Controlled FES-assisted gait training for hemiplegic stroke patients based on inertial sensors

Stroke is one of the leading causes to disability in the weste rn world. The effects of stroke are many and in many cases include impairment of motor, sensory input, em otion, language, perception and cognitive functions. Impairment of motor functions usually involves paralysis or paresis on one side of the body. One crucial component in rehabilitation following a stroke is the restoration of mobility of which walking is an essential part. For almost 50 years Functional Electri cal Stimulation (FES) has been applied to restore motor function and to improve gait. In 1961 Liberson used functional electrical stimulation to elicit the withdrawal reflex during the swing phase. This work is concerned with the technological development o f a new system for FES-assisted gait training for stroke patients. The system is based on an inertial se nsor system consisting of 2 inertial sensor units mounted to each foot. Algorithms for estimation of foo t rientation, detection of gait phases and estimation of movement parameters as well as strategies for multichannel FES-assisted gait training have been developed. The algorithm for gait phase detection uses all possible information from one inertial sensor to detect four distinct gait phases. The gait phases a re represented as states in a state machine and the transitions are governed by logical functions. The dete ction system was validated by use of an insole foot pressure measurement system. In experiments involvin g five hemiplegic subjects, it was found that the detection system worked robustly, meaning that all gait phases were detected and no critical failure in the phase detection occurred. Based on the gait phase detection algorithm and the estimati on of foot orientation, an algorithm for estimation of movement parameters like step length and foot clearance was developed. The method takes into account known constraints of the movement, like initia l and end conditions in order to improve the accuracy. The estimated movement parameters were compared with measurement from an optical motion analysis system for 5 stroke patients walking on a treadmill . The results showed that the movement parameters could be estimated from inertial sensor data wit h a high accuracy. Furthermore, on basis of the gait phase detection algorithm , a ultichannel stimulation strategy for stroke patients walking on a treadmill was developed and imp le ented in a laboratory set-up. Gait analysis measurements with an optical motion analysis syst em for two stroke patients showed that the electrical stimulation had an immediate positive e ffect on the gait pattern. Furthermore, a closed-loop strategy was derived in order to control movement parameter s th estimated by the inertial sensor system. By evaluating the movement parameters after a completed str ide the stimulation intensity was found for the next stride. The closed-loop strategy was successfully tested in simulation. In experiments with one stroke patient having a drop foot, the strategy was successf ully applied to control the maximum sagittal angle of the foot by stimulation of tibialis.