TY - GEN
T1 - Gait event detection through neuromorphic spike sequence learning
AU - Lee, Wang Wei
AU - Yu, Haoyong
AU - Thakor, Nitish V.
PY - 2014/9/30
Y1 - 2014/9/30
N2 - We present a novel sampling and processing method for detecting gait events from an insole pressure sensor. Inspired by how tactile data is processed in the brain, we propose the use of timing, instead of intensity, as our event detection feature. By sacrificing the need for accurate intensity measurements, it is possible to achieve superior temporal resolution, which is arguably more important given the need for timely feedback. In this paper, we demonstrate temporally accurate gait-event detection of 1.2±7ms (mean and standard deviation) for heel-strike and 0.2±14 ms for toe-off events compared to the reference system, and a success rate of above 97% in most trials, using only 1 bit of pressure information per channel. Our method thus has the potential to achieve much lower computational complexity and bandwidth, both of which are key to low-cost, portable solutions for prosthetics, exoskeletons or long-term gait monitoring applications.
AB - We present a novel sampling and processing method for detecting gait events from an insole pressure sensor. Inspired by how tactile data is processed in the brain, we propose the use of timing, instead of intensity, as our event detection feature. By sacrificing the need for accurate intensity measurements, it is possible to achieve superior temporal resolution, which is arguably more important given the need for timely feedback. In this paper, we demonstrate temporally accurate gait-event detection of 1.2±7ms (mean and standard deviation) for heel-strike and 0.2±14 ms for toe-off events compared to the reference system, and a success rate of above 97% in most trials, using only 1 bit of pressure information per channel. Our method thus has the potential to achieve much lower computational complexity and bandwidth, both of which are key to low-cost, portable solutions for prosthetics, exoskeletons or long-term gait monitoring applications.
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M3 - Conference contribution
AN - SCOPUS:84918543235
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 899
EP - 904
BT - "2014 5th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2014
A2 - Carloni, Raffaella
A2 - Masia, Lorenzo
A2 - Garcia-Aracil, Nicolas
A2 - Loureiro, Rui C. V.
A2 - Saltaren, Roque
A2 - Van der Kooij, Herman
A2 - Vanderborght, Bram
A2 - Zollo, Loredana
A2 - Agrawal, Sunil
A2 - Casadio, Maura
A2 - Siqueira, Adriano A. G.
A2 - Zariffa, Jose
A2 - Bianchi, Matteo
A2 - Formica, Domenico
A2 - Godfrey, Sasha Blue
A2 - Mattos, Leonardo
A2 - Lanari Bo, Antonio Padilha
A2 - Cleary, Kevin
A2 - Fumagalli, Matteo
A2 - Khalil, Islam S.M.
A2 - Munoz, Victor
A2 - Park, Hyung-Soon
A2 - Tsagarakis, Nikolaos
A2 - Lambercy, Olivier
A2 - Squeri, Valentina
A2 - Sabater-Navarro, Jose Maria
A2 - Ackermann, Marko
A2 - Ajoudani, Arash
A2 - Artemiadis, Panagiotis
A2 - Deshpande, Ashish
A2 - Rodriguez Cheu, Luis Eduardo
A2 - Stienen, Arno H.A.
A2 - Vitiello, Nicola
PB - IEEE Computer Society
T2 - 5th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2014
Y2 - 12 August 2014 through 15 August 2014
ER -