Contactless Sensing-Aided Respiration Signal Acquisition Using Improved Empirical Wavelet Transform for Rhythm Detection.

Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration signal acquisition technique, which can adaptively extract the desired signal under time-varying respiration rhythms within a wide range. To be specific, respiration is perceived by piezoelectric ceramics sensors along with ballistocardiography and other interference in a contactless manner, and the proposed improved empirical wavelet transform (IEWT) performs spectrum division and recognition based on upper envelop and principal component criteria, respectively, to adaptively extract the respiration spectrum for signal reconstruction.

Sleep Staging Using Noncontact-Measured Vital Signs.

As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people's quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient's mobility during the night, which is inconvenient for in-home monitoring.

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.

Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.

Chromatic dispersion mitigation using a SEFDM-based diversity technique for IM/DD long reach optical links.

Spectral efficient frequency division multiplexing (SEFDM) can offer a higher spectral efficiency (SE) than orthogonal frequency division multiplexing (OFDM). In this work, we propose a diversity technique based on SEFDM for beyond 100-Gb/s optical intensity modulation and direct detection (IM/DD) long reach (LR) applications. We mathematically demonstrate that the self-created inter-carrier interference of SEFDM signals can be reused to achieve a diversity gain on each sub-carrier and, in turn, improve the tolerance to power fading induced by chromatic dispersion (CD) in IM/DD LR links.

150-Gb/s SEFDM IM/DD transmission using log-MAP Viterbi decoding for short reach optical links.

Spectral efficient frequency division multiplexing (SEFDM) can improve the spectral efficiency for next-generation optical and wireless communications. In this work, we apply SEFDM in beyond 100-Gb/s optical intensity modulation and direct detection transmissions and propose a low-complexity logarithmic-maximum-a-posteriori (log-MAP) Viterbi decoding algorithm to achieve the maximum likelihood (ML) detection. We evaluate the likelihood of detections using a posteriori probability instead of Euclidean distance by taking both noise and inter-carrier interference into consideration.