Analysis of Foot-pressure Data to Classify Mobility Pattern

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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eISSN: 1178-5608

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VOLUME 7 , ISSUE 5 (December 2014) > List of articles

Special issue ICST 2014

Analysis of Foot-pressure Data to Classify Mobility Pattern

Goutam Chakraborty / Tetsuhiro Dendou

Keywords : Foot pressure data, Resistive sensor, Fast Fourier Transform, Artificial Neural Network

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 5, Pages 1-6, DOI: https://doi.org/10.21307/ijssis-2019-119

License : (CC BY-NC-ND 4.0)

Published Online: 15-February-2020

ARTICLE

ABSTRACT

The pressure exerted by foot, while a person is standing still for a while or moving or doing any physical activity, is a rich source of information. The continuous signal obtained throughout the day, collected by pressure-sensors on shoe sole, could be analyzed to obtain simple to complex facts about the person’s health conditions and habits. It could be used to measure body-weight and balance, while the person is standing. It could as well be used to find the total calorie burnt during movement activities throughout the day. Varied applications would need different number of sensors spread over inside or outside the shoe-sole. In this work, we restrict our investigation to simple applications like, measuring the body weight when the person is standing still, or the speed when the person is moving, or whether she/he is climbing up or down the stairs. Our aim is to use as few sensors as possible, and the algorithm simple and efficient. For measuring body-weight and movement speed, we could achieve nearly 100% accuracy. We could also classify between climbing up or down the stairs with 100% accuracy. All these could be accomplished by a single or a pair of sensors. It is also revealed that the optimum location of the sensor/s for the highest accuracy varies from person to person.

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REFERENCES

[1] http://www.tekscan.com/plantar-fasciitis-pressure-mapping-williams

[2] Tekscan, Inc., Products and Applications, South Boston, MA. 021271309, USA, http://www.tekscan.com/medical/system-fscan1.html

[3] Goutam Chakraborty, Tetsuhiro Dendou, ” How much information could be revealed by analyzing data from pressure sensors attached to shoe insole”, IEEE International conference on Instrumentation and Measurements (I2MTC), Graz, Austria, 2012.

[4] Alan V. Oppenheim, Ronald W. Schafer, ”Digital Signal Processing Paperback”, Prentice Hall.

[5] Harry JD, Niemi JB, Priplata AA, Collins JJ., Balancing act - noise based sensory enhancement technology, IEEE Spectrum. 2005; pp:36-41.

[6] Christopher M. Bishop, Neural Networks for Pattern Recognition, Cladrendon Press, Oxford, 1995.

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