Analyzing Human Motion using Inertial Sensors

From ISLAB/CAISR
Title Analyzing Human Motion using Inertial Sensors
Summary Develop an algorithm that can detect walking events from accelerometers positioned at different parts of the body.
Keywords inertial sensors, gait
TimeFrame
References J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. & Phys., vol. 32, no. 6, pp. 545–552, 2010.

J. J. Kavanagh and H. B. Menz, “Accelerometry: A technique for quantifying movement patterns during walking,” Gait & Posture, vol. 28, no. 1, pp. 1–15, 2008.

D. Lai, R. Begg, and M. Palaniswami, “Computational intelligence in gait research: A perspective on current app. And future challenges,” Info. Tech. in Biomed., IEEE Trans. on, vol. 13, no. 5, pp. 687–702, 2009.

Prerequisites Background in signal analysis and programming are required. Interest in inertial sensors is a bonus.
Author
Supervisor Siddhartha Khandelwal, Nicholas Wickström
Level Master
Status Open

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Background:

Human walking (gait) consists of three primary components: locomotion, balance and ability to adapt to the environment. Measuring fundamental gait events of Heel-Strike (when the heel of the foot strikes the ground) and Toe-Off (when the toe leaves the ground) is of vital importance in diagnosis and assessment of gait disorders. Many industrial applications such as functional electrical simulation systems, orthotics, etc. require accurately measuring these events in real-time. Moreover, continuous monitoring of these events can help in assessing the human body’s response to drug therapy making it extremely valuable for the drug industry.


Project Description:

The goal of this project is to develop an algorithm that can detect gait events of HS and TO from accelerometers positioned at different parts of the body. As this is a highly active research area right now, there is a high probability of this project leading to a research publication in a reputed conference or journal.

Activity Plan:

WP1 Study state of the art algorithms for gait event detection from different body parts.

WP2 Develop an algorithm using accelerometers positioned at different parts of the body and validate it on real-world data.