Real-time Motion Analysis using Inertial Sensors
| Title | Real-time Motion Analysis using Inertial Sensors |
|---|---|
| Summary | Develop a real-time (online) algorithm that can detect walking events from accelerometer signals. |
| Keywords | real-time, gait, inertial sensors |
| TimeFrame | |
| References | Chia Bejarano, N.; Ambrosini, E.; Pedrocchi, A.; Ferrigno, G.; Monticone, M.; Ferrante, S., "A Novel Adaptive, Real-Time Algorithm to Detect Gait Events From Wearable Sensors," in Neural Systems and Rehabilitation Engineering, IEEE Transactions on , vol.23, no.3, pp.413-422, May 2015
J. Lee and E. Park, “Quasi real-time gait event detection using shank-attached gyroscopes,” Med. & Bio. Eng. & Comp., vol. 49, no. 6, pp. 707–712, 2011. |
| 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 |
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 a real-time algorithm that can detect gait events of HS and TO from accelerometer signals. 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 real-time algorithms for gait event detection.
WP2 Develop a real-time algorithm using accelerometer signals and validate it on real-world data.
