Long-Short Term Neural Network Analysis of Center of Pressure of Gait

Detectionofvasculardementiainearlystagesofcognitiveimpairmentisdifficulttodoinaclinical settingsincetheearliestchangesareoftendiscreteandphysiologicalinnature.Onemajoraspectof thisisgaitpatterns.Thisprojectutilizesforce-sensingplatforms,motioncapture,andEMGsensorsto unobtrusivelycollectbiometricdatafromanindividual’swalkinggaitpatterns.Thedataparameters gatheredwerecenterofpressure,gaitphaseandendofunloading/toe-ffevents.Byquantifyingand analyzingmachinelearningalgorithms,specificallydeeplearningtime-seriesbasedmodels,onset patternsofvasculardementiaareexploredwithanoverarchinggoalofcreatingasystemthatwill assistinunderstandinganddiagnosingcasesofvasculardementia.Theproposedsystemprovidesa toolforwhichgaitcanbeanalyzedandcomparedoveralongperiodoftimeandopensopportunity to increased personalization in health monitoring and disease diagnosis and provides an avenue toincreasepatient-centricityofmedicalcare.Sincegaitisoneoftheearlypredictorsofvascular dementia,wedevelopedalongshort-termneuralnetworktopredictthegaitvariationsfromwhich wecanpredicttheonsetofvasculardementia. KEywordS Center Of Pressure, Gait, Machine Learning Algorithms, Neural Network Force, Vascular Dementia, Walking Patterns

[1]  Matteo Gadaleta,et al.  IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks , 2016, Pattern Recognit..

[2]  Jeffrey Kaye,et al.  The trajectory of gait speed preceding mild cognitive impairment. , 2010, Archives of neurology.

[3]  B. Ferrell,et al.  Fatigue in an Older Population , 2000, Journal of the American Geriatrics Society.

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[6]  M. Nixon,et al.  Automated Human Recognition by Gait using Neural Network , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

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

[8]  Zoubin Ghahramani,et al.  A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.

[9]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  B. L. Kalman,et al.  Why tanh: choosing a sigmoidal function , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[11]  Herman Buschke,et al.  Abnormality of gait as a predictor of non-Alzheimer's dementia. , 2002, The New England journal of medicine.

[12]  Denise C. Park,et al.  Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[13]  Carole Dufouil,et al.  Beyond mild cognitive impairment: vascular cognitive impairment, no dementia (VCIND) , 2009, Alzheimer's Research & Therapy.

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

[15]  A. Hofman,et al.  Trajectories of decline in cognition and daily functioning in preclinical dementia , 2016, Alzheimer's & Dementia.

[16]  Executive control function: a rational basis for the diagnosis of vascular dementia. , 1999, Alzheimer disease and associated disorders.

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

[18]  Julius Hannink,et al.  Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks , 2016, IEEE Journal of Biomedical and Health Informatics.

[19]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[20]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

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

[22]  R. Petersen,et al.  Mild cognitive impairment , 2006, The Lancet.

[23]  P. McKeon,et al.  Balance training and center-of-pressure location in participants with chronic ankle instability. , 2015, Journal of athletic training.

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