Accuracy Evaluation on the Respiration Rate Estimation using Off-the-shelf Pulse-Doppler Radar

This student paper presents preliminary results of using a pulse Doppler radar to detect the respiration rate of human subjects, examining the accuracy of the approach and evaluating the parameters to obtain the most precise result. In the study, the respiration data is recorded by repeatedly detecting people seated in front of the radar at different ranges as well as different aspect angles each time. Then Movement Target Indication, short-time Fourier transform and the analysis of the choice of doppler bins and window size of STFT are performed to evaluate the respiration rate and its precision. The results indicate that the respiration rate can be successfully detected at various ranges and angles and the relationship between Doppler bins and window size in processing is also observed to help us find the most accurate respiration rate

A comprehensive multimodal dataset for contactless lip reading and acoustic analysis

Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject’s lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition