phat 18(15): e5

Research Article

A Pervasive Sensing Approach to Automatic Assessment of Trunk Coordination Using Mobile Devices

Download31 downloads
  • @ARTICLE{10.4108/eai.13-7-2018.159604,
        author={Zilu Liang and Yasuyuki  Yoshida and Nami Iino and Takuichi Nishimura and Mario Alberto Chapa-Martell and Satoshi Nishimura},
        title={A Pervasive Sensing Approach to Automatic Assessment of Trunk Coordination Using Mobile Devices},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={4},
        number={15},
        publisher={EAI},
        journal_a={PHAT},
        year={2018},
        month={7},
        keywords={trunk coordination, health promotion, pervasive computing, inertia measurement units (IMU), accelerometer, gyroscope, machine learning},
        doi={10.4108/eai.13-7-2018.159604}
    }
    
  • Zilu Liang
    Yasuyuki Yoshida
    Nami Iino
    Takuichi Nishimura
    Mario Alberto Chapa-Martell
    Satoshi Nishimura
    Year: 2018
    A Pervasive Sensing Approach to Automatic Assessment of Trunk Coordination Using Mobile Devices
    PHAT
    EAI
    DOI: 10.4108/eai.13-7-2018.159604
Zilu Liang1,2,*, Yasuyuki Yoshida3, Nami Iino3, Takuichi Nishimura3, Mario Alberto Chapa-Martell4, Satoshi Nishimura3
  • 1: Kyoto University of Advanced Science, Kyoto, Japan
  • 2: The University of Tokyo, Tokyo, Japan
  • 3: National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
  • 4: CAC Corporation, Tokyo, Japan
*Contact email: z.liang@cnl.t.u-tokyo.ac.jp

Abstract

Assessing trunk coordination has many potential applications in health promotion. However, traditional bio-mechanical approaches are not suited for daily use as they require expensive devices and manual analysis. This study aimed to develop an approach for automatic classification of good and poor trunk coordination using widely available mobile devices. We investigated different combinations of sensor locations (i.e. chest and pelvis), sensing modalities (i.e. accelerometer and gyroscope) and classification techniques (i.e. SVM, KNN, and decision tree). Results showed that using both sensing modalities at chest and pelvis with SVM produced the best classification accuracy: 96% for chest rotation and 100% for pelvis rotation. In practice, however, using one device with both sensing modalities (i.e. accelerometer and gyroscope) will achieve a better trade-off between feasibility and accuracy. In this case, the device should be fixed on the chest. KNN should be selected as the classification technique for chest rotation (best accuracy 95%), and SVM should be selected as the classification technique for pelvis rotation (best accuracy 79%). Post hoc analysis found that poor coordination during chest rotation was associated to weak cross-correlation of angular velocity between chest and pelvis in the frontal plane, while poor coordination during pelvis rotation was associated to weak correlations of angular velocity between the three orthogonal components at chest. This study demonstrated how simple mobile devices can capture relevant motion data and extract key features that help construct computational models for automatic assessment of trunk coordination.