Work

Descriptor: A Word-Level Wi-Fi CSI-Based Deep Bangladeshi Sign Language Dataset (WiBaSL)

Year: 2025

Type: article

Abstract: WiFi-based human sensing has shown remarkable potential to detect sign language gestures in a non-intrusive manner. However, most previous works focus on American Sign Language detection, ignoring applications in other widely used languages such as Bangla Sign Language. There also remains a lack of collection of sign language gestures for Activities of Daily Life (ADL) necessary for instructing children with Autism Spectrum Disorder (ASD). To bridge this gap, we introduce WiBaSL, a WiFi-based word-level deep Bangladeshi Sign Language recognition dataset with WiFi CSI (Channel State Information) measurements of hand gestures for 24 Bangladeshi Sign Language words necessary for Activities of Daily Living. This article presents the WiBaSL data acquisition process, covering the collection protocol, experimental setup, and validation strategy. CSI signals for 24 Bangla sign language gestures were recorded in controlled conditions and preprocessed using standard filtering techniques. Dynamic Time Warping (DTW) was applied to assess consistency across samples. The dataset is intended to support future research in device-free, WiFi-based sign language recognition.

Source: IEEE data descriptions.

DOI: 10.1109/ieeedata.2025.3584021

Authors: Mahmud Wasif Nafee, Mahian Kabir Joarder, Mahmudul Mohtasim, Tahsina Farah Sanam

Institution: Bangladesh University of Engineering and Technology

Language: English

Topic: Hand Gesture Recognition Systems

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $600

Funders: Information and Communication Technology Division, Organization for Women in Science for the Developing World