Smart Wearable Systems for Enhanced Monitoring and Mobility


Article Preview

The percentage of people over age 65 will shift from 12% to 20% nationwide while the average life expectancy for men and women of all races continues to rise, introducing a national and global concern for health related expenses. In particular, diminished stability leading to an increased risk of falling is on the forefront of medical expense projections. The World Health Organization (WHO) estimates there are 285 million suffering from visual impairment (39 million blind, 246 million low vision) worldwide. When adding the aging population with concomitant increases in life expectancy and the climbing rates of vision pathology, the numbers are even more dramatic. Blindness and low vision result in a host of social, emotional and health problems, often due to antecedent difficulties with mobility. This paper presents two smart wearable systems designed to enhance the mobility and monitoring of elderly and those with impaired vision. By using advances in sensors, actuators, and micro-electronics, these wearable systems acquire large amount of data, and with high speed data processing and pattern recognition, provide feedback signals to those wearing them. These systems are self-contained and operate with an easily accessible battery power. Details of the design and analysis of these smart wearable systems are presented.



Edited by:

Pietro Vincenzini




R. A. Shoureshi et al., "Smart Wearable Systems for Enhanced Monitoring and Mobility", Advances in Science and Technology, Vol. 100, pp. 172-178, 2017

Online since:

October 2016




* - Corresponding Author

[1] Akyol, Falls in the elderly: what can be done? International Nursing Review 54 (2), 191–196.

[2] Alexandr, N. (2002) Falls. Chapter 20 . Available at: http: /www. merck. com.

[3] Carollo James J., M.D., Strategies for clinical motion analysis based on functional decomposition of the gait cycle. Physical Medicine and Rehabilitation Clinics of North America, 2002, 13: p.28.


[4] Department of Health and Human Services Report to Congress: Appropriateness of minimum nurse staffing ratios in nursing homes – Phase II final report (April 2002).

[5] Fuller, G. (2000) Falls in the Elderly, American Family Physician. 2000 Apr 1; 61(7): 2159-68, 2173-4.

[6] Hahn ME and Chou L-S. Can motion of individual body segments identify dynamic instability in the elderly? Clinical Biomechanics, 2003, 18: 737-744.


[7] Hill, K. (2002) Review: intrinsic and environmental risk factors modi-fication reduces falls in elderly people. Evidence-based Medicine, 7, 116–118.


[8] Maki BE and McIlroy WE. Postural control in the older adult. Clinical Geriatric Medicine. 1996, 12(4): 635-658.

[9] Okada, S, Hirakawa, K, Takada, Y and Kinoshita, H, Relationship between fear of falling and balancing ability during abrupt deceleration in aged women having similar habitual physical activities Eur J Appl Physiol, 2001, 85(6): pp.501-6.


[10] Rahmat A. Shoureshi, Sun Lim, Bio-Inspired Nervous System for Civil Structures, Journal of Smart Structures and System, Vol. 5, No. 2, (2009).


[11] Shoureshi RA, Lim SW, Dolev E, et al., Electro-magnetic-acoustic transducers for automatic monitoring and health assessment of transmission lines, Journal Of.

[12] Dynamic Systems Measurement And Control-Transactions Of The ASME. 2004, 126(2): 303-308.

[13] R. Shoureshi, Z. Hu, Tsukamoto-Type Neural Fuzzy Inference Network, Proceedings of the 2000 American Control Conference, Chicago, June (2000).


Fetching data from Crossref.
This may take some time to load.