Mourad Benoussaad, Katja Mombaur - IWR
Christine Azevedo-Costes - LIRMM, Montpellier, France
Drop foot is a symptom which frequently appears in hemiplegic patients but is also caused by other diseases such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) or muscular dystrophy. It is due to the inability of the foot to perform a dorsiflexion which result in non-natural walking motions dragging the toes on the ground during the swing phase. Drop foot correction (DFC) gathers techniques and solutions to overcome these problems. The classical one is the Ankle-Foot Orthosis (AFO), which consists in a rigid and permanent fixation of the ankle at a given position. It however induces a non-natural behavior of the foot during walking and doesn’t offer any therapeutic benefit to hemiplegic patients in terms of dorsiflexion recovery.
DFC using Functional Electrical Stimulation (FES) presents an attractive alternative. The aim is to provoke contractions of the tibialis anterior (TA) muscle to restore the dorsi-flexion capabilities. Classically, the stimulation signal applied to the muscle is predefined with a trapezoidal shape, activated by an on/off foot-switch sensor, when the heel-off is detected.
However, this predefined empirical stimulation pattern induces more muscular fatigue because it doesn’t consider any information about the ankle-foot behavior and inter-subject variability. In addition, this empirical solution suffers
from the lack of adaptability to changes of the stimulated muscle behavior (muscle fatigue) and of the environment (stairs, sloping ground). On the other hand, the use of a on/off f
oot-switch sensor is not accurate enough for an efficient drop foot correction.
The purpose of this project is the application of optimal control techniques to improve the electrical stimulation patterns for the drop foot correction during the swing phase gait by controlling the foot orientation. The optimal computations consider the subject specific model and the inertial measurements of the foot and shank, obtained through the Inertial Measurement Unit (IMU) sensors.
We are investigating both
- Offline optimal control to investigate optimal stimulation patterns minimizing different objective functions for the dynamical model (minimizing e.g. criteria related to energy consumption or fatigue)
- Nonlinear model predictive control (NMPC) to optimize these motions online using state estimation based on imperfect sensor data.
Special attention is paid to the definition of the most relevant constraints to guarantee e.g. swing foot clearance to the efficient processing and fusion of the inertial data.
R. Heliot, K. Mombaur, and C. Azevedo-Coste, “Online CPG-based gait monitoring and optimal control of the ankle joint for assisted walking in hemiplegic subjects,” in "Modeling, Simulation and Optimization of Bipedal Walking", COSMOS, Springer, 2013.
M. Benoussaad, K. Mombaur, and C. Azevedo-Coste, “Optimal Control of Joint Ankle For Drop Foot Correction Through Functional Electrical Stimulation ,” Submitted.
K. Mombaur, email@example.com
Last Update: 27.04.2013 - 18:16