A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Analyzing Jo-Ha-Kyu Mechanism in Japanese Traditional Performing Art Ningyo Joruri

IoT devices that connect people without physical contact become more and more important after the COVID-19 impact. However, strange appearances and movements performed by IoT devices (interactive humanoid robots) cause human discomfort, so-called the uncanny valley, preventing widespread use of humanoid IoT devices. On the contrary, a Japanese traditional performing art named Ningyo Joruri (puppet theater) is recognized as a UNESCO intangible cultural heritage, and the sophisticated puppet motions and its unique music style somehow can avoid causing human discomfort even if the appearance of puppets is close enough to humans. One of the most important factors in empathizing humans with the puppet without uncomfortable is the modulation technique of both music tempo and motion speed known as Jo-Ha-Kyu. In this study, we analyzed Ningyo Joruri based on the Jo-Ha-Kyu mechanism, which is an art concept adopted in the puppet theater to interact with audiences according to modulation of the tempo. First, we obtained puppet movements using motion capture systems with the music. Second, we detected the changing tempo in Ningyo Joruri using the deep learning method to demonstrate the Jo-Ha-Kyu mechanism quantitatively. Finally, we showed the correlation of Jo-Ha-Kyu between Ningyo Joruri music and puppet manipulation techniques in the frequency domain using the Hilbert Huang transform. Our results revealed that low-frequency movements play an important role in synchronizing motion to the tempo of corresponding music, presenting novel knowledge to motion designers for humanoid robots IoT devices. © 2022 IEEE.

A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery.

In the aftermath of stock market crash due to COVID-19, not all sectors recovered in the same way. Recently, a stock price model is proposed by Mahata et al. (2021) that describes V- and L-shaped recovery of the stocks and indices, but fails to simulate the U- and Swoosh-shaped recovery that arises due to sharp fall, continuation at the low price and followed by quick recovery, slow recovery for longer period, respectively. We propose a modified model by introducing a new parameter θ = + 1 , 0 , - 1 to quantify investors' positive, neutral and negative sentiments, respectively. The model explains movement of sectoral indices with positive financial anti-fragility ( ϕ ) showing U- and Swoosh-shaped recovery. Simulation using synthetic fund-flow with different shock lengths, ϕ , negative sentiment period and portion of fund-flow during recovery period show U- and Swoosh-shaped recovery. It shows that recovery of indices with positive ϕ becomes very weak with extended shock and negative sentiment period. Stocks with higher ϕ and fund-flow show quick recovery. Simulation of Nifty Bank, Nifty Financial and Nifty Realty show U-shaped recovery and Nifty IT shows Swoosh-shaped recovery. Simulation results are consistent with stock price movement. The estimated time-scale of shock and recovery of these indices are also consistent with the time duration of change of negative sentiment from the onset of COVID-19. We conclude that investors need to evaluate sentiment along with ϕ before investing in stock markets because negative sentiment can dampen the recovery even in financially anti-fragile stocks.