Kangsoo Shin

Noncontact Sleep Study by Multi-Modal Sensor Fusion

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

Location based hybrid application using Wi-Fi zone

Smartphone is the fastest growing in handheld device market. Due to this factor, the importance of mobile web and applications are growing. We introduce an alternative enhanced hybrid application scheme to reduce a number of applications. The enhanced hybrid application can provide various services based on Wi-Fi zone. This application can reduce a number of applications and complexity in mobile home screen.

An experimental study: The sufficient respiration rate detection technique via continuous wave Doppler radar

In this paper, we present the experimental study based on filtering techniques to extract essential vital signals such respiration rate (RR) using continuous wave (CW) Doppler radar sensor. Due to the fact that the CW Doppler radar offers very high sensitivity towards minor movements, based on this technology, various filtering techniques were introduced and the detection rate was compared to the reference devices to conclude the leading method for the respiration rate detection.