High-quality Recordings Research Articles

Cardiovascular Risk Factors

Cardiovascular illnesses (CVDs) stay a main cause of morbidity and mortality worldwide. Various danger factors contribute to the improvement and development of CVDs, encompassing each modifiable and non-modifiable element. This abstract pursues to spotlight the important thing cardiovascular chance factors and their impact on coronary heart fitness. Age and Gender: Advancing age and being male are non-modifiable risk factors associated with increased CVD threat. Guys are usually at a better chance than premenopausal women; but, this difference decreases put up-menopause. High blood pressure: extended blood strain is a sizeable modifiable chance issue for CVDs. control hypertension damages blood vessels, selling atherosclerosis and increasing the threat of heart assault, stroke, and coronary heart failure. Dyslipidemia: high degrees of LDL cholesterol and triglycerides, coupled with low stages of HDL cholesterol, make contributions to atherosclerosis and plaque formation, main to coronary artery disorder and other cardiovascular complications. Smoking: Cigarette smoking is a main modifiable risk element for CVDs. It damages blood vessels, accelerates atherosclerosis, and decreases oxygen delivery to tissues, heightening the risk of heart disease and stroke. Diabetes Mellitus: each type 1 and kind 2 diabetes drastically raises the danger of CVDs because of insulin resistance, inflammation, and metabolic abnormalities that adversely affect blood vessels and the heart. Obesity: - extra body weight, specifically abdominal adiposity, increases the likelihood of CVDs using contributing to insulin resistance, hypertension, dyslipidemia, and inflammation. Bodily state of being inactive: Sedentary lifestyle and shortage of regular bodily pastimes are connected to weight problems and numerous metabolic disturbances that sell CVD improvement. Circle of relatives records: A high-quality own family record of premature CVD increases an individual's chance, suggesting a capacity genetic predisposition to heart disease. Weight-reduction plan: - consuming a diet excessive in saturated and Tran’s fat, salt, and introduced sugars even as missing fruits, veggies, and whole grains can make contributions to the CVD threat. Strain and intellectual health: chronic pressure, depression, and anxiety can impact CVD risk via numerous mechanisms, consisting of unhealthy coping behaviors and hormonal imbalances. Alcohol consumption: whilst mild alcohol consumption may also have some cardiovascular blessings, immoderate ingesting can boost blood strain and make contributions to coronary heart muscle damage. Efforts to mitigate cardiovascular hazard factors should recognition on lifestyle adjustments, consisting of ordinary workouts, a heart-wholesome diet, smoking cessation, stress management, and blood pressure and cholesterol control. Early identification of chance factors and their effective control can play an important role in decreasing the burden of cardiovascular illnesses and improving typical coronary heart health.

Design, Characterization, and In Vivo Application of Multi-Conductive Layer Organic Electrocorticography Probes

Biocompatible and plastic neural interface devices allow for minimally invasive recording of brain activity. Increasing electrode density in such devices is essential for high-resolution neural recordings. Superimposing conductive leads in devices can help multiply the number of recording sites while keeping probes width small and suitable for implantation. However, because of leads' vertical proximity, this can create capacitive coupling (CC) between overlapping channels, which leads to crosstalk. Here, we present a thorough investigation of CC phenomenon in multi-gold layer thin-film multi-electrode arrays with a parylene C (PaC) insulation layer between superimposed leads. We also propose a guideline on the design, fabrication, and characterization of such type of neural interface devices for high spatial resolution recording. Our results demonstrate that the capacitance created through CC between superimposed tracks decreases non-linearly and then linearly with the increase of insulation thickness. We identify an optimal PaC insulation thickness that leads to a drastic reduction of CC between superimposed gold channels while not significantly increasing the overall device thickness. Finally, we show that double gold layer electrocorticography probes with the optimal insulation thickness exhibit similar performances in vivo when compared to single-layer devices. This confirms that these probes are adequate for high-quality neural recordings.

AVbook, a high-frame-rate corpus of narrative audiovisual speech for investigating multimodal speech perception.

Seeing a speaker's face can help substantially with understanding their speech, particularly in challenging listening conditions. Research into the neurobiological mechanisms behind audiovisual integration has recently begun to employ continuous natural speech. However, these efforts are impeded by a lack of high-quality audiovisual recordings of a speaker narrating a longer text. Here, we seek to close this gap by developing AVbook, an audiovisual speech corpus designed for cognitive neuroscience studies and audiovisual speech recognition. The corpus consists of 3.6 h of audiovisual recordings of two speakers, one male and one female, each reading 59 passages from a narrative English text. The recordings were acquired at a high frame rate of 119.88 frames/s. The corpus includes phone-level alignment files and a set of multiple-choice questions to test attention to the different passages. We verified the efficacy of these questions in a pilot study. A short written summary is also provided for each recording. To enable audiovisual synchronization when presenting the stimuli, four videos of an electronic clapperboard were recorded with the corpus. The corpus is publicly available to support research into the neurobiology of audiovisual speech processing as well as the development of computer algorithms for audiovisual speech recognition.

Wireless Cursor Controller: Hand Gestures for Computer Navigation

Abstract: This research paper presents a wireless cursor controller system that uses hand gestures to navigate and control a computer's cursor without the need for a traditional mouse. The system employs a fixed position, inexpensive webcam with a high-quality recording feature installed on the top of a computer monitor or a fixed laptop camera. It uses Media-Pipe package for hand tracking and OpenCV for recording motions using computer vision. The system is capable of recognizing hand gestures to perform actions such as left-click, right-click, drag and drop, scroll, and adjust volume or brightness. The proposed system is expected to provide a simple and natural way of communicating for people with hand problems, as well as for those who want to interact with computers more efficiently.

Virtual reality set-up for studying vestibular function during head impulse test.

Virtual reality (VR) offers an ecological setting and the possibility of altered visual feedback during head movements useful for vestibular research and treatment of vestibular disorders. There is however no data quantifying vestibulo-ocular reflex (VOR) during head impulse test (HIT) in VR. The main objective of this study is to assess the feasibility and performance of eye and head movement measurements of healthy subjects in a VR environment during high velocity horizontal head rotation (VR-HIT) under a normal visual feedback condition. The secondary objective is to establish the feasibility of VR-HIT recordings in the same group of normal subjects but under altered visual feedback conditions. Twelve healthy subjects underwent video HIT using both a standard setup (vHIT) and VR-HIT. In VR, eye and head positions were recorded by using, respectively, an imbedded eye tracker and an infrared motion tracker. Subjects were tested under four conditions, one reproducing normal visual feedback and three simulating an altered gain or direction of visual feedback. During these three altered conditions the movement of the visual scene relative to the head movement was decreased in amplitude by 50% (half), was nullified (freeze) or was inverted in direction (inverse). Eye and head motion recording during normal visual feedback as well as during all 3 altered conditions was successful. There was no significant difference in VOR gain in VR-HIT between normal, half, freeze and inverse conditions. In the normal condition, VOR gain was significantly but slightly (by 3%) different for VR-HIT and vHIT. Duration and amplitude of head impulses were significantly greater in VR-HIT than in vHIT. In all three altered VR-HIT conditions, covert saccades were present in approximatively one out of four trials. Our VR setup allowed high quality recording of eye and head data during head impulse test under normal and altered visual feedback conditions. This setup could be used to investigate compensation mechanisms in vestibular hypofunction, to elicit adaptation of VOR in ecological settings or to allow objective evaluation of VR-based vestibular rehabilitation.

Restoration of missing or low-quality 12-lead ECG signals using ensemble deep-learning model with optimal combination

Background and ObjectiveIn a 12-lead electrocardiogram (ECG) examination, the ECG signals often have low-quality data problems due to high-frequency noise caused by muscles and low-frequency noise caused by body movement, breathing. These problems cause delays in examination results and increase medical costs. For this reason, solving low-quality data and missing ECG data problems can provide patients with improved medical services, reducing the work-loss and medical costs. The purpose of this study is to develop a signal restoration model for each of the 12 signals to solve the low-quality and missing data problems caused by mechanical and operator errors during 12-lead ECG examinations. MethodsFor this study, 13,862 high-quality 12-lead ECG recordings for multiple diseases were obtained from the 12-lead ECG database of a general hospital from 2016 to 2020. Two strategies were adopted to develop an accurate restoration model. First, to obtain the optimal input parameters for the ECG regeneration model for each ECG signal, linear regression (LR) models were developed for all 165 three-signal combinations of 11 signals. Second, the restoration models were constructed in a parallel architecture combining bidirectional long short-term memory (Bi-LSTM) with a convolutional neural network (CNN) to learn the temporal and spatial features of optimal combinations. ResultsThe performances of the 165 candidate combinations for restoring missing signal were analyzed through the LR model to find the optimal input parameter for all ECG signals. The average root mean square error of the optimal combinations was 0.082 μV. The average RMSE of the signal restoration model made using the optimal combinations and deep-learning model (Bi-LSTM&CNN) was 0.037 μV, and the cosine simplicity was 0.991. ConclusionsThis ECG restoration technology obtained optimal input parameters through the LR model and developed ECG restoration model through the Bi-LSTM&CNN combined model to restore ECG signals for multiple diseases. The 12-lead ECG signal restoration model developed through this study offers high accuracy for the magnitude and direction components of all 12 signals. This technology can be used in emergency medical systems and remote ECG measurement situations, as well as in synthetic ECG generation technologies for constructing research datasets.

The effect of head rotation on OSA is associated with disease severity: a cross-sectional study.

Head rotation is an effective positional therapy for obstructive sleep apnea (OSA). However, not all OSA patients benefit from head rotation. This study aimed to explore the clinical phenotype of OSA patients who can benefit from head rotation. We performed a retrospective review of 184 consecutive OSA patients who underwent polysomnography. Head rotation in supine position was determined by high-quality video recording. According to the changes in apnea-hypopnea index (AHI) after head rotation, OSA patients were divided into two groups: patients with response to head rotation(HR) and patients without response to head rotation(NHR). Demographic factors and overnight polysomnography were analyzed. Compared with NHR group, HR group showed significantly lower AHI (51.8 vs 31.5, p < 0.01), time spent with oxygen saturation below 90%(5.3% vs 0.51%, p < 0.01), and higher lowest oxygen saturation(80% vs 86%, p < 0.05). Logistic regression showed that AHI was an independent factor to predict the decrease of AHI in head rotation (OR 0.985, 95% CI 0.970-0.979, p < 0.05). Among mild to moderate group (AHI < 30/h), severe group (30/h ≤ AHI < 60/h), and extremely severe group (AHI ≥ 60/h), the percentage decrease of AHI in head rotation was 18.5%, 9.5%, and 2.6%, respectively. Surprisingly, the percentage decrease of AHI of 6 responders in mild to moderate group was more than 50%. OSA patients who respond well to head rotation have less severe disease, and patients with mild to moderate OSA are more likely to improve and benefit from this position. Our research provides potential strategies and insights into the individual treatment of OSA patients.

A data-driven model for Fennoscandian wildfire danger

Abstract. Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001–2019) satellite-based fire occurrence dataset at a 0.25∘ spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios.

Less-invasive subdural electrocorticography for investigation of spreading depolarizations in patients with subarachnoid hemorrhage.

Wyler-strip electrodes for subdural electrocorticography (ECoG) are the gold standard for continuous bed-side monitoring of pathological cortical network events, such as spreading depolarizations (SD) and electrographic seizures. Recently, SD associated parameters were shown to be (1) a marker of early brain damage after aneurysmal subarachnoid hemorrhage (aSAH), (2) the strongest real-time predictor of delayed cerebral ischemia currently known, and (3) the second strongest predictor of patient outcome at 7 months. The strongest predictor of patient outcome at 7 months was focal brain damage segmented on neuroimaging 2 weeks after the initial hemorrhage, whereas the initial focal brain damage was inferior to the SD variables as a predictor for patient outcome. However, the implantation of Wyler-strip electrodes typically requires either a craniotomy or an enlarged burr hole. Neuromonitoring via an enlarged burr hole has been performed in only about 10% of the total patients monitored. In the present pilot study, we investigated the feasibility of ECoG monitoring via a less invasive burrhole approach using a Spencer-type electrode array, which was implanted subdurally rather than in the depth of the parenchyma. Seven aSAH patients requiring extraventricular drainage (EVD) were included. For electrode placement, the burr hole over which the EVD was simultaneously placed, was used in all cases. After electrode implantation, continuous, direct current (DC)/alternating current (AC)-ECoG monitoring was performed at bedside in our Neurointensive Care unit. ECoGs were analyzed following the recommendations of the Co-Operative Studies on Brain Injury Depolarizations (COSBID). Subdural Spencer-type electrode arrays permitted high-quality ECoG recording. During a cumulative monitoring period of 1,194.5 hours and a median monitoring period of 201.3 (interquartile range: 126.1-209.4) hours per patient, 84 SDs were identified. Numbers of SDs, isoelectric SDs and clustered SDs per recording day, and peak total SD-induced depression duration of a recording day were not significantly different from the previously reported results of the prospective, observational, multicenter, cohort, diagnostic phase III trial, DISCHARGE-1. No adverse events related to electrode implantation were noted. In conclusion, our findings support the safety and feasibility of less-invasive subdural electrode implantation for reliable SD-monitoring.

Automatic Assessment of Parkinson's Disease Using Speech Representations of Phonation and Articulation

Speech from people with Parkinson's disease (PD) are likely to be degraded on phonation, articulation, and prosody. Motivated to describe articulation deficits comprehensively, we investigated 1) the universal phonological features that model articulation manner and place, also known as speech attributes, and 2) glottal features capturing phonation characteristics. These were further supplemented by, and compared with, prosodic features using a popular compact feature set and standard MFCC. Temporal characteristics of these features were modeled by convolutional neural networks. Besides the features, we were also interested in the speech tasks for collecting data for automatic PD speech assessment, like sustained vowels, text reading, and spontaneous monologue. For this, we utilized a recently collected Finnish PD corpus (PDSTU) as well as a Spanish database (PC-GITA). The experiments were formulated as regression problems against expert ratings of PD-related symptoms, including ratings of speech intelligibility, voice impairment, overall severity of communication disorder on PDSTU, as well as on the Unified Parkinson's Disease Rating Scale (UPDRS) on PC-GITA. The experimental results show: 1) the speech attribute features can well indicate the severity of pathologies in parkinsonian speech; 2) combining phonation features with articulatory features improves the PD assessment performance, but requires high-quality recordings to be applicable; 3) read speech leads to more accurate automatic ratings than the use of sustained vowels, but not if the amount of speech is limited to correspond to the sustained vowels in duration; and 4) jointly using data from several speech tasks can further improve the automatic PD assessment performance.

Conductive and antibacterial dual-network hydrogel for soft bioelectronics.

Conductive hydrogels have shown significant potential for use in soft bioelectronics due to their unique similarities to biological tissue, including high water content, low modulus, and conductivity. However, their high water content makes them susceptible to absorbing microorganisms and promoting bacterial growth, which can trigger an immune response. Besides, the adhesion and biocompatibility of the hydrogel are not satisfactory, seriously limiting the conductive hydrogel's high-performance applications in human healthcare monitoring. Herein, the problem is addressed by introducing borax through a swelling and a semi-dehydration method into the interpenetrated network of a polyvinyl alcohol and poly(acrylic acid) hydrogel. The hydrogel exhibits both outstanding antibacterial (>99.99% toward E. coli and S. aureus) activity and high ionic conductivity, in addition to tissue-like softness, strong wet-tissue adhesion (600 J m-2 for skin), environmental stability, and excellent biocompatibility. Furthermore, the as-prepared hydrogel can serve as a biosensing conductor, showing high-quality recording and monitoring of real-time tiny yet complex muscle movements during speaking and realizing neuromodulation through low-current electronic stimulation (40 μA) of a rat's nerve. Simultaneously, the hydrogel also exhibits the capacity to accelerate wound healing. Therefore, the proposed antibacterial conductive hydrogel is a safer option for next-generation bioelectronic materials in human healthcare.

Tuning Systems of Traditional Georgian Singing Determined from a New Corpus of Field Recordings

In this study we examine the tonal organization of the 2016 GVM dataset, a newly-created corpus of high-quality multimedia field recordings of traditional Georgian singing with a focus on Svaneti. For this purpose, we developed a new processing pipeline for the computational analysis of non-western polyphonic music which was subsequently applied to the complete 2016 GVM dataset. To evaluate under what conditions a single tuning system is representative of current Svan performance practice, we examined the stability of the obtained tuning systems from an ensemble-, a song-, and a corpus-related perspective. Furthermore, we compared the resulting Svan tuning systems with the tuning systems obtained for the Erkomaishvili dataset (Rosenzweig et al., 2020) in the study by Scherbaum et al. (2020). In comparison to a 12-TET (12-tone-equal- temperament) system, the Erkomaishvili and the Svan tuning systems are surprisingly similar. Both systems show a strong presence of pure fourths (500 cents) and fifths (700 cents), and ‘neutral’ thirds (peaking around 350 cents) as well as ‘neutral’ sixths. In addition, the sizes of the melodic and the harmonic seconds in both tuning systems differ systematically from each other, with the size of the harmonic second being systematically larger than the melodic one.

The Impact of Nurse Health-Coaching Strategies on Cognitive-Behavioral Outcomes in Older Adults.

The practice of nurse health coaching (NHC) draws from the art and science of nursing, behavioral sciences, and evidence-based health-coaching methods. This secondary analysis of the audio-recorded natural language of participants during NHC sessions of our recent 8-week RCT evaluates improvement over time in cognitive−behavioral outcomes: change talk, resiliency, self-efficacy/independent agency, insight and pattern recognition, and building towards sustainability. We developed a measurement tool for coding, Indicators of Health Behavior Change (IHBC), that was designed to allow trained health-coach experts to assess the presence and frequency of the indicators in the natural language content of participants. We used a two-step method for randomly selecting the 20 min audio-recorded session that was analyzed at each time point. Fifty-six participants had high-quality audio recordings of the NHC sessions. Twelve participants were placed in the social determinants of health (SDH) group based on the following: low income (<USD 20,000/year), early-onset hypertension, and social disadvantages. Our analyses significantly improved change talk and the other four factors over time. Our factor analyses indicated two distinct factors at each measurement point of the study, demonstrating the stability of the outcome measures over time. Our newly developed measurement tool, IHBC, proved stable in structure over time and sensitive to change. This NHC program shows promise in improving cognitive−behavioral indicators associated with health behavior change in both non-SDH and SDH individuals.

Scalable and Robust Hollow Nanopillar Electrode for Enhanced Intracellular Action Potential Recording.

Electrophysiology is a unique biomarker of the electrogenic cells that can perform a disease investigation or drug assessment. In the recent decade, vertical nanoelectrode arrays can successfully achieve a high-quality intracellular electrophysiological study in electrogenic cells and their networks. However, a high success rate and high-quality and long-term intracellular recording using low-cost nanostructures is still a considerable challenge. Herein, we develop a scalable and robust hollow nanopillar electrode to achieve enhanced intracellular recording of cardiomyocytes. The template-based synthesis of vertical hollow nanopillars is compatible with large-scale and efficient microfabrication processes and is convenient to regulate the geometry of hollow nanopillars. Compared with the conventional same-size planar electrode, the regulating height of a hollow nanopillar can achieve high-quality and prolonged intracellular recordings, which can improve the cell-electrode interface for tight coupling and effective electroporation. It is demonstrated that the geometry regulation of a nanostructure is a powerful strategy to enhance intracellular recording.

Scratch-AID: a deep-learning based system for automatic detection of mouse scratching behavior with high accuracy.

Mice are the most commonly used model animals for itch research and for development of anti-itch drugs. Most labs manually quantify mouse scratching behavior to assess itch intensity. This process is labor-intensive and limits large-scale genetic or drug screenings. In this study, we developed a new system, Scratch-AID Automatic Itch Detection), which could automatically identify and quantify mouse scratching behavior with high accuracy. Our system included a custom-designed videotaping box to ensure high-quality and replicable mouse behavior recording and a convolutional recurrent neural network (CRNN) trained with frame-labeled mouse scratching behavior videos, induced by nape injection of chloroquine (CQ). The best trained network achieved 97.6% recall and 96.9% precision on previously unseen test videos. Remarkably, Scratch-AID could reliably identify scratching behavior in other major mouse itch models, including the acute cheek model, the histaminergic model, and a chronic itch model. Moreover, our system detected significant differences in scratching behavior between control and mice treated with an anti-itch drug. Taken together, we have established a novel deep learning-based system that is ready to replace manual quantification for mouse scratching behavior in different itch models and for drug screening.

Beyond a Lush Life: Representation and Performativity at America's [Only] Queer Jazz Festival

Beyond a Lush Life: Representation and Performativity at America's [Only] Queer Jazz Festival

An Expert Consensus to Standardize Assessment of Bowel Cleansing for Clinical Trials of Bowel Preparations for Crohn's Disease.

Despite regular need for colonoscopy in patients with Crohn's disease (CD), the efficacy and tolerability of bowel preparation (BP) agents is rarely assessed in this population. Assessing BP quality with existing scales may be challenging in CD due to presence of inflammation, bowel resection, and strictures. To provide recommendations for assessing BP quality in clinical trials for CD using a modified Research and Development/University of California, Los Angeles appropriateness process. Based on systematic reviews and a literature search, 110 statements relating to BP quality assessment in CD were developed. A panel of 15 gastroenterologists rated the statements as appropriate, uncertain, or inappropriate using a 9-point Likert scale. Panelists considered it appropriate that central readers, either alone or with local assessment, score BP quality in clinical trials. Central readers should be trained on scoring BP quality and local endoscopists on performing high-quality video recording. Both endoscope insertion and withdrawal phases should be reviewed to score BP quality in each colonic segment and segments should align with endoscopic disease activity indices. The Harefield Cleansing Scale and the Boston Bowel Preparation Scale were considered appropriate. The final score should be calculated as the average of all visualized segments. Both total and worst segment scores should also be assessed. We developed a framework for assessing BP quality in patients with CD based on expert feedback. This framework could support the development or refinement of BP quality scales and the integration of BP quality assessment in future CD studies.

Exploring Collection of Sign Language Videos through Crowdsourcing

Inadequate sign language data currently impedes advancement of sign language ML and AI. Training on existing datasets results in limited models due to small size, and lack of diverse signers in real-world settings. Complex labeling problems in particular often limit scale. In this work, we explore the potential for crowdsourcing to help overcome these barriers. To do this, we ran a user study with exploratory crowdsourcing tasks designed to support scalability: 1) to record videos of specific content -- thereby enabling automatic, scalable labeling -- and 2) to perform quality control checks for execution consistency -- further reducing post-processing requirements. We also provided workers with a searchable view of the crowdsourced dataset, to boost engagement and transparency and align with Deaf community values. Our user study included 29 participants using our exploratory tasks to record 1906 videos and perform 2331 quality control checks. Our results suggest that a crowd of signers may be able to generate high-quality recordings and perform reliable quality control, and that the signing community values visibility into the resulting dataset.

A transient dual-type sensor based on MXene/cellulose nanofibers composite for intelligent sedentary and sitting postures monitoring

Sedentary and poor sitting postures will significantly raise the risk of cardiovascular disease and musculoskeletal disorders in humans. Thus, it is urgent to realize sedentary and sitting postures monitoring through various types of sensors. However, these sensors are commonly rigid, and non-degradable with low sensitivity and sensing range, which greatly restricts their practical applications in seamless, eco-friendly, and accurate monitoring in real-time. Here, we report flexible, degradable, transient, highly sensitive MXene/cellulose nanofibers (CNF) dual-type sensors including pressure sensor and bioelectric sensor. The pressure sensor is fabricated with hierarchical multi-layer MXene/CNF (MMC) as sensing layer, which shows wide detection range (0–950 kPa), high sensitivity (34.6 kPa−1), excellent stability (3000 cycles), transient and degradability. Moreover, the bioelectric sensor is obtained by delaminated MXene/CNF (DMC) sensing film, exhibiting high conductive, low contact impedance in high-quality electrocardiogram signal recording. Furthermore, with the aid of the deep neural network, the dual sensors can be integrated into an intelligent health monitoring system to achieve continous electrocardiogram, heart rate, sedentary, and sitting posture monitoring simultaneously. The system can realize multi-users identification and sitting postures recognition with high accuracy (>97%), showing great potential in health status monitoring with more privacy compared with previous methods.

High-sensitive and ultra-wide spectrum multifunctional triboelectric acoustic sensor for broad scenario applications

Based on the unique capacitance model of triboelectric technology, here, a novel self-powered triboelectric acoustic sensor (T-MIC) was fabricated, which is capable of simultaneously sensing acoustic waves not only covering the full frequency range of human hearing from 20 to 20,000 Hz, but also possessing higher frequency resolution (accuracy 1 Hz) and higher sensitivity (21.50 times) than the best reported triboelectric acoustic sensor. The T-MIC is faultless competent for commercial applications in high-quality music recording, wireless video communication, voice content inputting and human-robot interaction. Furthermore, based on the principle of acoustics, a theoretical model of T-MIC is proposed. Considering a broad selection of materials and the simpler preparation process of T-MIC, it has great advantages than that of many commercial acoustic wave sensors in performance and cost. So, the T-MIC can provide a feasible low-cost option for perception and detection of the acoustic waves in the commercial applications.