A word-level Wi-Fi CSI based Deep Bangladeshi Sign Language Dataset(WiBaSL)

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.