Abstract
The chapter presents a study on an electromyography-based wearable system for fall risk assessment. It has been focused especially on the electrical activity analysis of the user’s lower limb muscles in relation to his body movement. For that purpose four wireless electromyography probes (sEMG) have been placed on the Gastrocnemius/Tibialis muscles and an accelerometer-equipped t-shirt has been worn during the Activities of Daily Living (ADLs) and fall events simulations. The results obtained have shown that the simultaneous contraction of the muscles considered appear relevant immediately after the starting of the imbalance condition, when the vertical velocity of the user’s body is too low for the commonly used inertial-based pre-fall detection systems. So an sEMG-based platform should be suitable to realize a more efficient platform to prevent the injures due to the fall. The mean lead-time measured, in controlled condition, is more than 750 ms with performance in terms of sensitivity and specificity more than 75%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
M.C. Chung, K.J. McKee, C. Austin, H. Barkby, H. Brown, S. Cash, J. Ellingford, L. Hanger, T. Pais, Posttraumatic stress disorder in older people after a fall. Int. J. Geriatr. Psychiatry. 24(9) 955–64 (September 2009)
F. Bagalà, C. Becker, A. Cappello, L. Chiari, K. Aminian, J.M. Hausdorff, W. Zijlstra, J. Klenk, Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7, e37062 (2012)
G. Rescio, A. Leone, and P. Siciliano, Supervised expert system for wearable mems accelerometer-based fall detector. J. Sens. vol. 2013, Article ID 254629, p. 11 (2013)
G. Wu, Distinguishing fall activities from normal activities by velocity characteristics. J. Biomech. 33(11) 1497–1500 (2000)
C. Pylatiuk, M. Muller-Riederer, A. Kargov, S. Schulz, O. Schill, M. Reischl, G. Bretthauer, Comparison of surface EMG monitoring electrodes for long-term use in rehabilitation device control. in IEEE International Conference on Rehabilitation Robotics, ICORR 2009, (2009) pp. 300–304
S.M. Lee, H.J. Byeon, J.H. Lee, D.H. Baek, K.H. Lee, J.S. Hong, S.-H. Lee, Self-adhesive epidermal carbon nanotube electronics for tether-free long-term continuous recording of biosignals. Sci Rep. 4, 6074 (2014)
A. K. Bourke, P. W. J. van de Ven, A. E. Chaya, G. M. OLaighin and J. Nelson, The design and development of a long-term fall detection system incorporated into a custom vest for the elderly. in 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, 2008, pp. 2836–2839
Y. He, Y. Li, Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone. Int. J. Distributed Sen. Networks. vol. 2013, Article ID 481580, p. 10 (2013)
G. Rescio, A. Leone, A. Caroppo, F. Casino, P. Siciliano, A minimally invasive electromyography-based system for pre-fall detection. Int. J. Eng. Inno. Technol. (IJEIT), 5(6) (2015)
A. Phinyomark, G. Chujit, P. Phukpattaranont, C. Limsakul, H. Hu, A preliminary study assessing time-domain EMG features of classifying exercises in preventing falls in the elderly. in 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2012, pp. 1,4, 16–18
B. Horsak, et al., A muscle co-contraction around the knee when walking with unstable shoes. J Electromyogr Kinesiol. 25 (2015)
A.K. Bourke, K.J. O’donovan, G. Olaighin, The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls. Med. Eng. Phys. 30(7), 937–946 (2008)
N. Noury, P. Rumeau, A.K. Bourcke, G. Olaighin, J.E. Lundy, A proposal for the classification and evaluation of fall detectors. IRBM 29(6), 340–349 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Leone, A., Rescio, G., Caroppo, A., Siciliano, P. (2018). Wireless Electromyography Technology for Fall Risk Evaluation. In: Andò, B., Baldini, F., Di Natale, C., Marrazza, G., Siciliano, P. (eds) Sensors. CNS 2016. Lecture Notes in Electrical Engineering, vol 431. Springer, Cham. https://doi.org/10.1007/978-3-319-55077-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-55077-0_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55076-3
Online ISBN: 978-3-319-55077-0
eBook Packages: EngineeringEngineering (R0)