Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Design and Implementation of a Multi-Purpose Low-Cost Hall-Effect Sensor Glove for Sign Language Recognition

Dinanath Padhya, Jenish Pant, Krishna Acharya, Sajen Maharjan, Sudip Kumar Thakur

TL;DR

This work tackles the shortage of affordable sign language technology for Nepali Sign Language by introducing a non-contact Hall-effect glove with 14 sensors and an IMU, running an embedded neural network on an Arduino Mega. The system achieves $96%$ accuracy on an 11-word NSL vocabulary at a BOM of approximately $80$–$100$, with durability advantages over resistive sensors due to the absence of mechanical wear. A four-stage data pipeline, 3D-printed rigid finger modules, and a compact electronic architecture enable on-device inference and offline training that can be deployed without cloud dependence. The study demonstrates the practicality of low-cost, robust wearables for NSL education in Nepal and outlines a scalable path toward larger vocabularies and broader deployment in resource-constrained settings.

Abstract

Despite the prevalence of severe hearing loss affecting over 430 million people globally, access to sign language interpretation remains critically scarce, particularly in low-resource settings like Nepal. Assistive technologies divide into two flawed categories: prohibitively expensive commercial gloves (often exceeding \$3,000) or fragile research prototypes reliant on flex sensors that degrade rapidly under mechanical stress. This paper introduces a robust, cost-effective sign language recognition system tailored for the Nepali Sign Language (NSL) community. Departing from traditional resistive sensing, we implement a non-contact Hall-effect architecture that correlates magnetic field intensity with finger flexion, eliminating mechanical wear and signal drift. The system integrates 14 sensor nodes across the DIP, PIP, and MCP joints, augmented by an MPU6050 IMU for wrist orientation. An embedded Multi-Layer Perceptron, executed locally on an Arduino Mega, performs gesture classification, negating the need for cloud dependencies. With a Bill of Materials between \$80 and \$100, this solution is approximately 30 times more affordable than market alternatives. Validation trials across five subjects yielded 96\% accuracy on a fundamental NSL vocabulary. Stress testing confirmed that the Hall-effect configuration maintains signal fidelity over repeated cycles where traditional sensors fail. This study demonstrates that high-precision recognition is achievable through strategic engineering rather than premium components, offering a scalable pathway for deployment in Nepal's deaf schools.

Design and Implementation of a Multi-Purpose Low-Cost Hall-Effect Sensor Glove for Sign Language Recognition

TL;DR

This work tackles the shortage of affordable sign language technology for Nepali Sign Language by introducing a non-contact Hall-effect glove with 14 sensors and an IMU, running an embedded neural network on an Arduino Mega. The system achieves accuracy on an 11-word NSL vocabulary at a BOM of approximately –, with durability advantages over resistive sensors due to the absence of mechanical wear. A four-stage data pipeline, 3D-printed rigid finger modules, and a compact electronic architecture enable on-device inference and offline training that can be deployed without cloud dependence. The study demonstrates the practicality of low-cost, robust wearables for NSL education in Nepal and outlines a scalable path toward larger vocabularies and broader deployment in resource-constrained settings.

Abstract

Despite the prevalence of severe hearing loss affecting over 430 million people globally, access to sign language interpretation remains critically scarce, particularly in low-resource settings like Nepal. Assistive technologies divide into two flawed categories: prohibitively expensive commercial gloves (often exceeding \80 and \$100, this solution is approximately 30 times more affordable than market alternatives. Validation trials across five subjects yielded 96\% accuracy on a fundamental NSL vocabulary. Stress testing confirmed that the Hall-effect configuration maintains signal fidelity over repeated cycles where traditional sensors fail. This study demonstrates that high-precision recognition is achievable through strategic engineering rather than premium components, offering a scalable pathway for deployment in Nepal's deaf schools.
Paper Structure (31 sections, 2 equations, 7 figures)

This paper contains 31 sections, 2 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. System Architecture
  5. 3D Structural Design
  6. Anatomical Alignment and Design Validation
  7. Sensor Integration: Hall-Effect Mechanism
  8. Magnetic Actuation Strategy.
  9. Response Characterization.
  10. Voltage response modelling.
  11. Magnetic field interference mitigation.
  12. Sensor data normalization.
  13. Electronic Circuit Architecture
  14. Power management cascade.
  15. Signal multiplexing and wiring.
  16. ...and 16 more sections

Figures (7)

  • Figure 1: System architecture showing data flow from sensor acquisition to speech output
  • Figure 2: 3D CAD model showing the glove's structural design and sensor mounting points
  • Figure 3: Early proof-of-concept prototype using cardboard chassis for initial sensor validation and testing
  • Figure 4: Hall-effect sensor and magnet mounting mechanism on a finger joint
  • Figure 5: Nonlinear Hall-effect sensor response showing sigmoidal saturation at extreme flexion angles
  • ...and 2 more figures