A Classification System to Assess Low Back Muscle Endurance and Activity Using mHealth Technologies

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10306)


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.


Muscle Activity Root Mean Square Maximum Voluntary Muscle Contraction Muscle Fatigue Football Player 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) Projects TIN2015-71873-R and TIN2015-67020-P together with the European Fund for Regional Development (FEDER). The authors want to especially thank all of the volunteers who participated in the experiments and Cristian Rivera Peregrina for your help.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Architecture and Computer TechnologyUniversity of GranadaGranadaSpain
  2. 2.Department of Nursing and PhysiotherapyUniversity of CadizCadizSpain
  3. 3.Telemedicine Group, Center for Telematics and Information TechnologyUniversity of TwenteEnschedeNetherlands

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