Methods for Gait Analysis During Obstacle Avoidance Task

In this study, we present algorithms developed for gait analysis, but suitable for many other signal processing tasks. A novel general-purpose algorithm for extremum estimation of quasi-periodic noisy signals is proposed. This algorithm is both flexible and robust, and allows custom adjustments to detect a predetermined wave pattern while being immune to signal noise and variability. A method for signal segmentation was also developed for analyzing kinematic data recorded while performing on obstacle avoidance task. The segmentation allows detecting preparation and recovery phases related to obstacle avoidance. A simple kernel-based clustering method was used for classification of unsupervised data containing features of steps within the walking trial and discriminating abnormal from regular steps. Moreover, a novel algorithm for missing data approximation and adaptive signal filtering is also presented. This algorithm allows restoring faulty data with high accuracy based on the surrounding information. In addition, a predictive machine learning technique is proposed for supervised multiclass labeling with non-standard label structure.

[1]  Leslie S. Smith,et al.  Smoothing and thresholding in neuronal spike detection , 2006, Neurocomputing.

[2]  J. Dingwell,et al.  Use of an instrumented treadmill for real-time gait symmetry evaluation and feedback in normal and trans-tibial amputee subjects , 1996, Prosthetics and orthotics international.

[3]  S. Urry,et al.  Reliability of spatiotemporal and kinetic gait parameters determined by a new instrumented treadmill system , 2013, BMC Musculoskeletal Disorders.

[4]  R. Kress.  Numerical Analysis , 1998 .

[5]  Pan Du,et al.  Bioinformatics Original Paper Improved Peak Detection in Mass Spectrum by Incorporating Continuous Wavelet Transform-based Pattern Matching , 2022 .

[6]  Martin Wolf,et al.  An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals , 2012, Algorithms.

[7]  Girish Keshav Palshikar.  Simple Algorithms for Peak Detection in Time-Series , 2009 .

[8]  A. Mirelman,et al.  V-TIME: a treadmill training program augmented by virtual reality to decrease fall risk in older adults: study design of a randomized controlled trial , 2013, BMC Neurology.

[9]  Adriano O. Andrade,et al.  A simple, reliable method to determine the mean gait speed using heel markers on a treadmill , 2017, Computer methods in biomechanics and biomedical engineering.

[10]  G. Howells,et al.  A Peak-Trough Detection Algorithm Based on Momentum , 2008, 2008 Congress on Image and Signal Processing.

[11]  J. O'Loughlin,et al.  Incidence of and risk factors for falls and injurious falls among the community-dwelling elderly. , 1993, American journal of epidemiology.

[12]  R. C. Nelson,et al.  Biomechanics of overground versus treadmill running. , 1972, Medicine and science in sports.

[13]  R. Zivanovic,et al.  Automatic signal segmentation based on abrupt change detection for power systems applications , 2006, 2006 IEEE Power India Conference.

[14]  Jeannette R. Mahoney,et al.  Effect of Treadmill Training on Specific Gait Parameters in Older Adults With Frailty: Case Series , 2011, Journal of geriatric physical therapy.

[15]  Jeffrey M. Hausdorff,et al.  Addition of a non-immersive virtual reality component to treadmill training to reduce fall risk in older adults (V-TIME): a randomised controlled trial , 2016, The Lancet.

[16]  David Smith,et al.  A continuous wavelet transform algorithm for peak detection , 2008, Electrophoresis.

[17]  Julio Michael Stern,et al.  A Sequential Algorithm for Signal Segmentation , 2018, Entropy.

[18]  Nicola Lovecchio,et al.  Gait analysis on force treadmill in children: comparison with results from ground-based force platforms , 2017, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation.

[19]  P. Choong,et al.  Three-dimensional knee kinematic analysis during treadmill gait , 2017, Bone & joint research.

[20]  Kristin H. Jarman,et al.  A new approach to automated peak detection , 2003 .

[21]  Karin A Mack,et al.  Gender differences in seeking care for falls in the aged Medicare population. , 2012, American journal of preventive medicine.

[22]  A. Mirelman,et al.  Clinical Experience Using a 5-Week Treadmill Training Program With Virtual Reality to Enhance Gait in an Ambulatory Physical Therapy Service , 2014, Physical Therapy.

[23]  J. C. Wall,et al.  A kinematic study of long-term habituation to treadmill walking. , 1981, Ergonomics.

[24]  Joel W. Burdick,et al.  Spike detection using the continuous wavelet transform , 2005, IEEE Transactions on Biomedical Engineering.

[25]  J B Dingwell,et al.  A rehabilitation treadmill with software for providing real-time gait analysis and visual feedback. , 1996, Journal of biomechanical engineering.

[26]  Abdullah Ruhi Soylu,et al.  Fundamentals of Quantitative Gait Analysis , 2016 .

[27]  Sanei Saeid,et al.  A Novel Signal Segmentation Method based on Standard Deviation and Variable Threshold , 2011 .

[28]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[29]  Yijung Chung,et al.  Treadmill Training with Virtual Reality Improves Gait, Balance, and Muscle Strength in Children with Cerebral Palsy. , 2016, The Tohoku journal of experimental medicine.

[30]  A. Cereatti,et al.  Treadmill-virtual reality combined training program to improve gait in multiple sclerosis individuals , 2015, 2015 International Conference on Virtual Rehabilitation (ICVR).