# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
## 6 Conclusions  
In this study, we developed a novel gait rehabilitation system controlled by deep neural networks (DNNs) to provide functional electric stimulation (FES) for hemiplegic patients. The DNN-based controller computed amplitude of the FES on the paretic side based on the reference EMG and walking speed. The reference EMG was computed by functionalizing the EMG envelopes acquired from the treadmill gaits of healthy human subjects at various walking speeds. For the training data of the DNN, the EMG data and its corresponding joint torque were collected during isometric contraction tests with healthy human subjects. The DNN algorithm (CNN-MLP) was implemented on the FES control system to stimulate the soleus and TA during gait, and its performance was assessed in comparison with a conventional method based on the trapezoidal FES envelope considering the range of motion of the ankle joint during treadmill gait, amount of FES-induced fatigue after isometric stimulation, and amount of energy consumption. The experimental results showed that the proposed FES control system could produce a larger range of motion with reduced muscle fatigue and less energy consumption by providing a smooth and timely assistive force compared to the conventional FES control method. In addition, we aimed to develop an FES control algorithm to produce natural gait patterns in hemiplegic patients. By generating FES signals for the paretic side based on EMG data of healthy people, the burden on the muscles was effectively relieved while providing sufficient thrust force. Unlike conventional FES control methods with on/off step-like patterns or trapezoidal patterns, the smooth FES pattern can alleviate the impact on the muscles and electric energy consumption of the control device. The developed FES control algorithm is expected to be effective for chronic hemiplegia with a low degree of disability. However, further clinical evaluations are necessary. Furthermore, the long-term effect of muscle fatigue induced by FES should be further studied to predict the operation time of FES and to alert the impending exhaustion of muscle activity for safe use of the developed system. This study focused on the FES-induced ankle motion by stimulating the TA and the soleus only, while hemiplegia affects the motion of other joints. In our future work, we plan to apply the developed algorithm to the rehabilitation of knee and hip movements, as well as ankle movement. The FSR-based method used in this study is limited to coarse detection of the gait state, and additional sensors such as IMU can allow finer prediction  
Figure 14: Energy consumption ratio of DBE to TE. of the gait phase. The recent development of the multi-pad electrode enables FES control of the muscles of various sizes. The novel sensor and FES technologies can be applied to the system developed in this study to extend its application to the rehabilitation of various lower extremity motor functions. The proposed FES control can be applied more effectively in combination with exoskeleton-type rehabilitation robots. Further studies are required to address redundant actuation issues with hybrid rehabilitation systems of FES and exoskeleton robots. Conceptualization, S.P., S.-J.K. and S.J. ; methodology, S.P., S.-J.K. and S.J. ; software, S.J. ; Hardware, S.J., J.H.B. ; validation, S.P., J.H.B. ; formal analysis, S.J. ; investigation, S.J. ; data curation, S.J., J.H.B. ; writing--original draft preparation, S.J. ; writing--review and editing, S.J. S.P. ; supervision, S.P. ; project administration, S.P., S.-J.K.; funding acquisition, S.P., S.-J.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) (NRF 2016R1A5A1938472) and the work of [PERSON] was partially supported by the Sports Promotion Fund of Seoul Olympic Sports Promotion Foundation from Ministry of Culture, Sports and Tourism.