# Active Gait Retraining with Lower Limb Exoskeleton Based on Robust Force Control
## 1 Introduction
Additionally, ref. [15] provides a methodology to properly tune the adaptive behavior of rehabilitation exoskeletons to better suit patients. By repeating the sequence of motion, it adapts its control parameters to eliminate regular disturbance and enhance the tracking accuracy of the trajectory. Such progressive capabilities in learning are most welcome in scenarios of therapy because the performance of patients keeps changing. Another approach to detecting movement intention involves using electromyographic signals [16] to adjust the torque sent to the motors in real time, enabling safe and progressive training. Additionally, human-in-the-loop control [17; 18] has been utilized for combined data acquisition while the patient is wearing the exoskeleton. This approach achieves balance optimization through the zero moment point methodology, which allows patients to walk without crutches or other support devices, resulting in better stability and increased patient confidence. For human conditions like hemiplegia, exoskeletons have been combined with motion capture systems to provide an analysis of symmetry between both the functional and the affected leg [19; 20]. This system facilitates the design and analysis of motion trajectories from the patient's functional limb and allows for replication in the damaged limb, aiming to aid in the recovery of symmetrical walking patterns. In [21], neuromuscular rehabilitation was conducted using a system called Progressive Assist-as-Needed Control, which employs muscular signals to adjust the support. This system allows for a smooth transition between different levels of assistance and adapts to the patient's progress. Reinforcement Learning (RL), Deep Learning (DL), and Deep Reinforcement Learning (DRL) [22; 23; 24; 25] have been explored as alternative approaches, as these techniques offer promising results due to their robustness against disturbances and their ability to adapt to varying conditions. For instance, in [24], researchers developed a DRL-based controller for lower limb rehabilitation that employs a neural controller that adapts to several forces applied by the human, without requiring parameter adjustments. This methodology has demonstrated efficiency in reducing tracking error, suggesting that DRL models offer adaptable assistance. Furthermore, in [25], an exoskeleton was developed using DL for post-neurovascular rehabilitation. This system uses convolutional neural networks and recurrent networks to interpret electromyography signals, aiming to adapt the motor actuator function according to the patient's condition. Its application allows for better precision in exoskeleton control, achieving adaptation in individual gait parameters and reducing external disturbances. Given these advances, the use of DL and DRL represents a viable alternative for the control of rehabilitation exoskeletons, optimizing patient rehabilitation. These strategies facilitate the elimination of tracking errors and reductions in disturbances during gait, enabling the development of efficient and customized rehabilitation technologies. Additionally, exoskeletons have been designed with anthropometric considerations that allow them to adjust to gait parameters [26], effectively eliminating tracking errors and disturbances [27]. Innovative devices with 12 degrees of freedom [28] have also been created. These devices can operate effectively against unmodeled uncertainties, potentially enhancing the autonomy and flexibility of the exoskeleton and the patient, thereby opening a wide range of opportunities for gait rehabilitation. Recent advancements propose the integration of neural networks, including recurrent neural networks such as Long Short-Term Memory (LSTM) architectures [29; 30; 31; 32], as well as optimization algorithms like fuzzy logic controllers [33; 34]. These innovations enhance adaptability and enable real-time adjustments based on biomechanical data obtained from IMU sensors. Controllers based on LSTM networks and fuzzy logic offer significant benefits in gait rehabilitation.