Functional electrical stimulation (FES) is important in gait rehabilitation for patients with dropfoot. Since there are time-varying velocities during FES-assisted walking, it is difficult to achieve a good movement performance during walking. To account for the time-varying walking velocities, seven poststroke subjects were recruited and fuzzy logic control and a linear model were applied in FES-assisted walking to enable intensity- and duration-adaptive stimulation (IDAS) for poststroke subjects with dropfoot. In this study, the performance of IDAS was evaluated using kinematic data, and was compared with the performance under no stimulation (NS), FES-assisted walking triggered by heel-off stimulation (HOS), and speed-adaptive stimulation. A larger maximum ankle dorsiflexion angle in the IDAS condition than those in other conditions was observed. The ankle plantar flexion angle in the IDAS condition was similar to that of normal walking. Improvement in the maximum ankle dorsiflexion and plantar flexion angles in the IDAS condition could be attributed to having the appropriate stimulation intensity and duration. In summary, the intensity- and duration-adaptive controller can attain better movement performance and may have great potential in future clinical applications.
Introduction
Stroke is a leading cause of disability in the lower limb, such as dropfoot (1). A typical cause of dropfoot is muscle weakness, which results in a limited ability to lift the foot voluntarily and an increased risk of falls (2–4). Great effort is made toward the recovery of walking ability for poststroke patients with dropfoot, such as ankle–foot orthoses (5), physical therapy (6), and rehabilitation robot (7).
Functional electrical stimulation (FES) is a representative intervention to correct dropfoot and to generate foot lift during walking (8, 9). The electrical pulses were implemented via a pair of electrodes to activate the tibialis anterior (TA) muscle and to increase the ankle dorsiflexion angle. The footswitch or manual switch was used to time the FES-assisted hemiplegic walking in previous studies, while they were only based on open-loop architectures. The output parameters of the FES required repeated manual re-setting and could not achieve an adaptive adjustment during walking (10, 11). Some researchers have found that the maximum ankle dorsiflexion angle by using FES with a certain stimulation intensity had individual differences due to the varying muscle tone and residual voluntary muscle activity and varied during gait cycles (12, 13). If the stimulation intensity was set to a constant value during the whole gait cycle, the result could be that the muscle fatigues rapidly (14). Another important problem was that the FES using fixed stimulation duration from the heel-off event to the heel-strike event would affect the ankle plantar flexion angle (15, 16).
Closed-loop control was an effective way to adjust the stimulation parameters automatically, and several control techniques have been proposed (17, 18). Negård et al. applied a PI controller to regulate the stimulation intensity and obtain the optimal ankle dorsiflexion angle during the swing phase (19). A similar controller was also used in Benedict et al.’s study, and the controller was tested in simulation experiments (20). Cho et al. used a brain–computer interface to detect a patient’s motion imagery in real time and used this information to control the output of the FES (21). Laursen et al. used the electromechanical gait trainer Lokomat combined with FES to correct the foot drop problems for patients, and there were significant improvements in the maximum ankle dorsiflexion angles compared to the pre-training evaluations (22). There were also several studies that used trajectory tracking control to regulate the output and regulate the pulse width and pulse amplitude of the stimulation (23). The module was based on an adaptive fuzzy terminal sliding mode control and fuzzy logic control (FLC) to determine the stimulation output and force the ankle joint to track the reference trajectories. In their study, FES applied to TA was triggered before the heel-off event. Because the TA activation has been proven to occur after the heel-off event and the duration of the TA activation changed with the walking speed (24, 25), a time interval should be implemented after the heel-off event (26). In Thomas et al.’s study, the ankle angle trajectory of the paretic foot was modulated by an iterative learning control method to achieve the desired foot pitch angles (27). The non-linear relationship between the FES settings and the ankle angle influenced the responses of the ankle motion (28). FLC represents a promising technology to handle the non-linearity and uncertainty without the need for a mathematical model of the plant, which has been widely used in robotic control (29). Ibrahim et al. used FLC to regulate the stimulation intensity of the FES (30), and the same control was used on the regulation of the stimulation duration to obtain a maximum knee extension angle in Watanabe et al.’s study (31). However, most closed-loop controls adjust only one stimulation parameter, and few FES controls considered both varying the stimulation intensity and duration while accounting for the changing walking velocities.
In the present study, an intensity- and duration-adaptive FES was established, the FLC and a linear model were used to regulate the stimulation intensity and duration, respectively. The performance of the intensity- and duration-adaptive stimulation (IDAS) was compared with those of stimulation triggered by no stimulation (NS), heel-off stimulation (HOS), and speed-adaptive stimulation (SAS) for poststroke patients walking on a treadmill. The objective of this study is to find an appropriate FES control strategy to realize a more adaptive ankle joint motion for poststroke subjects.[…]
Figure 4. (A) Ankle angles during the gait cycle for one poststroke subject at free speed; (B) knee angles during the gait cycle for the same poststroke subject at free speed.
TBI Rehabilitation 