# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
###### Abstract  
In this study, we proposed a novel machine-learning-based functional electrical stimulation (FES) control algorithm to enhance gait rehabilitation in post-stroke hemiplegic patients. The electrical stimulation of the muscles on the paretic side was controlled via deep neural networks, which were trained using muscle activity data from healthy people during gait. The performance of the developed system in comparison with that of a conventional FES control method was tested with healthy human subjects. functional electrical stimulation; electromyogram; machine learning; muscle fatigue; gait rehabilitation +
Footnote †: journal: Medical Medical Engineering  
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[PERSON]; [PERSON]; [PERSON]; [PERSON] DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients. _Appl. Sci._**2021**, _11_, 3163. [[https://doi.org/10.3390/app11073163](https://doi.org/10.3390/app11073163)]([https://doi.org/10.3390/app11073163](https://doi.org/10.3390/app11073163))  
Academic Editor [PERSON]  
Received: 10 March 2021  
Accepted: 29 March 2021  
Published: 1 April 2021  
**Published: 1 April 2021**  
Subsequent studies introduced the trapezoidal envelope with ramp up/down for the FES signal to relieve muscle fatigue that can be caused by abrupt changes in the electric stimulus in the rectangular pattern [14]. It was argued that neither the rectangular on/off pattern nor the trapezoidal pattern of the FES envelope was close to the electromyogram (EMG) patterns observed during natural walking [15; 16; 17]. Researchers have attempted to enhance the rehabilitation outcome by designing the envelope of the FES signal to match the EMG patterns acquired from natural movements. [PERSON] et al. demonstrated that the dorsiflexion angle increased by 76%, and the overall current decreased by 47% when an electrical stimulation pattern close to the natural EMG envelope was employed [15]. These studies employed a rather simple approach by mapping the EMG envelope to the intensity of the FES. However, this approach had limitations in terms of the generation of accurate muscle forces required for producing natural gait motion. To generate a natural movement, the relationship among FES, EMG, and the required joint torque and motion should be identified. [PERSON] et al. estimated the joint torque based on the measurement of evoked EMG, which was a combined signal of volitional EMG and FES artifacts measured using surface electrodes [18]. The drawback of this method was that the estimation was highly sensitive to the measurement of EMG, where the measurement was easily affected by external disturbances and electrode attachment conditions. [PERSON] et al. investigated the relationship between the joint torque and FES intensity through isometric contraction tests. Their experimental results yielded a linear model for the joint torque and FES amplitude [19]. This linear model was used to simply estimate the FES intensity, which was required to produce the target joint torque. However, the model was inappropriate for the estimation of joint torque during dynamic body movements owing to the complexity of the neuromusculoskeletal system of the human body [20]. Other researchers employed [PERSON]'s muscle model consisting of a mechanical spring and a damper to estimate joint torque based on EMG data [21; 22]. While this approach was robust in the application of muscle stimulation, it suffered from variability in EMG measurement caused by electrode reposition, skin condition, and muscle fatigue [23]. In recent years, machine learning has been actively used in various applications. Machine learning algorithms can learn from training data to make estimations, predictions, or decisions by themselves. Among the many developed machine learning techniques, deep neural networks (DNNs) have shown outstanding performance in handling nonlinearity and noisy signals. As an alternative to mathematical models for the human musculoskeletal system, such as [PERSON]'s muscle model, the DNN model can be used to estimate the joint torque to produce a desired motion.