Gait event detection through neuromorphic spike sequence learning

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± 14ms 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.

[1]  B-J Hsue,et al.  GAIT TIMING EVENT DETERMINATION USING KINEMATIC DATA FOR THE TOE WALKING CHILDREN WITH CEREBREAL PALSY , 2007 .

[2]  Brian T. Smith,et al.  Application of a neuro-fuzzy network for gait event detection using electromyography in the child with Cerebral palsy , 2005, IEEE Transactions on Biomedical Engineering.

[3]  Xiaoqin Wang,et al.  Temporal and rate representations of time-varying signals in the auditory cortex of awake primates , 2001, Nature Neuroscience.

[4]  R. Johansson,et al.  Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. , 1984, Human neurobiology.

[5]  Gregory Cohen,et al.  Synthesis of neural networks for spatio-temporal spike pattern recognition and processing , 2013, Front. Neurosci..

[6]  Nicolas Brunel,et al.  Sensory neural codes using multiplexed temporal scales , 2010, Trends in Neurosciences.

[7]  James C. Wall,et al.  Accuracy and reliability of temporal gait measurement , 1994 .

[8]  S. Thorpe,et al.  Spike times make sense , 2005, Trends in Neurosciences.

[9]  N. V. Thakor,et al.  Bio-mimetic strategies for tactile sensing , 2013, 2013 IEEE SENSORS.

[10]  R. Johansson,et al.  First spikes in ensembles of human tactile afferents code complex spatial fingertip events , 2004, Nature Neuroscience.

[11]  Salim Ghoussayni,et al.  Assessment and validation of a simple automated method for the detection of gait events and intervals. , 2004, Gait & posture.

[12]  G. Lyons,et al.  The use of accelerometry to detect heel contact events for use as a sensor in FES assisted walking. , 2003, Medical engineering & physics.

[13]  R N Marshall,et al.  Algorithms to determine event timing during normal walking using kinematic data. , 2000, Journal of biomechanics.

[14]  A. J. del-Ama,et al.  Review of hybrid exoskeletons to restore gait following spinal cord injury. , 2012, Journal of rehabilitation research and development.

[15]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[16]  Paola Catalfamo,et al.  Detection of gait events using an F-Scan in-shoe pressure measurement system. , 2008, Gait & posture.

[17]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[18]  Michael J. Berry,et al.  The Neural Code of the Retina , 1999, Neuron.

[19]  S. Simon.  Gait Analysis, Normal and Pathological Function. , 1993 .

[20]  M. Hanlon,et al.  Real-time gait event detection using wearable sensors. , 2006, Gait & posture.

[21]  F. Ponulak,et al.  FPGA implementation of ReSuMe learning in Spiking Neural Networks , 2006 .

[22]  Glenn C. Turner,et al.  Oscillations and Sparsening of Odor Representations in the Mushroom Body , 2002, Science.

[23]  H.J. Stam,et al.  Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controls , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.