Feedback control of coronal plane hip angle in paraplegic subjects using functional neuromuscular stimulation

Abstract

This paper reports on an investigation of feedback control of coronal plane posture in paraplegic subjects who stand using functional neuromuscular stimulation (FNS). A feedback control system directed at regulating coronal plane hip angle in neutral position was designed, implemented, and evaluated in two paraplegic subjects. The control system included sensor mounting and signal processing techniques, a two-stage feedback controller, stimulation hardware, and a set of percutaneous intramuscular electrodes. The feedback controller consisted of two-stages in cascade: a modified discrete-time proportional-integral-derivative (PID) stage and a nonlinear single-input, multiple-output stage to determine the stimulation to be sent to several muscles. The focus of this work was on evaluating the performance of the feedback controller by comparing the response of the feedback-controlled system to that of an open-loop stimulation system. In an evaluation based on temporal response characteristics the controlled system exhibited a 41% reduction in root-mean-squared (rms) error (where error is defined as the deviation from the desired angle), a 52% reduction in steady-state error, and a 22% reduction in hip compliance. In addition, the feedback-controlled system exhibited significant reductions in variability of these measures on several days. These results demonstrate the ability of the feedback controller to improve the temporal response characteristics of the FNS control system.

A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

“…In light of these challenges, researchers have explored several control strategies to develop effective NMES controllers; e.g., linear PID-based pure feedback methods (cf., Abbas and Chizeck, 1991;Lan et al, 1991a,b;Lynch and Popovic, 2012;Klauer et al, 2014; and the references therein), neural network (NN) based controllers (cf., Tong and Granat, 1999;Kordylewski and Graupe, 2001;Sepulveda, 2003;Zhang and Zhu, 2004;Giuffrida and Crago, 2005;Wang et al, 2013; and the references therein), and combined feedback and feedforward methods (Chang et al, 1997;Ferrarin et al, 2001;Chen et al, 2005;Ajoudani and Erfanian, 2009;Freeman et al, 2009;Freeman, 2014). Recently, Lyapunov-based techniques were utilized in Schauer et al (2005), Sharma et al (2009bSharma et al ( , 2011Sharma et al ( , 2012, and Downey et al (2015) to design NMES controllers and prove asymptotic stability for an uncertain nonlinear muscle model.…”

A Versatile Labview-Based Toolbox Design and Man-Machine Interface for the Electrical Stimulation System

“…It can be employed in various clinical applications such as preventing muscle atrophy, decreasing muscle spasm, improving local blood circulation and bone growth, relieving pain, cardiac pacing, and functional electrical stimulation (FES) [1][2][3][4][5][6][7]. In addition, the physical conditions of the human body will change after long-term electrical stimulation treatments, for instance, the muscle bulk, strength, and motor response will increase [8][9][10][11][12][13].…”

Discrete-Time Cycle-to-Cycle Fuzzy Logic Control of FES-Induced Swinging Motion

“…Lower limb model [4] The knee joint model input is the stimulation pulsewidth as would be delivered in practice by an electrical stimulator. The complete model of knee joint thus developed is utilized as platform for simulation of the system and development of control approaches.…”

“…The problem arises especially due to the existing parameter variations (e.g., muscle fatigue), inherent time-variance, and strong nonlinearities present in the neuromuscular-skeletal system or the plant to be controlled. Besides, in such open-loop control approach, the actual movement is not assessed in real time and any mechanism of adapting the stimulation pattern in response to unforeseen circumstances such as external perturbations or muscle spasms is absent [4]. These prominent problems can be resolved by having a suitable closed-loop adaptive control mechanism.…”