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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| balance training using Artificial Intelligence | Experimental | Study group will receive exercises to facilitate motor control, function of the more affected lower extremity (strengthening exercises and stretching exercises) and balance training for 30 min in addition to balance training using Artificial Intelligence for 15 min. The total duration of the session will be 45 min for 6 weeks (3 times per week). |
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| conventional balance training | Active Comparator | Control group will receive exercises to facilitate motor control, function of the more affected lower extremity (strengthening exercises and stretching exercises) and balance training. The total duration of the session will be 45 min for 6 weeks (3 times per week). |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| balance training using artificial intelligence | Other | For each participant, sensors will be securely placed on key anatomical landmarks, including the lower back at the level of the L5 vertebra, the midpoints of both thighs and shanks, and the dorsal surfaces of both feet. This configuration will enable comprehensive 3D tracking of lower limb kinematics. Prior to data collection, the system will be calibrated for each participant's anthropometric dimensions to ensure measurement accuracy. The balance training will include both static and dynamic tasks. In the static component, participants will be asked to stand still for 1O minutes. In the dynamic component, participants will perform voluntary weight-shifting tasks in multiple directions for 5 minutes. During these tasks, the system will measure balance-related metrics such as center of pressure sway characteristics including sway path length, sway area, and sway velocity as well as postural stability indices and limits of stability. |
| Measure | Description | Time Frame |
|---|---|---|
| Evaluation of Pelvic asymmetry using digital pelvic inclinometer (DPI ) | The digital pelvic inclinometer will be used to evaluate sagittal and lateral pelvic tilt. Patients will be asked to stand bare feet wearing non-restrictive clothes. They will be asked to maintain an upright position with both feet in contact with the ground and apart 10-12 cm from each other. The prominence of both anterior superior iliac spines (ASIS) and posterior superior iliac spines (PSIS) will be palpated and marked with a marker. Evaluation of lateral pelvic inclination: It will be detected by measuring the angle between a line connecting both ASIS and the horizontal line. It will be measured by placing the thumb and index fingers of both hands on each end of the DPI arms. Then, they will be placed on the previously marked ASIS. The degree of inclination will be displayed on the LCD. Evaluation of sagittal pelvic inclination: It will be detected by measuring the angle between a line connecting ASIS and PSIS of the same side. | before starting the treatment procedure and at the end of six weeks of treatment |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| faculty of physical therapy, Cairo university | Giza | Egypt |
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For each participant, sensors will be securely placed on key anatomical landmarks, including the lower back at the level of the L5 vertebra, the midpoints of both thighs and shanks, and the dorsal surfaces of both feet. This configuration will enable comprehensive 3D tracking of lower limb kinematics. Prior to data collection, the system will be calibrated for each participant's anthropometric dimensions to ensure measurement accuracy. The balance training will include both static and dynamic tasks. In the static component, participants will be asked to stand still for 1O minutes. In the dynamic component, participants will perform voluntary weight-shifting tasks in multiple directions for 5 minutes. During these tasks, the system will measure balance-related metrics such as center of pressure sway characteristics including sway path length, sway area, and sway velocity as well as postural stability indices and limits of stability.
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| conventional balance training | Other | Static Balance Exercises: These include activities where the patient maintains a stable position, such as standing with feet together, semi tandem, tandem, or on one leg. Progression can be achieved by narrowing the base of support or altering sensory input (e.g., eyes closed, standing on foam).
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