Smartphone-based gait analysis and real-time feedback system for post-stroke lower-limb rehabilitation (Note: If you’d like more options or a different focus within Medical Rehabilitation, I can generate additional topics.)

Project Abstract

This study presents the development and evaluation of a smartphone-based gait analysis and real-time feedback system designed to assist post-stroke rehabilitation of the lower limbs. The system leverages the ubiquitous mobile device and its embedded sensors (accelerometer, gyroscope, and magnetometer) to capture spatiotemporal gait parameters, joint kinematics, and asymmetry metrics during overground walking and supervised gait training. A dedicated mobile application processes raw sensor data through a lightweight, on-device algorithm augmented by cloud-based optimization to estimate stride length, cadence, stance and swing phases, pelvic and knee angles, and ground reaction force proxies. Real-time feedback is delivered through multimodal cues including visual overlays, auditory prompts, and haptic signals, guiding users to adopt targeted movement strategies such as corrected toe clearance, normalized step length, and improved symmetry. The system is designed to operate with minimal setup, requiring a single wearable sensor placement at the shank or thigh and optional integration with a fixed smartphone mount to reduce user burden. The research follows a mixed-methods approach comprising three phases (i) algorithm development and validation against gold-standard motion capture and instrumented gait analysis in a cohort of 40 post-stroke individuals with varying degrees of impairment, (ii) a randomized controlled trial enrolling 60 participants to compare conventional therapy versus augmented rehabilitation using the smartphone solution over eight weeks, and (iii) usability and acceptance assessment through standardized questionnaires and semi-structured interviews with participants, caregivers, and clinicians. Primary outcomes include improvements in gait speed, functional ambulation categories, Timed Up and Go performance, and gait symmetry indices, while secondary outcomes assess adherence, user satisfaction, perceived exertion, and cognitive load associated with device use. Data analytics employ personalized calibration to account for leg length, assistive devices, and compensatory patterns, with adaptive feedback tailored to progress while minimizing cognitive demand. The study also investigates the system’s potential for telerehabilitation, enabling remote monitoring of gait metrics by therapists and automated generation of progress reports. Ethical considerations address data privacy, consent, and safety during unsupervised use, with risk mitigation strategies such as inactivity alerts and fall-detection mechanisms. Anticipated findings suggest that real-time, actionable feedback delivered through a portable platform can enhance motor learning, promote more symmetric gait patterns, and accelerate functional recovery post-stroke without the need for expensive laboratory infrastructure. The research contributes to the field by (a) delivering an accessible, scalable mHealth solution for post-stroke rehabilitation, (b) validating smartphone-based gait metrics against gold-standard measures to establish clinical credibility, and (c) providing a framework for integrating mobile gait analysis into routine clinical practice and home-based therapy. Practical implications include potential reductions in clinic visit frequency, improved patient engagement, and the democratization of gait rehabilitation resources across diverse settings. Limitations address sensor drift, variability in shoe-wear, environmental constraints, and the need for broader validation across diverse stroke populations. The study concludes with recommendations for integration into rehabilitation pathways and directions for future enhancements, including multimodal sensor fusion and AI-driven personalization.

Project Overview

What This Project Is About

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The project explores using a smartphone to analyze how a person walks after a stroke and to give real-time feedback to help improve movement of the leg that is affected.

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The Problem It Addresses

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Many stroke survivors have difficulty walking clearly, which can slow recovery and reduce independence. Traditional labs are costly and not always accessible. This project looks for a low-cost, easy-to-use way to monitor gait and guide rehabilitation at home or in clinics.

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Objectives of the Project

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1. Design a simple smartphone setup to collect movement data from walking tasks.
2. Identify common gait issues after stroke, such as uneven steps or limited ankle movement.
3. Provide real-time feedback to the user to encourage safer and more symmetric walking.
4. Evaluate whether feedback improves basic gait measures over time.

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What You Will Do Step by Step

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1) Review basic gait concepts and how sensors capture movement. 2) Develop an app or use existing tools to record walking data from a smartphone's sensors. 3) Create simple feedback signals (auditory or visual) based on movement patterns. 4) Run small tests with volunteers to collect walking data. 5) Analyze data to see changes in gait and whether feedback helps. 6) Discuss limitations and possible improvements for home use.

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Expected Outcome

A usable, low-cost method that tracks walking quality and offers easy-to-follow feedback. The study should show whether real-time cues help improve gait and suggest steps toward larger trials or home-based rehabilitation tools.