Citation
Yashaswini Prathivadi, Terrell R. Bennett, Roozbeh Jafari. "Robust Activity Recognition using Wearable IMU Sensors". Sensors 2014, IEEE, 2, November, 2014.

Abstract
In this paper, an orientation transformation (OT) algorithm is presented that increases the effectiveness of performing activity recognition using body sensor networks (BSNs). One of the main limitations of current recognition systems is the requirement of maintaining a known, or original, orientation of the sensor on the body. The proposed OT algorithm overcomes this limitation by transforming the sensor data into the original orientation framework such that orientation dependent recognition algorithms can still be used to perform activity recognition irrespective of sensor orientation on body. The approach is tested on an orientation dependent activity recognition system which is based on dynamic time warping (DTW). The DTW algorithm is used to detect the activities after the data is transformed by OT. The precision and recall for the activity recognition for five subjects and five movements was observed to range from 74% to 100% and from 83% to 100%, respectively. The correlation coefficient between the transformed data and the data from the original orientation is above 0.94 on axis with well-defined patterns.

Electronic downloads

• https://www.src.org/library/publication/p071047/. (Link to paper on SRC website.)
• http://dx.doi.org/10.1109/ICSENS.2014.6985041

• HTML

  Yashaswini Prathivadi, Terrell R. Bennett, Roozbeh Jafari.
  <a
  href="http://www.terraswarm.org/pubs/340.html"
  >Robust Activity Recognition using Wearable IMU
  Sensors</a>, Sensors 2014, IEEE, 2, November, 2014.

• Plain text

  Yashaswini Prathivadi, Terrell R. Bennett, Roozbeh Jafari.
  "Robust Activity Recognition using Wearable IMU
  Sensors". Sensors 2014, IEEE, 2, November, 2014.

• BibTeX

  @inproceedings{PrathivadiBennettJafari14_RobustActivityRecognitionUsingWearableIMUSensors,
      author = {Yashaswini Prathivadi and Terrell R. Bennett and
                Roozbeh Jafari},
      title = {Robust Activity Recognition using Wearable IMU
                Sensors},
      booktitle = {Sensors 2014},
      organization = {IEEE},
      day = {2},
      month = {November},
      year = {2014},
      abstract = {In this paper, an orientation transformation (OT)
                algorithm is presented that increases the
                effectiveness of performing activity recognition
                using body sensor networks (BSNs). One of the main
                limitations of current recognition systems is the
                requirement of maintaining a known, or original,
                orientation of the sensor on the body. The
                proposed OT algorithm overcomes this limitation by
                transforming the sensor data into the original
                orientation framework such that orientation
                dependent recognition algorithms can still be used
                to perform activity recognition irrespective of
                sensor orientation on body. The approach is tested
                on an orientation dependent activity recognition
                system which is based on dynamic time warping
                (DTW). The DTW algorithm is used to detect the
                activities after the data is transformed by OT.
                The precision and recall for the activity
                recognition for five subjects and five movements
                was observed to range from 74% to 100% and from
                83% to 100%, respectively. The correlation
                coefficient between the transformed data and the
                data from the original orientation is above 0.94
                on axis with well-defined patterns.},
      URL = {http://terraswarm.org/pubs/340.html}
  }
  

Posted by Barb Hoversten on 28 Jul 2014.