Balance Training Using Artificial Intelligence on Pelvic Asymmetry in Stroke Patients.

After stroke, hemiplegia is one of the most prevalent impairments. It has an extensive effect on altering balance and gait performance. During weight transfer, stroke patients struggle with maintaining their spine erect, rotating their trunk, moving their pelvis forward and backward and maintaining their balance response. The altered standing posture and impaired balance function in stroke patients also result in greater body sway of the center of mass. Poor balance and postural instability impair gait abilities, making daily living more challenging. The pelvis, which is a connecting link between the trunk and lower limbs, plays a crucial role in balance and also in lower limb performance exclusively in gait. During both static and dynamic postural adjustments, the pelvic area is acknowledged as an essential location that enables the body to maintain momentum and adjust weight variations. After stroke, Asymmetrical weight bearing on the lower limbs and abnormal pelvic alignment are frequently observed in standing and walking. Functional mobility skills require the ability to shift weight on the affected lower extremity. In stroke patients, the forward and backward pelvic tilts are often impaired. When standing, they have a more forward-leaning posture and their pelvis is tilted anteriorly. Reduced hip muscle control or inadequate trunk-pelvis dissociation can cause the altered pelvic alignment, which causes stroke patients to experience abnormal pelvic movement. Artificial intelligence (AI) is rapidly transforming balance rehabilitation for stroke patients by enabling more personalized, adaptive, and effective interventions. AI-driven decision support systems can automatically tailor rehabilitation routines to each patient's progress, optimizing exercise type, intensity, and duration based on real-time performance data, which enhances both efficiency and outcomes. Integration of AI supports individualized therapy by providing immediate feedback, adjusting training parameters, and maintaining patient engagement, all of which contribute to improved motor function, balance, and independence. The use of machine learning and deep learning algorithms also enables precise assessment and prediction of recovery trajectories, supporting clinicians in making data-driven decisions for ongoing therapy adjustments. Collectively, these advancements demonstrate that AI not only streamlines and personalizes balance rehabilitation for stroke patients but also holds promise for improving long-term functional outcomes and quality of life.

Inclusion Criteria: * Stroke patients of both sexes diagnosed with first onset of stroke. * Stroke duration of 3 to 6 months. * Patients aged between 55-65 years. * Sufficient cognitive function (\< 24 points on the mini-mental state examination). * Ability to stand and walk 10 meters independently without supervision. * Lower limb spasticity graded as ≤ 2 on the modified Ashworth scale (MAS). * Patients who are medically stable. Exclusion Criteria: * Recurrent strokes. * Brainstem or cerebellar strokes. * Other neurological diseases that could affect balance. * Patients with disability in visual, auditory, and vestibular systems. * Musculoskeletal diseases such as recent fractures/ surgeries of lower extremities or contractures of the hip and knee flexors affecting standing balance. * Sensory, perceptual and cognitive deficits.