Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields

Antonio L. Alfeo, Mario G. C. A. Cimino, Gigliola Vaglini

2017

Abstract

Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capacity, and prevent diseases. It is known that human annotations of PAL can be affected by subjectivity and inaccuracy. Recently developed smart devices can allow a non-invasive, analytic, and continuous gathering of physiological signals. We present an innovative computational system fed by signals of heartbeat rate, wrist motion and pedometer sensed by a smartwatch. More specifically, samples of each signal are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects’ coordination mechanism, and is managed by computational units called stigmergic receptive fields (SRFs). SRFs, which compute the similarity between trails, are arranged in a stigmergic perceptron to detect a collection of micro-behaviours of the raw signal, called archetypes. A SRF is adaptive to subjects: its structural parameters are tuned by a differential evolution algorithm. SRFs are used in a multilayer architecture, providing further levels of processing to realize macro analyses in the application domain. As a result, the architecture provides a daily PAL, useful to detect behavioural shift indicating initial signs of disease or deviations in performance. As a proof of concept, the approach has been experimented on three subjects.

References

  1. Avvenuti, M., Cesarini, D., Cimino, M.G.C.A. (2013). MARS, a multi-agent system for assessing rowers' coordination via motion-based stigmergy, Sensors, MDPI, 13(9), 12218-12243.
  2. Abbate, S., Avvenuti, M., and Light, J. (2012). MIMS: a minimally invasive monitoring sensor platform. IEEE Sensors Journal, 12(3), 677-684.
  3. Barsocchi, P., Cimino, M.G.C.A., Ferro, E., Lazzeri, A., Palumbo, F. and Vaglini, G. (2015). Monitoring elderly behavior via indoor position-based stigmergy, Pervasive and Mobile Computing, Elsevier Science, 23, 26-42.
  4. Boletsis, C., McCallum, S., and Landmark, B. F. (2015, August). The use of smartwatches for health monitoring in home-based dementia care. In International Conference on Human Aspects of IT for the Aged Population (pp. 15-26). Springer International Publishing.
  5. Bonomi, A. G., Plasqui, G., Goris, A. H., and Westerterp, K. R. (2010). Estimation of Free Living Energy Expenditure Using a Novel Activity Monitor Designed to Minimize Obtrusiveness. Obesity, 18(9), 1845-1851.
  6. Cimino, M. G. C. A., Pedrycz, W., Lazzerini, B., Marcelloni, F. (2009). Using Multilayer Perceptrons as Receptive Fields in the Design of Neural Networks Neurocomputing, Elsevier Science, 72(10-12) 2536- 2548.
  7. Cimino, M. G. C. A., Lazzeri, A. and Vaglini, G., 2015, Improving the analysis of context-aware information via marker-based stigmergy and differential evolution, Proceeding of the international Conference on Artificial Intelligence and Soft Computing (ICAISC 2015), in Springer LNAI, Vol. 9120, Part II, pp. 1-12, 2015.
  8. Fontecha, J., Hervás, R., Sánchez, L., Navarro, F. J., and Bravo, J. (2011, December). A proposal for elderly frailty detection by using accelerometer-enabled smartphones. In 5th International Symposium of Ubiquitous Computing and Place Intelligence.
  9. Fontecha, J., Navarro, F. J., Hervás, R., and Bravo, J. (2013). Elderly frailty detection by using accelerometer-enabled smartphones and clinical information records. Personal and ubiquitous computing, 17(6), 1073-1083.
  10. Guiry, J. J., van de Ven, P., and Nelson, J. (2014). Multisensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices. Sensors, 14(3), 5687-5701.
  11. Hager, G. D. (2012). Task-directed sensor fusion and planning: a computational approach (Vol. 99). Springer Science and Business Media.
  12. Jansen, F. M., Prins, R. G., Etman, A., van der Ploeg, H. P., de Vries, S. I., van Lenthe, F. J., and Pierik, F. H. (2015). Physical activity in non-frail and frail older adults. PloS one, 10(4), e0123168.
  13. Parkka, J., Ermes, M., Antila, K., van Gils, M., Manttari, A., and Nieminen, H. (2007, August). Estimating intensity of physical activity: a comparison of wearable accelerometer and gyro sensors and 3 sensor locations. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1511-1514). IEEE.
  14. Zhu, J., Pande, A., Mohapatra, P., and Han, J. J. (2015, October). Using Deep Learning for Energy Expenditure Estimation with wearable sensors. In 2015 17th International Conference on E-health Networking, Application and Services (HealthCom) (pp. 501-506). IEEE.
Download


Paper Citation


in Harvard Style

Alfeo A., Cimino M. and Vaglini G. (2017). Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 724-730. DOI: 10.5220/0006253307240730


in Bibtex Style

@conference{icpram17,
author={Antonio L. Alfeo and Mario G. C. A. Cimino and Gigliola Vaglini},
title={Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={724-730},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006253307240730},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields
SN - 978-989-758-222-6
AU - Alfeo A.
AU - Cimino M.
AU - Vaglini G.
PY - 2017
SP - 724
EP - 730
DO - 10.5220/0006253307240730