Artificial Neural Networks (ANN) Applied for Gait Classification and Physiotherapy Monitoring in Post Stroke Patients

1.1 Overview of the problem Humans have an innate predisposition for ambulation (walking). The motor neuron stimulation involved in ambulation is generated by a natural neural network located in the spinal cord, known as the central pattern generator for locomotion. This network is strongly influenced both by super-spinal structures situated mainly in the hypothalamus and brainstem, and by signals coming from various types of peripheral receptors (Carter & Page 2009). To facilitate research and analysis, free gait in humans is traditionally divided into phases and cycles. Each full gait cycle comprises two individual steps; a single step consists of a stance phase and a swing phase. The gait cycle includes a stage of single limb stance (when the body rests on a single lower extremity) and a double limb stance (on both lower extremities). Kinematic gait analysis assumes a simplified, 15-segment model of the human body (feet, shins, thighs, forearms, upper arms, hands, head, torso, and pelvis) (Blaszczyk 2004). There are two kinds of basic parameters adopted for gait modelling and routine testing of ambulation in healthy and disabled individuals: spatial values of motion (including step length, velocity of the body mass centre, progressions of changes in joint angles, body mass oscillations) and dynamic values of gait mechanics (most often including ground reaction forces in 3 planes and the distribution of foot forces on the ground). These physical values are measured in parallel with bioelectric muscle activity (EMC), registered by surface electrodes as a subject walks (Perry & Burnfield, 2010). Correct ambulation requires the precise integration of practically all the systems of the human body. When one of the elements, especially a motor organ, is damaged as a consequence of injury, degeneration, or deformation, this immediately finds reflection in divergences of the above parameters from normative values, which is in practice described as pathological gait (Perry & Burnfield, 2010). The field of clinical biomechanics therefore

[1]  J Grigsby,et al.  Simulated neural networks to predict outcomes, costs, and length of stay among orthopedic rehabilitation patients. , 1994, Archives of physical medicine and rehabilitation.

[2]  W. Alves,et al.  Ischemic stroke: relation of age, lesion location, and initial neurologic deficit to functional outcome. , 1998, Archives of physical medicine and rehabilitation.

[3]  L. Ohno-Machado,et al.  Neural network applications in physical medicine and rehabilitation. , 1999, American journal of physical medicine & rehabilitation.

[4]  C. Benaïm,et al.  [Postural balance following stroke: towards a disadvantage of the right brain-damaged hemisphere]. , 1999, Revue neurologique.

[5]  Yu-Cheng Pei,et al.  Foot contact pattern analysis in hemiplegic stroke patients: an implication for neurologic status determination. , 2004, Archives of physical medicine and rehabilitation.

[6]  S. Olney,et al.  Hemiparetic gait following stroke. Part I: Characteristics , 1996 .

[7]  Sharon Kinsella,et al.  Gait pattern categorization of stroke participants with equinus deformity of the foot. , 2008, Gait & posture.

[8]  V. L. Nickel,et al.  Gait parameters following stroke: a practical assessment. , 1995, Journal of rehabilitation research and development.

[9]  J. Pélissier,et al.  Posture et équilibre après accident vasculaire cérébral : désavantage de l'hémisphère droit , 2008 .

[10]  R Wolfe,et al.  Sagittal gait patterns in spastic diplegia. , 2004, The Journal of bone and joint surgery. British volume.

[11]  Amy L Shortal.  Recovery of Walking Function in Stroke Patients: The Copenhagen Stroke Study , 1996 .

[12]  Hans H.C.M. Savelberg,et al.  Assessment of the horizontal, fore-aft component of the ground reaction force from insole pressure patterns by using artificial neural networks , 1999 .

[13]  W. Fries,et al.  Motor recovery following capsular stroke. Role of descending pathways from multiple motor areas. , 1993, Brain : a journal of neurology.

[14]  Richard W. Bohannon,et al.  Rehabilitation goals of patients with hemiplegia , 1988 .

[15]  Dejan Popović,et al.  Automatic vs hand-controlled walking of paraplegics. , 2003, Medical engineering & physics.

[16]  H. Duvernoy.  The Human Brain , 1999, Springer Vienna.

[17]  K Aminian,et al.  Incline, speed, and distance assessment during unconstrained walking. , 1995, Medicine and science in sports and exercise.

[18]  R. Burdett,et al.  Gait comparison of subjects with hemiplegia walking unbraced, with ankle-foot orthosis, and with Air-Stirrup brace. , 1988, Physical therapy.

[19]  B. Volpe,et al.  Improved functional outcome in patients with hemorrhagic stroke in putamen and thalamus compared with those with stroke restricted to the putamen or thalamus. , 2000, Stroke.

[20]  P Bach-y-Rita,et al.  Brain plasticity as a basis of the development of rehabilitation procedures for hemiplegia. , 1981, Scandinavian journal of rehabilitation medicine.

[21]  M. Dominkus,et al.  Transcranial electrical motor evoked potentials as a prognostic indicator for motor recovery in stroke patients. , 1990, Journal of neurology, neurosurgery, and psychiatry.

[22]  F Van Lente,et al.  Diagnostic accuracy of pancreatic enzymes evaluated by use of multivariate data analysis. , 1993, Clinical chemistry.

[23]  Dejan B Popovic,et al.  Restitution of reaching and grasping promoted by functional electrical therapy. , 2002, Artificial organs.

[24]  Liberson Wt,et al.  Functional electrotherapy: stimulation of the peroneal nerve synchronized with the swing phase of the gait of hemiplegic patients. , 1961, Archives of physical medicine and rehabilitation.

[25]  W. Garraway,et al.  The Natural History of Hemispheric and Brainstem Infarction in Rochester, Minnesota , 1984, Stroke.

[26]  Hongyin Lau,et al.  The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot. , 2008, Gait & posture.

[27]  Jin-Shin Lai,et al.  Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic , 2004, Journal of medical engineering & technology.

[28]  H. Damasio,et al.  A computed tomographic guide to the identification of cerebral vascular territories. , 1983, Archives of neurology.

[29]  Uta Frith,et al.  The Human Brain Book , 2009 .

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

[31]  R. Prescott,et al.  Predicting functional outcome following acute stroke using a standard clinical examination. , 1982, Stroke.

[32]  U. Bogataj,et al.  The rehabilitation of gait in patients with hemiplegia: a comparison between conventional therapy and multichannel functional electrical stimulation therapy. , 1995, Physical therapy.

[33]  T. Oberg,et al.  Gait in subjects with hemiplegia , 2004 .

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

[35]  J D Habbema,et al.  The diagnostic value of computer-assisted primary cervical smear screening: a longitudinal cohort study. , 1999, Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc.

[36]  J. Daly,et al.  Answering the Call: The Influence of Neuroimaging and Electrophysiological Evidence on Rehabilitation , 2007, Physical Therapy.

[37]  Yea-Ru Yang,et al.  Effects of Functional Electric Stimulation on Upper Limb Motor Function and Shoulder Range of Motion in Hemiplegic Patients , 2002, American journal of physical medicine & rehabilitation.

[38]  J. G. Barton,et al.  Development of a connectionist expert system to identify foot problems based on under-foot pressure patterns. , 1995, Clinical biomechanics.

[39]  M. Goffredo,et al.  A neural tracking and motor control approach to improve rehabilitation of upper limb movements , 2008, Journal of NeuroEngineering and Rehabilitation.

[40]  JoAnne K. Gronley,et al.  Classification of walking handicap in the stroke population. , 1995, Stroke.

[41]  JoAnne K. Gronley,et al.  Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke. , 2003, Gait & posture.

[42]  M. Giroud,et al.  Clinical and topographical range of callosal infarction: a clinical and radiological correlation study. , 1995, Journal of neurology, neurosurgery, and psychiatry.

[43]  Chao Qian,et al.  Population , 1940, State Rankings 2020: A Statistical View of America.

[44]  M. Grasso,et al.  Rehabilitation of Walking With Electromyographic Biofeedback in Foot‐Drop After Stroke , 1994, Stroke.

[45]  R C Quijano,et al.  Prediction of valve-related complications for artificial heart valves using adaptive neural networks: a preliminary study. , 1993, The Journal of heart valve disease.

[46]  Ciara M O'Connor,et al.  Automatic detection of gait events using kinematic data. , 2007, Gait & posture.

[47]  Ching-yi Wu,et al.  Gait Performance with Compensatory Adaptations in Stroke Patients with Different Degrees of Motor Recovery , 2003, American journal of physical medicine & rehabilitation.

[48]  M. Wong,et al.  Brain lesion size and location: effects on motor recovery and functional outcome in stroke patients. , 2000, Archives of physical medicine and rehabilitation.

[49]  Y. Laufer,et al.  Standing Balance and Functional Recovery of Patients with Right and Left Hemiparesis in the Early Stages of Rehabilitation , 2003, Neurorehabilitation and neural repair.

[50]  S. Simon,et al.  Gait Pattern in the Early Recovery Period after Stroke* , 1996, The Journal of bone and joint surgery. American volume.

[51]  Silvia Conforto,et al.  A biologically inspired neural network controller for ballistic arm movements , 2007, Journal of NeuroEngineering and Rehabilitation.

[52]  D W Grieve,et al.  The use of neural networks to recognize patterns of human movement: gait patterns. , 1995, Clinical biomechanics.

[53]  C. Allen.  Predicting the outcome of acute stroke: a prognostic score. , 1984, Journal of neurology, neurosurgery, and psychiatry.

[54]  S K Rogers,et al.  Artificial neural networks for early detection and diagnosis of cancer. , 1994, Cancer letters.

[55]  Richard W. Bohannon,et al.  Importance of four variables of walking to patients with stroke , 1991, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation.

[56]  F F Nobre,et al.  Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait. , 2010, Journal of biomechanics.

[57]  E Knutsson,et al.  Different types of disturbed motor control in gait of hemiparetic patients. , 1979, Brain : a journal of neurology.

[58]  T Brott,et al.  Measurements of acute cerebral infarction: lesion size by computed tomography. , 1989, Stroke.

[59]  Alfred D. Grant.  Gait Analysis: Normal and Pathological Function , 2010 .

[60]  S. Olney,et al.  Hemiparetic gait following stroke. Part II: Recovery and physical therapy , 1996 .

[61]  J. Eng,et al.  Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. , 2004, Gait & posture.

[62]  Pamela W. Duncan,et al.  Similar Motor Recovery of Upper and Lower Extremities After Stroke , 1994, Stroke.

[63]  K. Kaczmarczyk,et al.  Gait classification in post-stroke patients using artificial neural networks. , 2009, Gait & posture.

[64]  K. Furie,et al.  Lesion Characteristics, NIH Stroke Scale, and Functional Recovery After Stroke , 2007, American journal of physical medicine & rehabilitation.

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

[66]  G. E. Smith.  The Human Brain , 1924, Nature.

[67]  J. Perry,et al.  Gait Analysis , 2024 .

[68]  G. Baer,et al.  The recovery of walking ability and subclassification of stroke. , 2001, Physiotherapy research international : the journal for researchers and clinicians in physical therapy.

[69]  Kara K Patterson,et al.  Association Between Gait Asymmetry and Brain Lesion Location in Stroke Patients , 2009, Stroke.

[70]  T Sinkjaer,et al.  Kinematic and kinetic analysis of the walking pattern in hemiplegic patients with foot-drop using a peroneal nerve stimulator. , 2000, Clinical biomechanics.

[71]  A. Prochazka,et al.  A functional electric stimulation-assisted exercise therapy system for hemiplegic hand function. , 2004, Archives of physical medicine and rehabilitation.

[72]  Yukihiko Fujii,et al.  Cortical reorganization in patients with subcortical hemiparesis: neural mechanisms of functional recovery and prognostic implication. , 2003, Journal of neurosurgery.

[73]  J. G. Barton,et al.  An application of neural networks for distinguishing gait patterns on the basis of hip-knee joint angle diagrams , 1997 .

[74]  Jose L. Martinez,et al.  Walking recovery after an acute stroke: assessment with a new functional classification and the Barthel Index. , 2005, Archives of physical medicine and rehabilitation.

[75]  Lucila Ohno-Machado,et al.  Comparison of multiple prediction models for ambulation following spinal cord injury , 1998, AMIA.

[76]  Dorothy F. Edwards,et al.  Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage , 1999, Neurology.

[77]  T. Olsen,et al.  Compensation in recovery of upper extremity function after stroke: the Copenhagen Stroke Study. , 1994, Archives of physical medicine and rehabilitation.

[78]  Hailong Zhu,et al.  Support vector machine for classification of walking conditions of persons after stroke with dropped foot. , 2009, Human movement science.

[79]  S H Holzreiter,et al.  Assessment of gait patterns using neural networks. , 1993, Journal of biomechanics.

[80]  T Chau,et al.  A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. , 2001, Gait & posture.

[81]  Matt Carter,et al.  Minds and Computers , 2007 .

[82]  R Lafuente,et al.  Design and test of neural networks and statistical classifiers in computer-aided movement analysis: a case study on gait analysis. , 1998, Clinical biomechanics.

[83]  Adam Miller,et al.  Gait event detection using a multilayer neural network. , 2009, Gait & posture.

[84]  J S Higginson,et al.  Two simple methods for determining gait events during treadmill and overground walking using kinematic data. , 2008, Gait & posture.

[85]  B. H. Blott,et al.  Review of neural network applications in medical imaging and signal processing , 1992, Medical and Biological Engineering and Computing.