A Batch Rival Penalized Expectation-Maximization Algorithm for Gaussian Mixture Clustering with Automatic Model Selection

Within the learning framework of maximum weighted likelihood (MWL) proposed by Cheung, 2004 and 2005, this paper will develop a batch Rival Penalized Expectation-Maximization (RPEM) algorithm for density mixture clustering provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm in Cheung, 2004 and 2005, this batch RPEM need not assign the learning rate analogous to the Expectation-Maximization (EM) algorithm (Dempster et al., 1977), but still preserves the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM in general. The experiments show the superior performance of the proposed algorithm on the synthetic data and color image segmentation

AI-enabled soft sensing array for simultaneous detection of muscle deformation and mechanomyography for metaverse somatosensory interaction

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age

Mapping of spatiotemporal auricular electrophysiological signals reveals human biometric clusters

Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, a 3D graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes for full-auricle physiological monitoring is reported. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject-specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region-specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning-based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR-based biometrical findings show promising biomedical applications

Wide‐bandwidth nanocomposite‐sensor integrated smart mask for tracking multiphase respiratory activities

Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID-19) even in its coming endemic phase. Therefore, deploying a “smart mask” to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure-based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide-bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty-one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro-recalls of ≈95% in both individual and generalized models. With rich high-frequency (≈4000 Hz) information recorded, the two-/tri-phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life

The Potential Use of Vitamin C to Prevent Kidney Injury in Patients with COVID-19

The newly found SARS-CoV-2 has led to the pandemic of COVID-19, which has caused respiratory distress syndrome and even death worldwide. This has become a global public health crisis. Unfortunately, elders and subjects with comorbidities have high mortality rates. One main feature of COVID-19 is the cytokine storm, which can cause damage in cells and tissues including the kidneys. Here, we reviewed the current literature on renal impairments in patients with COVID-19 and analyzed the possible etiology and mechanisms. In addition, we investigated the potential use of vitamin C for the prevention of renal injury in those patients. It appears that vitamin C could be helpful to improve the outcomes of patients with COVID-19. Lastly, we discussed the possible protective effects of vitamin C on renal functions in COVID-19 patients with existing kidney conditions

Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin

With the development of 5G and Internet of Things (IoT), the era of big data‐driven product design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by recent breakthroughs in computing power and software architectures. In this regard, the digital twin, analyzing various sensor data with the help of AI algorithms, has become a cutting‐edge technology that connects the physical and virtual worlds, in which the various sensors are highly desirable to collect environmental information. However, although existing sensor technologies, including cameras, microphones, inertial measurement units, etc., are widely used as sensing elements for various applications, high‐power consumption and battery replacement of them is still a problem. Triboelectric nanogenerators (TENGs) as self‐powered sensors supply a feasible platform for realizing self‐sustainable and low‐power systems. Herein, the recent progress on TENG‐based intelligent systems, that is, wearable electronics, robot‐related systems, and smart homes, followed by prospective future development enabled by sensor fusion technology, is focused on. Finally, how to apply artificial intelligence to the design of intelligent sensor systems for the 5G and IoT era is discussed

A new strategy for enhancing the room temperature conductivity of solid-state electrolyte by using a polymeric ionic liquid

An ether-functionalized polymeric ionic liquid poly(methyl methacrylate-1-vinyl-3-methoxyl-ethyl-imidazolium bis(trifluoromethanesulfonyl)imide) P(MMA-co-VIm(1O2)) (TFSI) polymeric ionic liquid (PIL) was successfully synthesized, characterized, and used as polymer matrix. The performances of solid polymer electrolytes membrane were measured through blending with poly(vinylidene fluoride-cohexafluoropropylene) (PVDF-HFP) and 1-propyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl) imide ([Pyr(13)][TFSI]). The ionic conductivity of polymer electrolytes was optimized up to 5.10 x 10(-4)S cm(-1)at 25 degrees C, and a wide electrochemical window of 5.23 V vs Li/Li(+)could be obtained. Moreover, the polymer electrolytes showed excellent cycle performance for Li/LiFePO(4)cell at both 25 degrees C and 60 degrees C, demonstrating the capability of being a promising candidate for the application of solid-state lithium-ion batteries.</p

A new solid-state electrolyte based on polymeric ionic liquid for high-performance supercapacitor

As a polymer host, one polymeric ionic liquid poly(methyl methacrylate-1-vinyl-3-ethyl-imidazolium bis(trifluoromethylsulfonyl) imide) (abbr. P(MMA-co-VEImTFSI)) was successfully synthesized and characterized. Four poly(vinylidene fluoride-co-hexafluoropropylene) (abbr. PVDF-HFP)-based polymer electrolytes were prepared by blending 0, 5,15, and 25wt% P(MMA-co-VEImTFSI). The electrochemical performances of the prepared electrolytes were studied carefully. The results revealed that increasing the polymeric ionic liquid content, the ionic conductivity of the polymer electrolytes could be enhanced and it obeyed the Arrhenius rule. The highest ionic conductivity of the polymer electrolytes was up to 2.09x10(-3)Scm(-1) at room temperature. The polymer with 25wt% polymeric ionic liquid showed an excellent electrochemical performance for supercapacitor electrolyte. After 2000 cycles, the retention of capacitance in P(MMA-co-VEImTFSI)-based polymer electrolyte was above 80%. It implied that the present P(MMA-co-VEImTFSI) polymeric ionic liquid was a decent component candidate in the high-performance polymer electrolytes.</p

AI‐Enabled Micro Motion Sensors for Revealing the Random Daily Activities of Caged Mice

More than 120 million mice and rats are used yearly for scientific purposes. While tracking their motion behaviors has been an essential issue for the past decade, present techniques, such as video‐tracking and IMU‐tracking have considerable problems, including requiring a complex setup or relatively large IMU modules that cause stress to the animals. Here, we introduce a wireless IoT motion sensor (i.e., weighing only 2 g) that can be attached and carried by mice to collect motion data continuously for several days. We also introduce a combined segmentation method and an imbalanced learning process that are critical for enabling the recognition of common but random mouse behaviors (i.e., resting, walking, rearing, digging, eating, grooming, drinking water, and scratching) in cages with a macro‐recall of 94.55%. An interactive preprint version of the article can be found at: https://doi.org/10.22541/au.166005321.10787501/v1