
Field
DocType
Volume
Computer science,Absenteeism,mHealth,Artificial intelligence,Physical medicine and rehabilitation,Wearable technology,Football,Wearable computer,Simulation,Mobile device,Muscle fatigue,Low back pain,Machine learning
Conference
10306
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Ignacio Diaz Reyes100.34
M. Damas238733.04
José Antonio Moral-Muñoz3122.91
Oresti Baños438035.57