Application of a Multilayer Perceptron Neural Network for Classifying Software Platforms of a Powered Prosthesis through a Force Plate

The amalgamation of conventional gait analysis devices, such as a force plate, with a machine learning platform facilitates the capability to classify between two disparate software platforms for the same bionic powered prosthesis. The BiOM powered prosthesis is applied with its standard software platform that incorporates a finite state machine control architecture and a biomimetic software platform that uniquely accounts for the muscle modeling history dependence known as the winding filament hypothesis. The feature set is derived from a series of kinetic and temporal parameters derived from the force plate recordings. The multilayer perceptron neural network achieves 91% classification between the software platforms for the BiOM powered prosthesis conventional finite state machine control architecture and biomimetic software platform based on the force plate derived feature set.

[1]  P. Levinger,et al.  Support Vector Machines for detecting recovery from knee replacement surgery using quantitative gait measures , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  P. Komi,et al.  Muscle-tendon interaction and elastic energy usage in human walking. , 2005, Journal of applied physiology.

[3]  R Begg,et al.  A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. , 2005, Journal of biomechanics.

[4]  Robert LeMoyne,et al.  Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson's disease hand tremor. , 2015, Methods in molecular biology.

[5]  Jeremy L. Petak.  Performance testing of a musculoskeletal model controller for a robotic prosthesis , 2014 .

[6]  Ken Endo,et al.  Speed adaptation in a powered transtibial prosthesis controlled with a neuromuscular model , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

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

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  W. Herzog.  History dependence of skeletal muscle force production: implications for movement control. , 2004, Human movement science.

[10]  Robert LeMoyne,et al.  ADVANCES REGARDING POWERED PROSTHESIS FOR TRANSTIBIAL AMPUTATION , 2015 .

[11]  Robert LeMoyne,et al.  Implementation of Machine Learning for Classifying Hemiplegic Gait Disparity through Use of a Force Plate , 2014, 2014 13th International Conference on Machine Learning and Applications.

[12]  Nicholas P. Fey,et al.  The influence of energy storage and return foot stiffness on walking mechanics and muscle activity in below-knee amputees. , 2011, Clinical biomechanics.

[13]  Toshinori Munakata,et al.  Fundamentals of the New Artificial Intelligence - Neural, Evolutionary, Fuzzy and More, Second Edition , 2007, Texts in Computer Science.

[14]  Alena M. Grabowski,et al.  Bionic ankle–foot prosthesis normalizes walking gait for persons with leg amputation , 2012, Proceedings of the Royal Society B: Biological Sciences.

[15]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[16]  Robert LeMoyne,et al.  Implementation of a smartphone for evaluating gait characteristics of a trans-tibial prosthesis , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  D. Pai,et al.  Is titin a ‘winding filament’? A new twist on muscle contraction , 2012, Proceedings of the Royal Society B: Biological Sciences.

[18]  K. Webster,et al.  The application of support vector machines for detecting recovery from knee replacement surgery using spatio-temporal gait parameters. , 2009, Gait & posture.

[19]  Hartmut Geyer,et al.  Control of a Powered Ankle–Foot Prosthesis Based on a Neuromuscular Model , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  K. Nishikawa,et al.  A molecular basis for intrinsic muscle properties: implications for motor control. , 2013, Advances in experimental medicine and biology.