Research Domain

Project Domain

Understanding the research landscape, problem space, and technical approach behind ArmiGo.

Literature Review

What existing research says about pediatric rehabilitation

Hemiplegia in children — often resulting from perinatal stroke or cerebral palsy — affects 1 in 1,000 live births. Conventional therapy relies on repetitive physical exercises under clinical supervision, which is resource-intensive and difficult to sustain long-term.

Recent literature highlights the effectiveness of technology-assisted rehabilitation. Studies on constraint-induced movement therapy (CIMT) and robot-assisted therapy show statistically significant improvements in motor function. VR-based approaches show comparable outcomes with higher patient engagement and adherence rates, particularly among younger populations.

IoT-enabled wearables allow continuous remote monitoring, closing the feedback loop between patients and clinicians. However, most existing solutions target adult populations and are cost-prohibitive for widespread adoption in developing regions.

Research Gap

Existing rehabilitation technologies fail to address the paediatric context holistically. Key gaps identified include:

  • Lack of age-appropriate, gamified therapy solutions for children under 14.
  • Absence of affordable IoT wearables designed for small limb profiles.
  • No unified platform connecting the child, parent, and clinician in real time.
  • Limited adaptive difficulty systems that personalise therapy intensity.
  • Insufficient solutions suitable for low-resource healthcare settings.

Research Problem

"How can an integrated IoT and VR platform improve upper-limb motor rehabilitation outcomes for hemiplegic children aged 6–14, while maintaining engagement and enabling remote monitoring by caregivers and clinicians?"

Research Objectives

1

Develop IoT-based wearable devices to support upper-limb rehabilitation for hemiplegic children aged 6–14.

2

Design engaging VR games that motivate children to perform repetitive therapeutic exercises.

3

Provide a real-time progress monitoring dashboard for parents and hospital staff.

4

Integrate machine learning to personalise therapy difficulty based on patient performance.

5

Reduce reliance on in-clinic sessions by enabling effective home-based rehabilitation.

Methodology

01

Requirement Analysis

Clinician interviews, literature synthesis, and patient family surveys to define functional and non-functional requirements.

02

Iterative Design & Build

Agile sprints covering hardware prototyping, game development, backend APIs, and mobile/web dashboards.

03

Evaluation & Validation

Usability testing with target users, clinical expert review, and quantitative performance metrics analysis.

Technologies Used

React NativeNext.jsNestJSUnity 3DArduinoESP32TensorFlow LitePostgreSQLPrisma ORMWebSocketAWS S3Docker