MENTAL HEALTH DECLINE PREDICTION - A Smart Sensor for Day to Day Activity Recognition

Thomas Kaegi-Trachsel, Juerg Gutknecht, Dennis Majoe

Abstract

The ambulatory activity of a person may be used as one component within an overall wearable sensor system that predicts the onset of mental health problems. Ergonomic smart sensors that can determine the total energy expenditure and type of ambulation may provide unique insights to the coping behaviour of stressed people. Rather than relying on wearable computers, a single smart miniature sensor that is worn 24/7 should perform the complex embedded recognition tasks while meeting difficult battery life, wireless communications and ergonomic constraints. The development and testing of such a smart sensor is described which takes into account action timeline variations, as well as action variations both intra individual and inter individual.

References

  1. S. M. Stahl, “The Psychopharmacology of Energy and Fatigue”, Journal of Clinical Psychiatry, Physicians Postgraduate Press, January 2002, pages 6-31.
  2. T. Takeuchi, M. Nakao, et al, “Association of the metabolic syndrome with depression and anxiety in Japanese men: A 1-year cohort study”, Journal of Diabetes Metabolic Research, Rev. 2009 Nov, Vol 25(8), pages 762-7.
  3. A. Berlin, W. Kop and P. Deuster, “Depressive Mood Symptoms and Fatigue After Exercise Withdrawal: The Potential Role of Decreased Fitness”, Journal of Psychosomatic Medicine, 2006, Vol 68, pages 224- 230.
  4. B. Ainsworth, M. Haskell et al, “Compendium of physical activities: an update of activity codes and MET intensities”, Journal of Medical Science Sports Exercise, 2000, Vol 32, No.9, Suppl., pages. S498- S516.
  5. J. Michalak, N. Troje et al, “Embodiment of Sadness and Depression-Gait Patterns Associated With Dysphoric Mood”, Journal of Psychosomatic Medicine, 2009, Vol 71, pages 580-587.
  6. U. Maurer, A. Smailagic, D. Siewiorek and M. Deisher, “Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions”, Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, 2006, pages 113 - 116.
  7. M. Lee, J. Kim, K. Kim, I. Lee, S. H. Jee, S. K. Yoo, “Physical Activity Recognition Using a Single TriAxis Accelerometer”, Proceedings of the World Congress on Engineering and Computer Science, 2009, Vol 1. WCECS.
  8. J. Suutala, S. Pirttikangas, J. Roning, H. Ichikawa et al, “Discriminative Temporal Smoothing for Activity Recognition from Wearable Sensors”, UCS 2007, LNCS 4836, pages. 182-195.
  9. D. Majoe, L. Widmer and J. Gutknecht, “Enhanced Motion Interaction for Multimedia Applications”, Proceedings of the 7th International Conference on Advances in Mobile Computing & Multimedia, 2009, Kuala Lumpur, Malaysia.
  10. L. R. Rabiner, “A tutorial on HMM and selected applications in speech recognition”, In Proc. IEEE, Vol. 77, No. 2, pp. 257-286, Feb. 1989.
Download


Paper Citation


in Harvard Style

Kaegi-Trachsel T., Gutknecht J. and Majoe D. (2011). MENTAL HEALTH DECLINE PREDICTION - A Smart Sensor for Day to Day Activity Recognition . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011) ISBN 978-989-8425-34-8, pages 219-227. DOI: 10.5220/0003150002190227


in Bibtex Style

@conference{healthinf11,
author={Thomas Kaegi-Trachsel and Juerg Gutknecht and Dennis Majoe},
title={MENTAL HEALTH DECLINE PREDICTION - A Smart Sensor for Day to Day Activity Recognition},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)},
year={2011},
pages={219-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003150002190227},
isbn={978-989-8425-34-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)
TI - MENTAL HEALTH DECLINE PREDICTION - A Smart Sensor for Day to Day Activity Recognition
SN - 978-989-8425-34-8
AU - Kaegi-Trachsel T.
AU - Gutknecht J.
AU - Majoe D.
PY - 2011
SP - 219
EP - 227
DO - 10.5220/0003150002190227