Real-Time Embedded Moroccan Sign Language Word Recognition Using Dual IMU-Based Smart Gloves and Lightweight LSTM Network

Abstract

Automatic sign language recognition has become an important research area for improving communication between deaf individuals and the hearing community. While vision-based approaches achieve high recognition performance, their dependence on cameras and environmental conditions limits their suitability for portable real-time applications. This paper presents a fully embedded Moroccan Sign Language (MSL) recognition system based on a dual smart glove equipped with ten MPU6050 inertial measurement units (IMUs). A dedicated dataset of approximately 8000 gesture sequences representing 20 common MSL gesture classes was collected from 80 participants. The acquired multivariate inertial signals were preprocessed and used to train a lightweight Long Short-Term Memory (LSTM) network, which was quantized and deployed on a Raspberry Pi Pico microcontroller using TensorFlow Lite Micro. Experimental results achieved an overall recognition accuracy of approximately 98%, with high precision, recall, and F1-score, with an average inference latency of 27.4 ± 1.0 ms on the embedded platform. The proposed platform demonstrates the feasibility of accurate and low-latency MSL recognition on resource-constrained embedded hardware under controlled acquisition conditions, representing an initial proof of concept toward future wearable assistive communication systems.