Topologically Enhanced Dual-Network Hydrogels with Rapid Recovery for Low-Hysteresis, Self-Adhesive Epidemic Electronics.

Dual-network conductive hydrogels have drawn wide attention in epidemic electronics such as epidemic sensors and electrodes because of their inherent low Young's modulus, high skin-compliance, and tunable mechanical strength. However, it is still full of challenges to gain a dual-network hydrogel with high stretchability, low hysteresis, and skin-adhesive performance simultaneously. Herein, to address this issue, a novel dual-network hydrogel (denoted as PAa hydrogel) with polyacrylamide as the first network and topologically entangled polydopamine as the secondary network was prepared through a facile gel-phase in situ self-polymerization and soaking treatment. Benefiting from the topological enhancement as well as the synergetic effects of hydrogen bonds and metal coordination bonds, low modulus (∼10 kPa), excellent stretchability (1090.8%), high compression (90%), negligible hysteresis (η = 0.019, energy loss coefficient), rapid recovery in seconds, and self-adhesion are obtained in the PAa hydrogels. To demonstrate their practical use, a states-independent and skin-adhesive epidemic sensor was successfully attached on human skin for motion detection. What is more, by using the hydrogel as an epidemic electrode, electromyogram signals were accurately detected and wirelessly transmitted to a smart phone. This work offers a new insight to understand the strengthening mechanism of dual network hydrogels and a design strategy for both epidemic sensors and electrodes.

[An endpoint-detection algorithm of surface electromyography insensitive to electrocardiogram interference].

Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.

[Automatic Classification of Dry Cough and Wet Cough Based on Improved Reverse Mel Frequency Cepstrum Coefficients].

Automatic classification of different types of cough plays an important role in clinical.In the previous research of cough classification or cough recognition,traditional Mel frequency cepstrum coefficients(MFCC)which extracts feature mainly from low frequency band is usually used as feature expression.In this paper,by analyzing the distributions of spectral energy of dry/wet cough,it is found that spectral difference of two types of cough exits mainly in middle frequency band and high frequency band.To better reflect the spectral difference of dry cough and wet cough,an improved method of extracting reverse MFCC is proposed.In this method,reverse Mel filter-bank in which filters are allocated in reverse Mel scale is adopted and is improved by placing filters only in the frequency band with high spectral energy.As a result,features are mainly extracted from the frequency band where two types of cough show both high spectral energy and distinguished difference.Detailed process of accessing improved reverse MFCC was introduced and hidden Markov models trained by 60 dry cough and 60 wet cough were used as cough classification model.Classification experiment results for 120 dry cough and 85 wet cough showed that,compared to traditional MFCC,better classification performance was achieved by the proposed method and the total classification accuracy was raised from 89.76%to 93.66%.

[Feature Extraction for Cough-sound Recognition Based on Principle Component Analysis and Non-uniform Filter-bank].

Cough recognition provides important clinical information for the treatment of many respiratory diseases. A new Mel frequency cepstrum coefficient (MFCC) extracting method has been proposed on the basis of the distributional characteristics of cough spectrum. The whole frequency band was divided into several sub-bands, and the energy coefficient for each band was obtained by method of principle component analysis. Then non-uniform filter-bank in Mel frequency is designed to improve the extracting process of MFCC by distributing filters according to the spectrum energy coefficients. Cough recognition experiment using hidden Markov model was carried out, and the results

[The endpoint detection of cough signal in continuous speech].

The endpoint detection of cough signal in continuous speech has been researched in order to improve the efficiency and veracity of manual recognition or computer-based automatic recognition. First, using the short time zero crossing ratio(ZCR) for identifying the suspicious coughs and getting the threshold of short time energy based on acoustic characteristics of cough. Then, the short time energy is combined with short time ZCR in order to implement the endpoint detection of cough in continuous speech. To evaluate the effect of the method, first, the virtual number of coughs in each recording was identified by two experienced doctors using the graphical user interface (GUI). Second, the recordings were analyzed by automatic endpoint detection program under Matlab7.0. Finally, the comparison between these two results showed: The error rate of undetected cough is 2.18%, and 98.13% of noise, silence and speech were removed. The way of setting short time energy threshold is robust. The endpoint detection program can remove most speech and noise, thus maintaining a lower rate of error.