Editorial: Technology aided personalized motor rehabilitation for individuals with neurological diseases

Abstract

Recently, there has been an increasing focus on using technology to support personalized motor rehabilitation. By leveraging advanced tools such as robotics, virtual reality (VR), wearable sensors, machine learning, and neuromodulation, researchers and clinicians can now design interventions tailored to each patient's unique needs. This Research Topic, Technology-Aided Personalized Motor Rehabilitation for Individuals with Neurological Diseases, brings together a diverse collection of studies that explore innovative technologies, their applications, and the challenges of implementing them in clinical practice. The articles in this collection highlight the potential of these approaches to improve outcomes while addressing the challenges of accessibility, affordability, and clinician training.One of the key themes in this collection is the development and validation of new technology-based interventions. Harrington et al. [1] explored the use of aquatic treadmill walking to compare muscle co-contraction in children with cerebral palsy and typically developing children. The authors examined how different calculation methods could alter clinical interpretations, underscoring the importance of standardized metrics and robust analytical frameworks. Such work highlights the influence of data analysis choices on treatment decisions, reinforcing the need for evidence-based, quantitative methodologies to guide clinicians. Similarly, Cerfoglio et al. [2] demonstrated the potential of low-cost solutions like the Azure Kinect to compare gait parameters in healthy and hemiplegic individuals. Their findings suggest that single-camera systems may offer viable alternatives for assessing patient progress in less resource-intensive settings. This line of research speaks to the potential of portable and more affordable solutions, particularly relevant for telehealth or remote rehabilitation contexts when in-person evaluations are not feasible.Virtual reality also plays a prominent role in this collection. Beyond gait, Bonanno et al. [3] examined whether the Computer Assisted Rehabilitation ENvironment (CAREN)including a split-belt treadmill, six-degrees-of-freedom motion platform, and large-scale VR display-could benefit patients with cerebellar ataxia. Their pilot study suggests that such immersive VR-based interventions may yield improvements in balance and gait mechanics, contributing to the growing body of evidence supporting VR's capacity to provide task-oriented and engaging therapy. Berger and d'Avella [4] took a different approach by combining myoelectric control with VR to enhance upper-limb rehabilitation for people with stroke. By providing personalized feedback based on real-time electromyographic signals, their study demonstrates how VR can be used to re-train dysfunctional muscle activation patterns and facilitate motor recovery -a prime example of how individualized feedback can re-engage neuroplastic processes.Neuromodulation is another promising avenue explored in this collection. Zhang et al. [5] studied non-invasive cervical spinal cord neuromodulation via trans-spinal electrical stimulation (tsES) on cortico-muscular coherence in people with stroke. Their findings highlighted how tsES can modulate cortico-muscular coherence, potentially enhancing descending excitatory control and inhibiting unwanted compensatory mechanisms in chronic stroke. This form of personalized neuromodulation could be integrated with other therapies to optimize motor recovery strategies.Huang et al. [6] was able to identify mores subtle proprioception deficits in the common condition of knee osteoarthritis by using the technological assistance of the ankle inversion discrimination apparatus for walking (AIDAW). Their findings emphasized that deficits in lower-limb proprioception may increase the risk of falls among individuals with knee osteoarthritis. This research points to the importance of evaluating multiple biomechanical and sensory fa