Walk and Jog Characterization Using a Triaxial Accelerometer

The characterization of daily activities using accelerometers is a currently active research field, with special interest on fall detection of elderly people and sports performance. Most works that account walking and running are peak-acceleration based, however, false positives due to artefact acceleration peaks affect the estimation. Also, the proposed algorithms must be simple enough to be implemented in a smartphone or an embedded device. In this work a non-peak based methodology is proposed and validated with three publicly available databases. As a result, the methodology provides a quality-of-activity estimation, useful for sports performance and avoiding false positives in fall detectors.

[1]  M. Nagabushanam,et al.  Fast Implementation of Lifting based 1D/2D/3D DWT-IDWT Architecture for Image Compression , 2012 .

[2]  Patrick Seeling,et al.  Towards the run and walk activity classification through step detection - An android application , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Claudia V. Isaza,et al.  Artificial Neural Networks as an alternative to traditional fall detection methods , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Chih-Hsien Hsia,et al.  Efficient modified directional lifting-based discrete wavelet transform for moving object detection , 2014, Signal Process..

[5]  Paul B Gastin,et al.  Validity of a trunk-mounted accelerometer to assess peak accelerations during walking, jogging and running , 2015, European journal of sport science.

[6]  Inmaculada Plaza,et al.  Challenges, issues and trends in fall detection systems , 2013, Biomedical engineering online.

[7]  A K Bourke,et al.  Activity classification using a single chest mounted tri-axial accelerometer. , 2011, Medical engineering & physics.

[8]  William J. Tharion,et al.  Real time gait pattern classification from chest worn accelerometry during a loaded road march , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Manolis Tsiknakis,et al.  The MobiFall Dataset: Fall Detection and Classification with a Smartphone , 2014, Int. J. Monit. Surveillance Technol. Res..

[10]  A. Enis Çetin,et al.  Fall detection using single-tree complex wavelet transform , 2013, Pattern Recognit. Lett..