Smart Monitoring of User’s Health at Home: Performance Evaluation and Signal Processing of a Wearable Sensor for the Measurement of Heart Rate and Breathing Rate

Sara Casaccia, Filippo Pietroni, Andrea Calvaresi, Gian Marco Revel, Lorenzo Scalise

Abstract

Nowadays, the monitoring of users’ health status is possible by means of smart sensing devices at low-cost and with high measuring capabilities. Wearable devices are able to acquire multiple physiological and physical waveforms and are equipped with on-board algorithms to process these signals and extract the required quantities. However, the performance of such processing techniques should be evaluated and compared to different approaches, e.g. processing of the raw waveforms acquired. In this paper, the authors have performed a metrological characterization of a commercial wearable monitoring device for the continuous acquisition of physiological quantities (e.g. Heart Rate - HR and Breathing Rate - BR) and raw waveforms (e.g. Electrocardiogram - ECG). The aim of this work is to compare the performance of the on-board processing algorithms for the calculation of HR and BR with a novel approach applied to the raw signals. Results show that the HR values provided by the device are accurate enough (±2.1 and ±2.8 bpm in static and dynamic tests), without the need of additional processing. On the contrary, the implementation of the dedicated processing technique for breathing waveform allows to compute accurate BR values (±2.1 bpm with respect to standard equipment).

References

  1. Abascal, J. Ambient intelligence for people with disabilities and elderly people. ACM's Special Interest Group on Computer-Human Interaction (SIGCHI), Ambient Intelligence for Scientific Discovery (AISD) Workshop, Vienna, 2004.
  2. Abdallah, Z. S., Gaber, M. M., Srinivasan, B. & Krishnaswamy, S. 2015. Adaptive mobile activity recognition system with evolving data streams. Neurocomputing, 150, 304-317.
  3. Angarita, G. A., Natarajan, A., Gaiser, E. C., Parate, A., Marlin, B., Gueorguieva, R., Ganesan, D., Malison, R. T., 2015. A remote wireless sensor network/electrocardiographic approach to discriminating cocaine use. Drug and Alcohol Dependence in press.
  4. Appelboom, G., Camacho, E., Abraham, M. E., Bruce, S. S., Dumont, E. LP., Zacharia, B. E., D'amico, R., Slomian, J., Reginster, J. Y., Bruyère, O., 2014. Smart wearable body sensors for patient self-assessment and monitoring. Archives of Public Health, 72, 28.
  5. Bakhchina, A. V., Shishalov, I. S., Parin, S. B., Polevaya, S. A., 2014. The dynamic cardiovascular markers of acute stress. International Journal of Psychophysiology, 2, 230.
  6. Bayat, A., Pomplun, M. & Tran, D. A. 2014. A study on human activity recognition using accelerometer data from smartphones. Procedia Computer Science, 34, 450-457.
  7. Bianchi, W., Dugas, A. F., Hsieh, Y., Saheed, M., Hill, P., Lindauer, C., Terzis, A., Rothman, R. E., 2013. Revitalizing a vital sign: improving detection of tachypnea at primary triage. Annals of emergency medicine, 61, 37-43.
  8. Catal, C., Tufekci, S., Pirmit, E. & Kocabag, G. 2015. On the use of ensemble of classifiers for accelerometerbased activity recognition. Applied Soft Computing.
  9. Cosoli, G., Casacanditella, L., Pietroni, F., Calvaresi, A., Revel, G. M., Scalise, L., A novel approach for features extraction in physiological signals. In: IEEE, ed. Memea - Medical Measurements and Applications, 2015 Turin. 380-385.
  10. Deepika, A., Baruah, S., Shukla, D. P., Sathyaprabha, T. N., Devi, B. I., 2015. Demonstration of subclinical autonomic dysfunction following severe traumatic brain injury using serial heart rate variability monitoring. Autonomic Neuroscience: Basic and Clinical in press.
  11. Demiris, G., Skubic, M., Rantz, M., Keller, J., Aud, M., Hensel, B., He, Z., 2006. Smart home sensors for the elderly: a model for participatory formative evaluation. human-computer interaction, 6, 7.
  12. Ehmen, H., Haesner, M., Steinke, I., Dorn, M., Gövercin, M., Steinhagen-Thiessen, E., 2012. Comparison of four different mobile devices for measuring heart rate and ECG with respect to aspects of usability and acceptance by older people. Applied ergonomics, 43, 582-587.
  13. Hemalatha, C. S., Vaidehi, V., 2013. Frequent bit pattern mining over tri-axial accelerometer data streams for recognizing human activities and detecting fall. Procedia Computer Science, 19, 56-63.
  14. Hu, X., Liu, J., Wang, J., Xiao, Z., Yao, J., 2014. Automatic detection of onset and offset of QRS complexes independent of isoelectric segments. Measurement, 51, 53-62.
  15. Johnstone, J. A., Ford, P. A., HugheS, G., Watson, T., Garrett, A. T., 2012a. BioHarness™ multivariable monitoring device: part. I: validity. Journal of sports science & medicine, 11, 400.
  16. Johnstone, J. A., Ford, P. A., Hughes, G., Watson, T., Garrett, A. T., 2012b. BioHarness™ Multivariable Monitoring Device: Part. II: Reliability. Journal of sports science & medicine, 11, 409.
  17. Johnstone, J. A., Ford, P. A., Hughes, G., Watson, T., Garrett, A. T., 2012c. Field based reliability and validity of the BioHarness™ multivariable monitoring device. Journal of sports science & medicine, 11, 643.
  18. Josko, A. Discrete wavelet transform in automatic ECG signal analysis. Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE, 2007. IEEE, 1-3.
  19. Kristiansen, J., Korshøj, M., Skotte, J. H., Jespersen, T., Søgaard, K., Mortensen, O. S. & Holtermann, A. 2011. Comparison of two systems for long-term heart rate variability monitoring in free-living conditions-a pilot study. Biomedical engineering online, 10, 27.
  20. Lowe, S. A., Ólaighin, G., 2014. Monitoring human health behaviour in one's living environment: a technological review. Medical engineering & physics, 36, 147-168.
  21. Pan, J. & Tompkins, W. J. 1985. A real-time QRS detection algorithm. Biomedical Engineering, IEEE Transactions on, 230-236.
  22. Pantelopoulos, A. & Bourbakis, N. G. 2010. A survey on wearable sensor-based systems for health monitoring and prognosis. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40, 1-12.
  23. Parak, J., Tarniceriu, A., Renevey, P., Bertschi, M., Delgado-Gonzalo, R. & Korhonen, I. Evaluation of the beat-to-beat detection accuracy of PulseOn wearable optical heart rate monitor. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 2015. IEEE, 8099-8102.
  24. Parvaneh, S., Grewal, G. S., Grewal, E., Menzies, R. A., Talal, T. K., Armstrong, D. G., Sternberg, E. & Najafi, B. 2014. Stressing the dressing: Assessing stress during wound care in real-time using wearable sensors. Wound Medicine, 4, 21-26.
  25. Revel, G. M., Pietroni, F., Zarli, A., Anfosso, A.,. Enhancing the built environment for ageing at home through ICTs and advanced sensing. . The Cities of Tomorrow: the Challenges of Horizon 2020, 2014 Torino, Italy.
  26. Riani, M., Perrotta, D., Torti, F., 2012. FSDA: A MATLAB toolbox for robust analysis and interactive data exploration. Chemometrics and Intelligent Laboratory Systems, 116, 17-32.
  27. Runova, E. V., Parin, S. B., Nekrasova, M. M., Bakhchina, A. V., Kovalchuk, A. V., Shyshalov, I. S., Polevaya, S. A., 2012. Monitoring and distant diagnostics of sportsmen's functional state based on information technologies and telemetry in the conditions of natural activity. International Journal of Psychophysiology, 85, 420-421.
  28. Sannino, G., De Falco, I., De Pietro, G., 2015. A supervised approach to automatically extract a set of rules to support fall detection in an mHealth system. Applied Soft Computing.
  29. Sixsmith, A., Sixsmith, J., 2000. Smart care technologies: meeting whose needs? Journal of telemedicine and telecare, 6, 190-192.
  30. Van andel, J., Ungureanu, C., Aarts, R., Leijten, F. & Arends, J. 2015. Using photoplethysmography in heart rate monitoring of patients with epilepsy. Epilepsy & Behavior, 45, 142-145.
  31. Vanderlei, L., Silva, R., Pastre, C., Azevedo, F. M. D. & Godoy, M. 2008. Comparison of the Polar S810i monitor and the ECG for the analysis of heart rate variability in the time and frequency domains. Brazilian Journal of Medical and Biological Research, 41, 854- 859.
Download


Paper Citation


in Harvard Style

Casaccia S., Pietroni F., Calvaresi A., Revel G. and Scalise L. (2016). Smart Monitoring of User’s Health at Home: Performance Evaluation and Signal Processing of a Wearable Sensor for the Measurement of Heart Rate and Breathing Rate . 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 175-182. DOI: 10.5220/0005694901750182


in Bibtex Style

@conference{biosignals16,
author={Sara Casaccia and Filippo Pietroni and Andrea Calvaresi and Gian Marco Revel and Lorenzo Scalise},
title={Smart Monitoring of User’s Health at Home: Performance Evaluation and Signal Processing of a Wearable Sensor for the Measurement of Heart Rate and Breathing Rate},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={175-182},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005694901750182},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Smart Monitoring of User’s Health at Home: Performance Evaluation and Signal Processing of a Wearable Sensor for the Measurement of Heart Rate and Breathing Rate
SN - 978-989-758-170-0
AU - Casaccia S.
AU - Pietroni F.
AU - Calvaresi A.
AU - Revel G.
AU - Scalise L.
PY - 2016
SP - 175
EP - 182
DO - 10.5220/0005694901750182