A Force Myography-Based System for Gait Event Detection in Overground and Ramp Walking

In this paper, we present a novel method to determine the heel strike (HS) and toe-off (TO) during overground (OG) and ramp walking, including the transition. The method utilizes force myography (FMG) signals from thighs while subjects walked on OG and ramp. Five adult male subjects wore a wireless FMG data acquisition system, developed in-house using force resistive sensors and electronic components. A heuristic approach for subject-dependent and terrain-independent model was developed to determine HS and TO in a given gait cycle in steady state and transition. The average error in HS determination was 9.66 ± 8.29, 9.38 ± 9.35, and 13.94 ± 18.95 ms, while TO was determined with an average error of 16.99 ± 18.12, 13.35 ± 15.10, and 17.29 ± 21.92 ms for OG, ramp, and transition, respectively. The proposed system is less expensive, simple to develop, and friendly to wear. The reported errors are comparable to previously reported errors using pressure sensitive insole, gyroscope, accelerometers, and electromyography, which are much complex and expensive in comparison to proposed FMG-based system. Although the tests were conducted on healthy subjects, the system promises to be generalizable to amputee and other pathological gaits also. While the tests were conducted on young adults at self-selected speeds, the system also promises to be generalizable for a wide range of walking speeds across the population.

[1]  Guido Pasquini,et al.  Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis , 2014, Sensors.

[2]  Nicholas P Fey,et al.  Anticipatory kinematics and muscle activity preceding transitions from level-ground walking to stair ascent and descent. , 2016, Journal of biomechanics.

[3]  E M Hennig,et al.  Heel to toe motion characteristics in Parkinson patients during free walking. , 2001, Clinical biomechanics.

[4]  Jinger S Gottschall,et al.  Neuromuscular strategies for the transitions between level and hill surfaces during walking , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[5]  S. Delp,et al.  The influence of muscles on knee flexion during the swing phase of gait. , 1996, Journal of biomechanics.

[6]  N. V. Thakor,et al.  Classification of gait phases from lower limb EMG: Application to exoskeleton orthosis , 2013, 2013 IEEE Point-of-Care Healthcare Technologies (PHT).

[7]  Andrea Parri,et al.  Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators , 2017, IEEE Transactions on Biomedical Engineering.

[8]  Deepak Joshi,et al.  A Novel Approach for Toe Off Estimation During Locomotion and Transitions on Ramps and Level Ground , 2016, IEEE Journal of Biomedical and Health Informatics.

[9]  R. Kram,et al.  The independent effects of gravity and inertia on running mechanics. , 2000, The Journal of experimental biology.

[10]  Nicola Vitiello,et al.  Automated detection of gait initiation and termination using wearable sensors. , 2013, Medical engineering & physics.

[11]  Conor J. Walsh,et al.  Multi-joint soft exosuit for gait assistance , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Rezaul K. Begg,et al.  Foot Plantar Pressure Measurement System: A Review , 2012, Sensors.

[13]  Deok-Hwan Kim,et al.  Real-time gait subphase detection using an EMG signal graph matching (ESGM) algorithm based on EMG signals , 2017, Expert Syst. Appl..

[14]  B. Day,et al.  Insights into the neural control of locomotion from walking through doorways in Parkinson's disease , 2010, Neuropsychologia.

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

[16]  S. Urry Plantar pressure-measurement sensors , 1999 .

[17]  J. Allum,et al.  Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. , 2006, Gait & posture.

[18]  John K. De Witt,et al.  Determination of toe-off event time during treadmill locomotion using kinematic data. , 2010 .

[19]  Qingshan She,et al.  EMG signals based gait phases recognition using hidden Markov models , 2010, The 2010 IEEE International Conference on Information and Automation.

[20]  Mamun Bin Ibne Reaz,et al.  Surface Electromyography Signal Processing and Classification Techniques , 2013, Sensors.

[21]  Ruud W. Selles,et al.  Automated Detection of Instantaneous Gait Events Using Time Frequency Analysis and Manifold Embedding , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Angelo M. Sabatini,et al.  Online Decoding of Hidden Markov Models for Gait Event Detection Using Foot-Mounted Gyroscopes , 2014, IEEE Journal of Biomedical and Health Informatics.

[23]  Jun-Young Jung,et al.  A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots , 2015, Sensors.

[24]  Hugh M Herr,et al.  Autonomous exoskeleton reduces metabolic cost of human walking during load carriage , 2014, Journal of NeuroEngineering and Rehabilitation.

[25]  Simona Crea,et al.  A Wireless Flexible Sensorized Insole for Gait Analysis , 2014, Sensors.

[26]  D Kotiadis,et al.  Inertial Gait Phase Detection for control of a drop foot stimulator Inertial sensing for gait phase detection. , 2010, Medical engineering & physics.

[27]  Angelo M. Sabatini,et al.  Assessment of walking features from foot inertial sensing , 2005, IEEE Transactions on Biomedical Engineering.

[28]  Robert J. Wood,et al.  Soft wearable motion sensing suit for lower limb biomechanics measurements , 2013, 2013 IEEE International Conference on Robotics and Automation.

[29]  A Rudins,et al.  Foot biomechanics during walking and running. , 1994, Mayo Clinic proceedings.

[30]  Tianjian Ji,et al.  FREQUENCY AND VELOCITY OF PEOPLE WALKING , 2005 .

[31]  Panagiotis K. Artemiadis,et al.  Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography , 2014, Front. Neurorobot..

[32]  W. Kraemer,et al.  FOOT STRIKE PATTERNS OF RUNNERS AT THE 15‐KM POINT DURING AN ELITE‐LEVEL HALF MARATHON , 2007, Journal of strength and conditioning research.

[33]  Don A. Yungher,et al.  Surface muscle pressure as a measure of active and passive behavior of muscles during gait. , 2011, Medical engineering & physics.

[34]  Arne Leijon,et al.  Continuous Hidden Markov Model for Pedestrian Activity Classification and Gait Analysis , 2013, IEEE Transactions on Instrumentation and Measurement.

[35]  Olivier Beauchet,et al.  Guidelines for clinical applications of spatio-temporal gait analysis in older adults , 2006, Aging clinical and experimental research.

[36]  Sneh Anand,et al.  ANFIS based knee angle prediction: An approach to design speed adaptive contra lateral controlled AK prosthesis , 2011, Appl. Soft Comput..

[37]  R. Baker Gait analysis methods in rehabilitation , 2006, Journal of NeuroEngineering and Rehabilitation.