Post-Stroke Gait Rehabilitation Using Task-Oriented Physiotherapy and Wearable Feedback: A Case Study

Abstract

Stroke is a leading cause of long-term disability worldwide, with gait impairment being one of the most disabling sequelae affecting independence and quality of life. We report the case of a 56-year-old male with post-ischemic stroke hemiparesis who presented with significant gait asymmetry, reduced walking speed, and balance deficits. A structured physiotherapy program incorporating task-oriented gait training and wearable sensor–based feedback was implemented over 12 weeks. Objective gait parameters and functional outcomes demonstrated significant improvement, with enhanced symmetry, endurance, and confidence during ambulation. This case highlights the value of combining conventional physiotherapy principles with wearable technology to optimize post-stroke gait rehabilitation.

Introduction

Stroke frequently results in motor impairments that compromise gait, balance, and functional mobility. Post-stroke gait dysfunction is characterized by asymmetrical step length, reduced stance time on the affected limb, decreased walking speed, and impaired coordination [1]. These abnormalities not only limit mobility but also increase fall risk and restrict participation in daily activities.

Task-oriented physiotherapy, grounded in motor learning and neuroplasticity principles, emphasizes repetitive practice of functional activities to promote cortical reorganization and motor recovery [2]. Recent advances in wearable sensor technology have enabled real-time feedback on gait parameters, facilitating more precise movement retraining and patient engagement [3]. Integrating wearable feedback with task-oriented training may enhance rehabilitation outcomes by improving motor awareness and reinforcing correct movement patterns. This case study illustrates the clinical application and benefits of such an integrated approach in post-stroke gait rehabilitation.

Patient Information

• Age / Gender: 56-year-old male
• Occupation: Office administrator
• Medical History: Hypertension, type 2 diabetes mellitus
• Neurological History: Left middle cerebral artery ischemic stroke (3 months prior)
• Surgical History: None
• Social History: Independent prior to stroke, non-smoker
• Medications: Antihypertensives, antiplatelets, statins
• Chief Complaints: Difficulty walking, imbalance, fear of falling, reduced endurance

Clinical Findings

• Motor Examination:
  • Right-sided hemiparesis (Medical Research Council grade 3+/5 lower limb)
• Tone: Mild spasticity in right plantar flexors (Modified Ashworth Scale grade 1+)
• Gait Assessment:
  • Reduced stance time on right limb
  • Circumduction during swing phase
  • Decreased step length on left side
• Balance: Berg Balance Scale score: 36/56
• Functional Mobility:
  • Timed Up and Go (TUG): 22 seconds

Initial Impression: Post-stroke hemiparetic gait with impaired motor control and balance.

Timeline

• Month 0: Ischemic stroke event
• Month 1: Acute inpatient rehabilitation
• Month 3: Referral to outpatient physiotherapy
• Week 0: Baseline gait and functional assessment
• Weeks 1–12: Task-oriented gait training with wearable feedback
• Week 12: Post-intervention reassessment

Diagnostic Assessment

• Imaging: MRI brain confirmed left MCA territory infarct
• Functional Measures:
  • Berg Balance Scale
  • Timed Up and Go
  • 10-Meter Walk Test
• Wearable Sensor Data:
  • Step length symmetry
  • Cadence
  • Stance-to-swing ratio

Diagnosis: Post-stroke hemiparetic gait dysfunction.

Therapeutic Intervention

Step 1 – Baseline Training

• Strengthening of lower limb muscles
• Stretching to manage spasticity
• Static and dynamic balance exercises

Step 2 – Task-Oriented Gait Training

• Overground walking with emphasis on symmetrical step length
• Sit-to-stand and obstacle negotiation
• Dual-task walking activities

Step 3 – Wearable Feedback Integration

• Use of inertial sensor–based wearable device placed at the waist and ankles
• Real-time auditory and visual feedback on step symmetry and cadence
• Progressive goal setting based on objective gait data

Step 4 – Home Exercise Program

• Walking drills with self-monitoring strategies
• Balance and strengthening exercises

Sessions were conducted 5 days per week for 12 weeks.

Challenges Faced

• Initial fear of falling limiting gait speed
• Difficulty integrating feedback during early sessions
• Fatigue during prolonged walking tasks
• Need for motivation and adherence to home program

Follow-Up and Outcomes

• Berg Balance Scale: Improved from 36 to 48
• Timed Up and Go: Reduced from 22 to 14 seconds
• 10-Meter Walk Test: Walking speed increased from 0.45 m/s to 0.85 m/s
• Wearable Metrics:
  • Improved step length symmetry
  • Increased stance time on affected limb

The patient regained independent community ambulation with a single-point cane.

Discussion

Gait recovery following stroke relies heavily on neuroplastic changes driven by repetitive, task-specific practice. Task-oriented physiotherapy promotes meaningful motor learning by closely simulating real-life activities, thereby enhancing functional carryover and improving the transfer of gains from the clinical setting to everyday ambulation [2,4]. By emphasizing repetitive walking practice, weight shifting, step initiation, and obstacle negotiation, structured gait tasks directly target the neuromuscular deficits responsible for post-stroke gait asymmetry. In this case, the systematic progression of task difficulty facilitated measurable improvements in balance, coordination, and endurance, contributing to greater walking efficiency and confidence.

The incorporation of wearable sensor feedback further augmented the rehabilitation process by providing objective, real-time information on gait performance. This immediate feedback enhanced patient awareness of asymmetrical movement patterns and reinforced corrective strategies during training sessions. Previous studies have demonstrated that augmented feedback, particularly when delivered in real time, can accelerate motor relearning, improve gait symmetry, and promote more consistent use of the affected limb in stroke survivors [3,5]. From a motor learning perspective, such feedback strengthens error-based learning and supports the development of more efficient movement strategies.

Wearable devices also enable therapists to individualize training intensity and progression based on quantifiable gait parameters, such as step length, cadence, and stance time, rather than relying solely on observational assessment. This data-driven approach helps bridge the gap between clinical observation and quantitative analysis, allowing for more precise goal setting and outcome tracking. Furthermore, objective feedback can enhance patient motivation and adherence by visibly demonstrating progress over time.

This case supports emerging evidence that technology-assisted physiotherapy can effectively complement traditional rehabilitation approaches, particularly in chronic stroke patients where spontaneous recovery has plateaued. Integrating wearable feedback into task-oriented training may help overcome residual deficits, optimize functional gains, and support long-term maintenance of improved gait performance.

Multidisciplinary Approach

• Physiotherapist: Gait training and rehabilitation planning
• Neurologist: Stroke management and medical optimization
• Occupational Therapist: Functional mobility and ADL training
• Rehabilitation Nurse: Education and safety monitoring

Key Takeaways

• Post-stroke gait impairment significantly affects independence
• Task-oriented training promotes functional motor recovery
• Wearable feedback enhances gait symmetry and patient engagement
• Objective data supports individualized rehabilitation planning
• Integrated approaches improve long-term functional outcomes

Patient’s Perspective

“I could finally see how I was walking and correct myself. The feedback helped me feel more confident and steady.”

Conclusion

This case demonstrates that combining task-oriented physiotherapy with wearable sensor–based feedback can significantly improve gait performance, balance, and overall functional mobility in post-stroke patients. By integrating repetitive, goal-directed movement practice with objective, real-time biomechanical feedback, rehabilitation interventions can more effectively target underlying motor impairments and promote adaptive neuroplastic changes. The observed improvements in gait symmetry, walking speed, and postural stability in this patient highlight the clinical value of aligning therapeutic exercises with functional demands encountered in daily life.

Early adoption of technology-assisted rehabilitation strategies may be particularly beneficial during the subacute and chronic phases of stroke recovery, when spontaneous neurological improvement begins to plateau. Wearable sensors provide precise, continuous data that enable clinicians to identify subtle gait deviations, track progress longitudinally, and adjust treatment intensity and complexity in a timely manner. This level of individualized monitoring supports more efficient use of therapy sessions and facilitates evidence-based clinical decision-making. Moreover, visual and auditory feedback derived from wearable devices can enhance patient engagement, motivation, and adherence by allowing patients to actively participate in their own recovery process.

Importantly, the integration of technology should not replace fundamental physiotherapy principles but rather complement them. A personalized, data-driven approach that incorporates patient-specific goals, functional limitations, and contextual factors is essential for maximizing rehabilitation outcomes. Collaboration within a multidisciplinary team including physiotherapists, neurologists, occupational therapists, and rehabilitation nurses ensures comprehensive management of motor, cognitive, and psychosocial aspects of recovery. Collectively, this integrated strategy has the potential to reduce fall risk, promote long-term independence, and substantially improve quality of life for individuals recovering from stroke.

References

1. Perry J, Burnfield JM. Gait Analysis: Normal and Pathological Function. 2nd ed. Slack Incorporated; 2010.
2. Carr JH, Shepherd RB. Stroke rehabilitation: moving from theory to practice. Physiotherapy. 2000;86(2):63–70.
3. Dobkin BH, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair. 2011;25(9):788–798.
4. Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. Lancet. 2011;377(9778):1693–1702.
5. Patterson KK, Gage WH, Brooks D, et al. Evaluation of gait symmetry after stroke. Stroke. 2010;41(5):967–972.
6. Mehrholz J, Thomas S, Elsner B. Treadmill training and body weight support for walking after stroke. Cochrane Database Syst Rev. 2017;8:CD002840.
7. Chen G, Patten C, Kothari DH, Zajac FE. Gait differences between individuals with post-stroke hemiparesis and non-disabled controls. J NeuroEngineering Rehabil. 2005;2:7.

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