Application of a Multilayer Perceptron Neural Network for Classifying Software Platforms of a Powered Prosthesis Through a Force Plate
Robert LeMoyne, Senior Member, IEEE
Timothy Mastroianni
Post-Doctorate, Department of Biological Sciences Northern Arizona University Flagstaff, AZ 86011-5640 USA e-mail:
[email protected],
[email protected]
Independent Pittsburgh, PA 15243 USA e-mail:
[email protected]
Anthony Hessel
Kiisa Nishikawa
Graduate Student, Department of Biological Sciences Northern Arizona University Flagstaff, AZ 86011-5640 USA e-mail:
[email protected]
Regents' Professor, Department of Biological Sciences Northern Arizona University Flagstaff, AZ 86011-5640 USA e-mail:
[email protected]
Abstract— 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. Keywords- Powered Prosthesis, BiOM Powered Prosthesis, Multilayer Perceptron, Neural Network, Machine Learning, Gait Analysis
I.
INTRODUCTION
The incidence of lower limb transtibial amputation is increasing at a considerable rate. The development of mechanized prostheses capable of imparting mechanical energy about the terminal aspects of the stance phase of gait has been realized. One of the most evolved of this class of prosthesis in the context of research, development, testing, and evaluation is the BiOM powered prosthesis. Current versions of the BiOM powered prosthesis feature a control software platform that incorporates a finite state machine, which transitions from prescribed subphase to subphase of the gait cycle based on threshold bounds of predetermined sensor signal feedback [1, 2]. Alternative software platforms incorporating the neuromuscular features, such as the Hill muscle model, have been applied to powered prosthesis applications. These
versions of powered prostheses benefit from the capacity to demonstrate adaptive characteristics of natural gait, which constitutes a considerable improvement relative to passive prostheses [1, 3, 4]. With advances in the scope of muscle modeling new techniques, such as the winding filament hypothesis, can be applied which demonstrate considerable promise especially with regards to history dependence of muscle activation patterns [5, 6]. The winding filament hypothesis entails the intrinsic utility of incorporating the role of titin into the muscle model. Amending the function of titin into a muscle model enables inherent potential energy features for a more biomimetic interpretation of muscle activation properties [5, 6]. A machine learning strategy, such as a multilayer perceptron neural network, for classifying between two powered prosthetic software platforms would be beneficial to the broad domains of medical, clinical, rehabilitation engineering, and research aspects of prosthetic applications. Machine learning through a multilayer perceptron neural network is applied to differentiate between the feature set attributes regarding the software platforms of the conventional finite state machine and winding filament hypothesis for a BiOM powered prosthesis. Kinetic and temporal data for establishing the feature set is provided through the recordings of a force plate embedded in a gait platform. The objective of the research from the perspective of engineering proof of concept is to apply machine learning with a multilayer perceptron neural network to classify between two software platforms consisting of a conventional finite state machine and winding filament hypothesis for a BiOM powered prosthesis. II.
BACKGROUND
The BiOM powered prosthesis is uniquely equipped with the capability to emulate powered plantar flexion, which is an inherent characteristic of the terminal aspect of the stance phase of gait. Mechanized energy is imparted to the gait
cycle through a series elastic actuator [1, 7]. Further evolutions of the powered prosthesis have incorporated modifications regarding the software platform for the control architecture. These modifications have incorporated neuromuscular models, which rely on the standard Hill muscle model. The Hill muscle model has been instituted for approximating muscle force production over the scope of approximately three quarters of a century. The neuromuscular derived control software platform does emulate adaptive capacity, such as the modulation of torque output as a consequence of walking condition [1, 3, 4]. However, the Hill model notably lacks the capacity to account for muscle history dependence, which has been advocated as a prominent feature for a truly realistic muscle model simulation [1, 5, 6, 8]. Nishikawa et al. advocates the winding filament hypothesis as a basis for accounting for muscle history dependence. The concept represents an evolutionary extrapolation beyond the conventional Hill model. The functional role of titin is instrumental to the description of the winding filament hypothesis. A significant historical perspective is that titin was discovered many decades after the acknowledgement of the Hill muscle model. A unique feature of the winding filament hypothesis is the capacity of titin’s N2A region to bind to the actin thin filament with Ca2+ release and potential energy storage about titin’s PEVK region, especially with regards to rotation with respect to the actin thin filament [1, 5, 6]. The software platform constituting the winding filament hypothesis as a control architecture has been successfully implemented with the BiOM powered prosthesis [9]. In order the develop a feature set to conduct machine learning classification between the finite state machine and winding filament hypothesis derived software platforms the characteristic aspects of the force plate profile regarding gait should be clarified. The force plate records data from ground reaction force of the stance phase, which represents 60% of the gait cycle. The force profile during stance produces a distinctive set of attributes for a feature set. Stance initiation induces a rise to a preliminary maximum, which is known as brake. During the brake segment the ankle-foot complex decelerates with eccentric contraction. Then the force signal declines to a local minimum. The terminal phase of stance applies powered plantar flexion. During powered plantar flexion the force signal rises to another maximum designated as push off [1, 10, 11]. Recent endeavors have successfully applied machine learning platforms for the classification of gait. LeMoyne et al. accomplished 100% classification of a hemiplegic affected leg and unaffected leg through the use of force plate derived feature set consisting of kinetic and temporal attributes. Logistic regression was applied as the machine learning platform [12]. A considerable diversity of machine learning platforms are available for the feature set classification under consideration. Machine learning platforms have been applied to the classification of gait with regards to before and after knee surgery and to distinguish between gait patterns of distinctive age groups [13, 14, 15]. As the winding filament
hypothesis control architecture software platform places an emphasis on biomimetic qualities, a machine learning platform that is likewise biomimetic in nature would be desirable. The multilayer perceptron neural network constitutes a computational analog to the brain based on the brain’s fundamental element the neuron [16]. Neural networks have been applied in the context of conventional gait analysis for distinguishing between healthy and pathological gait [17]. Therefore the multilayer perceptron neural network has been selected as the machine learning platform to classify the feature set of the force plate recordings for the BiOM powered prosthesis conventional finite state machine and winding filament hypothesis derived software platforms. III.
METHODS AND MATERIAL
The study pertained to one subject with transtibial amputation for machine learning classification regarding disparate software platforms of a powered prosthesis through a multilayer perceptron neural network. The basis for the subject’s amputation was due to trauma. The experiment was approved through the Northern Arizona University Institutional Review Board (IRB). The feature set was derived through the use of an AMTI force plate embedded in a gait platform. The gait platform is illustrated in figure 1. The subject acclimated to the gait platform while establishing a starting position that enabled the residual limb with the prosthesis to contact the force plate for the full portion of the stance phase aspect of gait. The subject applied a self-selected gait speed of 1.4 meters per second based on the timed differential of the fixed displacement of the gait platform. The BiOM powered prosthesis incorporated two disparate software platforms, which facilitated the control architecture. The conventional software platform employed a finite state machine. The experimental platform utilized the winding filament hypothesis [1, 2, 9]. The subject provided 40 trials for both software platforms using the BiOM powered prosthesis. The force plate recording was post-processed into a feature set through MATLAB. The foundation for the kinetic and temporal attributes of the feature set were established through the previous research conducted by LeMoyne et al. [12]. The feature set consisted of five numeric attributes, and the attribute variable names are represented within the following square brackets. Kinetic aspects of the feature set were established by: • Maximum at brake [MaximumForceBrake] • Maximum at push off [MaximumForcePush] • Average of the stance force profile [AverageForce] Temporal aspects of the feature set based on: • Rise time to first maximum (brake) [BrakeTimeDifferential] • Fall time from second maximum (push off) [PushTimeDifferential] The post-processing through MATLAB developed an Attribute-Relation File Format (ARFF) to represent the feature set. The multilayer perceptron neural network was
IV.
Figure 1. Gait platform with embedded force plate. The BiOM powered prosthesis is mounted to the right leg of the subject.
Figure 2. Multilayer perception neural network for achieving classification of two software platform control architectures (conventional finite state machine (FSM) and winding filament hypothesis (WFH)).
Figure 3. Classification accuracy of the full feature set, kinetic attributes of the feature set, and temporal attributes of the feature set.
applied through the Waikato Environment for Knowledge Analysis (WEKA), which provides a considerable assortment of machine learning platforms. Ten-fold crossvalidation was applied to the feature set for machine learning classification [18, 19, 20].
RESULTS AND DISCUSSION
The multilayer perceptron neural network using WEKA achieved 91% classification with respect to the BiOM powered prosthesis contrasting two software platforms representing the control architecture: conventional finite state machine and winding filament hypothesis. The feature set was derived from the kinetic and temporal aspects of the subject’s stance phase during gait. The WEKA machine learning platform derived a graphic interpretation of the multilayer perceptron neural network illustrated with figure 2. The generated multilayer perceptron neural network consists of one hidden layer with three nodes. Further analysis of the feature set was conducted by applying the multilayer perceptron neural network to specifically kinetic attributes and temporal attributes. The kinetic attributes of the feature set consisted of maximum at brake, maximum at push off, and average of the stance force profile, and 85% classification was attained. In consideration of the temporal attributes of the feature set comprised of rise time to first maximum (brake) and fall time from second maximum (push off), and 78% classification was achieved. The classification results are summarized in figure 3 for the full feature set, kinetic attributes of the feature set, and temporal attributes of the feature set. The BiOM powered prosthesis facilitates the user’s gait with mechanically augmented powered plantar flexion during the terminal phase of stance for a more biologically natural gait. Walking is a characteristically repetitive task, for which people may take on the order of 1000’s of step per day. Powered prosthetic technologies have been advocated for mitigating chronic degenerative morbidities, such as arthritis, when contrasted to more traditional passive prostheses [1]. The powered prosthesis is capable of being equipped with multiple types of software platforms for control architectures that benefit the context of the application [1, 2, 3, 4, 7, 9]. The current research demonstrates the capacity to achieve considerable classification to distinguish between two disparate software platforms through the application of machine learning. In the future the amalgamation of machine learning platforms and gait analysis systems is envisioned to facilitate the acuity for prosthetic diagnostics. The diagnostic capacity of machine learning would likely advance the quality of available rehabilitation strategies for the person with an amputation and the assessment of efficacy. As a person with an amputation experiences modifications to lifestyle patterns machine learning applied with the use of gait analysis could identify appropriate retuning of software parameters and potentially suitable novel software platforms. Gait analysis tools are rapidly expanding into the context of mobile platforms, such as smartphones and portable media devices. These platforms facilitate the environments for which a person can be evaluated for gait status during rehabilitation and recovery. For example, a person can be readily assessed in the context of their home environment, rather than negotiating the logistics of a therapy appointment in a clinical facility. Machine learning has been envisioned as instrumentally augmenting the diagnostic impact of these
mobile platforms, such as smartphones and portable media devices [21]. Recently a smartphone has been integrated with a transtibial prosthesis as a gait analysis platform. The experimental and post-processing locations were remotely positioned [22]. The implication is the person with an amputation undergoing a therapy evaluation could provide the rehabilitation therapist with a series of trial samples to post-process remotely through a suitable machine learning platform. Based on the findings of the machine learning classification a refined and augmented therapy strategy could be provided. V.
CONCLUSION
Machine learning, such as a multilayer perceptron neural network, offers considerable potential with gait analysis systems, such as the conventional force plate, for distinguishing between disparate software platforms of the BiOM powered prosthesis. With the feature set of a force plate signal the multilayer perceptron neural network has achieved 91% classification with regards to a conventional finite state machine and winding filament hypothesis software platform control architecture for a BiOM powered prosthesis. The preliminary research implicates the potential for highly automated machine learning diagnostic acuity for the rehabilitation process regarding people with amputation. Machine learning is envisioned to provide an augmented and automated decision process, and a rehabilitation therapist can improve the quality and efficacy of a progressive therapy strategy for a person with an amputation using a powered prosthesis.
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