Body Location Independent Activity Monitoring

Carina Figueira, Ricardo Matias, Hugo Gamboa


Human Activity Recognition (HAR) is increasingly common in people’s daily lives, being applied in health areas, sports and safety. Because of their high computational power, small size and low cost, smartphones and wearable sensors are suitable to monitor user’s daily living activities. However, almost all existing systems require devices to be worn in certain positions, making them impractical for long-term activity monitoring, where a change in position can lead to less accurate results. This work describes a novel algorithm to detect human activity independent of the sensor placement. Taking into account the battery consumption, only two sensors were considered: the accelerometer (ACC) and the barometer (BAR), with a sample frequency of 30 and 5 Hz, respectively. The signals obtained were then divided into 5 seconds windows. The dataset used is composed of 25 subjects, with more than 7 hours of recording. Daily living activities were performed with the smartphone worn in 12 different positions. From each window a set of statistical, temporal and spectral features were extracted and selected. During the classification process, a decision tree was trained and evaluated using a leave one user out cross validation. The developed framework achieved an accuracy of 94.5±6.8 %, regardless the subject and device’s position. This solution may be applied to elderly monitoring, as a rehabilitation tool in physiotherapy fields and also to be used by ordinary users, who just want to check their daily level of physical activity.


  1. Anjum, A. and Ilyas, M. U. (2013). Activity recognition using smartphone sensors. Consumer Communications and Networking Conference (CCNC), 2013 IEEE.
  2. Bianchi, F., Redmond, S. J., Narayanan, M. R., Cerutti, S., and Lovell, N. H. (2010). Barometric pressure and triaxial accelerometry-based falls event detection. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 18(6):619-627.
  3. Cabuz, E. I., Cabuz, C., and Wang, T.-Y. (2009). Piezoresistive pressure sensor. US Patent 7,546,772.
  4. Gomes, A. L. (2014). Human activity recognition with accelerometry: Novel time and frequency features. Master's thesis, Faculdade de Cieˆncias e Tecnologia da Universidade Nova de Lisboa.
  5. Grankin, M., Khavkina, E., and Ometov, A. (2012). Research of mems accelerometers features in mobile phones. In Proceedings of the 12th conference of Open Innovations Association FRUCT; Oulu, Finland, pages 31-36.
  6. Karantonis, D., Narayanan, M., Mathie, M., Lovell, N., and Celler, B. (2006). Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine, 10.
  7. Kavanagh, J. and Menz, H. (2008). Accelerometry: A technique for quantifying movement patterns during walking. ScienceDirect, Gait & Posture 28, 28.
  8. Kotsiantis, S. B., Zaharakis, I., and Pintelas, P. (2007). Supervised machine learning: A review of classification techniques.
  9. Li, N., Hou, Y., and Huang, Z. (2013). Implementation of a real-time human activity classifier using a triaxial accelerometer and smartphone. International Journal of Advancements in Computing Technology, 5(4).
  10. Machado, I. (2013). Human activity data discovery based on accelerometry. Master's thesis, Faculdade de Cieˆncias e Tecnologias da Universidade de Lisboa and PLUX Wireless Biosignals.
  11. Mathie, M. J., Coster, A. C., Lovell, N. H., and Celler, B. G. (2004). Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological measurement, 25(2):R1.
  12. Moncada-Torres, A., Leuenberger, K., Gonzenbach, R., Luft, A., and Gassert, R. (2014). Activity classification based on inertial and barometric pressure sensors at different anatomical locations. Physiological measurement, 35(7):1245.
  13. Muralidharan, K., Khan, A. J., Misra, A., Balan, R. K., and Agarwal, S. (2014). Barometric phone sensors: more hype than hope! In Proceedings of the 15th Workshop on Mobile Computing Systems and Applications, page 12. ACM.
  14. Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.
  15. Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., and Srivastava, M. (2010). Using mobile phones to determine transportation modes. ACM Trans. Sen. Netw., 6(2):13:1-13:27.
  16. Rodrigues, C. (2015). Smartphone-based inertial navigation system for bicycles. Master's thesis, Faculdade de Engenharia da Universidade do Porto.
  17. Silva, J. R. C. (2013). Smartphone based human activity prediction. Master's thesis, Faculdade de Engenharia da Universidade do Porto.
  18. Trier, Ø. D., Jain, A. K., and Taxt, T. (1996). Feature extraction methods for character recognition-a survey. Pattern Recognition, 29(4):641-662.
  19. Ture, M., Tokatli, F., and Kurt, I. (2009). Using kaplanmeier analysis together with decision tree methods (c&rt, chaid, quest, c4. 5 and id3) in determining recurrence-free survival of breast cancer patients. Expert Systems with Applications, 36(2):2017-2026.
  20. Xiao, L., He, B., Koster, A., Caserotti, P., Lange-Maia, B., Glynn, N. W., Harris, T., and Crainiceanu, C. M. (2014). Movement prediction using accelerometers in a human population.

Paper Citation

in Harvard Style

Figueira C., Matias R. and Gamboa H. (2016). Body Location Independent Activity Monitoring . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 190-197. DOI: 10.5220/0005699601900197

in Bibtex Style

author={Carina Figueira and Ricardo Matias and Hugo Gamboa},
title={Body Location Independent Activity Monitoring},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},

in EndNote Style

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Body Location Independent Activity Monitoring
SN - 978-989-758-170-0
AU - Figueira C.
AU - Matias R.
AU - Gamboa H.
PY - 2016
SP - 190
EP - 197
DO - 10.5220/0005699601900197