# Integrating Biofeedback in Dynamic Biomechanical Gait Training for Chronic Ankle Instability
## 1 Introduction  
Walking is a fundamental activity that is essential for daily living because the kinetic energy generated during walking facilitates body movements [1]. However, persistent abnormal gait patterns can lead to ankle joint problems. Numerous studies on gait analysis have highlighted that prolonged gait irregularities can result in chronic musculoskeletal injuries, particularly in the lower extremities [2, 3]. Ankle grains, one of the most common musculoskeletal injuries, are a notable example. Recurrent ankle sprains may progress to chronic ankle instability (CAI), further increasing the risk of injuries in the lower extremities. Standard gait-training protocols often focus on modifying cadence [4], loading [5], foot strike patterns [6], speed [7], and lower-extremity alignment [8]. Customizing these protocols to fit individual patient needs is critical for their effectiveness. Recent advancements in technology have introduced wearable devices, treadmills with integrated force plates, real-time feedback systems, and virtual reality (VR) for gait training [4; 5; 6; 7; 8]. In particular, wearable devices now enable the real-time monitoring of various gait parameters in practical settings. Studies using such technologies have demonstrated their effectiveness. For instance, gait training with plantar pressure biofeedback has shown measurable effects by analyzing differences in peak pressure and time to peak pressure [5]. Kinetic research has primarily focused on vertical ground reaction forces, whereas electromyography (EMG) studies have examined the activity of key muscles, such as the tibialis anterior (TA), peroneus longus (PL), and gluteus medius (G-MED), in relation to initial contact and the 200 ms preceding it. Similar findings have been observed in controlled laboratory environments. Moreover, recent research has shown that IMU-based systems significantly enhance gait training by providing real-time feedback, which is crucial for improving balance and reducing reinjury rates in individuals with CAI [9]. Although the mechanisms underlying gait instability are not yet fully understood, research suggests that incorporating visual feedback and advanced gait-training strategies can help identify and correct abnormal gait patterns in real time [1; 2]. Various gait-training approaches have shown promise in assisting individuals with pathological conditions, helping to improve gait patterns following injuries. Proper gait patterns are essential for preventing recurrent injuries. Real-time data exchange technology significantly contributes to injury prevention, particularly in sports settings. Developing treatment strategies with immediate feedback capabilities is crucial to promoting rapid recovery. Such strategies could provide tailored training methods for individuals, with technologies that monitor real-time scenarios during continuous gait training playing a vital role in reducing reinjury rates. Currently, there is limited research on gait-training techniques for individuals with CAI, particularly regarding running gait. Effective interventions for CAI include destabilization devices that induce inversion perturbations during walking [8], plantar pressure feedback systems, and novel gait-training devices aimed at medializing foot loading. This focus is critical because individuals with CAI tend to shift their center of pressure laterally while walking [10]. Few studies have examined gait-training techniques specifically for CAI or the learning processes involved. A significant limitation of the current strategies is the inability to adequately control movements at the talocrural and subtalar joints [11]. Addressing technological and cost barriers could pave the way for more effective gait-training strategies for patients with CAI. Precise gait-training strategies can be realized through continuous moment-arm tracking in real-world scenarios in which injuries occur. The growing interest in wearable devices equipped with inertial measurement unit (IMU) sensors has driven advancements in smart healthcare.