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Extremely stretchable self‐healing strain sensors based on conductive hydrogels are successfully fabricated. The strain sensor can achieve autonomic self‐heal electrically and mechanically under ambient conditions, and can sustain extreme elastic strain (1000%) with high gauge factor of 1.51. Furthermore, the strain sensors have good response, signal stability, and repeatability under various human motion detections.

In recent years, multi-modal flexible tactile sensors have become an important direction in the development of electronic skin because of their excellent sensitivity, flexibility and wearable properties. In this work, a humidity-pressure multi-modal flexible sensor based on polypyrrole (PPy)/Ti 3 C 2 T x sensitive film packaged with porous polydimethylsiloxane (PDMS) is investigated by combining the sensitive structure generation mechanism of in situ polymerization to achieve the simultaneous detection of humidity and pressure, which has a sensitivity of 89,113.4 Ω/% RH in a large humidity range of 0%–97% RH, and response/recovery time of 2.5/1.9 s. The tactile pressure sensing has a high sensitivity, a fast response of 67/52 ms, and a wide detection limit. The device also has excellent performance in terms of stability and repeatability, making it promising for respiratory pattern and motion detection. This work provides a new solution to address the construction of multi-modal tactile sensors with potential applications in the fields of medical health, epidemic prevention.

Insect nervous systems offer unique advantages for studying interactions between sensory systems and behavior, given their complexity with high tractability. By examining the neural coding of salient environmental stimuli and resulting behavioral output in the context of environmental stressors, we gain an understanding of the effects of these stressors on brain and behavior and provide insight into normal function. The implication of neonicotinoid (neonic) pesticides in contributing to declines of nontarget species, such as bees, has motivated the development of new compounds that can potentially mitigate putative resistance in target species and declines of nontarget species. We used a neuroethologic approach, including behavioral assays and multineuronal recording techniques, to investigate effects of imidacloprid (IMD) and the novel insecticide sulfoxaflor (SFX) on visual motion-detection circuits and related escape behavior in the tractable locust system. Despite similar LD50 values, IMD and SFX evoked different behavioral and physiological effects. IMD significantly attenuated collision avoidance behaviors and impaired responses of neural populations, including decreases in spontaneous firing and neural habituation. In contrast, SFX displayed no effect at a comparable sublethal dose. These results show that neonics affect population responses and habituation of a visual motion detection system. We propose that differences in the sublethal effects of SFX reflect a different mode of action than that of IMD. More broadly, we suggest that neuroethologic assays for comparative neurotoxicology are valuable tools for fully addressing current issues regarding the proximal effects of environmental toxicity in nontarget species.

The development of strain sensors with both superior sensitivity (gauge factor (GF) >100) and broad strain-sensing range (>50% strain) is still a grand challenge. Materials, which demonstrate significant structural deformation under microscale motion, are required to offer high sensitivity. Structural connection of materials upon large-scale motion is demanded to widen strain-sensing range. However, it is hard to achieve both features simultaneously. Herein, we design a crepe roll structure-inspired textile yarn-based strain sensor with one-dimensional (1D)-two-dimensional (2D) nanohybrid strain-sensing sheath, which possesses superior stretchability. This ultrastretchable strain sensor exhibits a wide and stable strain-sensing range from micro-scale to large-scale (0.01%–125%), and superior sensitivity (GF of 139.6 and 198.8 at 0.01% and 125%, respectively) simultaneously. The strain sensor is structurally constructed by a superelastic 1D-structured core elastomer polyurethane yarn (PUY), a novel high conductive crepe roll-structured (CRS) 1D-2D nanohybrid multilayer sheath which assembled by 1D nanomaterials silver nanowires (AgNWs) working as bridges to connect adjacent layers and 2D nanomaterials graphene nanoplates (GNPs) offering brittle lamellar structure, and a thin polydopamine (PDA) wrapping layer providing protection in exterior environment. During the stretching/deformation process, microcracks originate and propagate in the GNPs lamellar structure enable resistance to change significantly, while AgNWs bridge adjacent GNPs to accommodate applied stress partially and boost strain. The 1D crepe roll structure-inspired strain sensor demonstrates multifunctionality in multiscale deformative motion detection, such as respiratory motions of Sprague–Dawleyw rat, flexible digital display, and proprioception of multi-joint finger bending and antagonistic flexion/extension motions of its flexible continuum body.

Attentional selectivity tends to follow events considered as interesting stimuli. Indeed, the motion of visual stimuli present in the environment attract our attention and allow us to react and interact with our surroundings. Extracting relevant motion information from the environment presents a challenge with regards to the high information content of the visual input. In this work we propose a novel integration between an eccentric down-sampling of the visual field, taking inspiration from the varying size of receptive fields (RFs) in the mammalian retina, and the Spiking Elementary Motion Detector (sEMD) model. We characterize the system functionality with simulated data and real world data collected with bio-inspired event driven cameras, successfully implementing motion detection along the four cardinal directions and diagonally.

In upright standing and walking, the motion of the body relative to the environment is estimated from a combination of visual, vestibular and somatosensory cues. Associations between vestibular or somatosensory impairments and balance problems are well established, but less is known whether visual motion detection thresholds affect upright balance control. Typically, visual motion threshold values are measured while sitting, with the head fixated to eliminate self-motion. In this study we investigated whether visual motion detection thresholds: 1) can be reliably measured during standing and walking in the presence of natural self-motion; and 2) differ during standing and walking. Methods: Twenty-nine subjects stood on and walked on a self-paced, instrumented treadmill inside a virtual visual environment projected on a large dome. Participants performed a 2-alternative forced choice experiment in which they discriminated between a counterclockwise (\"left\") and clockwise (\"right\") rotation of a visual scene. A 6-down 1-up adaptive staircase algorithm was implemented to change the amplitude of the rotation. A psychometric fit to the participants' binary responses provided an estimate for the detection threshold Results: We found strong correlations between the repeated measurements in both the walking (R = 0.84, p < 0.001) and the standing condition (R = 0.73, p < 0.001) as well as good agreement between the repeated measures with Bland-Altman plots. Average thresholds during walking (mean = 1.04 degrees, SD = 0.43 degrees) were significantly higher than during standing (mean = 0.73 degrees, SD = 0.47 degrees). Conclusion: Visual motion detection thresholds can be reliably measured during both walking and standing, and thresholds are higher during walking.

High-performance wearable sensors, especially resistive pressure and strain sensors, have shown to be promising approaches for the next generation of health monitoring. Besides being skin-friendly and biocompatible, the required features for such types of sensors are lightweight, flexible, and stretchable. Cellulose-based materials in their different forms, such as air-porous materials and hydrogels, can have advantageous properties to these sensors. For example, cellulosic sensors can present superior mechanical properties which lead to improved sensor performance. Here, recent advances in cellulose-based pressure and strain sensors for human motion detection are reviewed. The methodologies and materials for obtaining such devices and the highlights of pressure and strain sensor features are also described. Finally, the feasibility and the prospects of the field are discussed.

With the development and improvement of artificial intelligence technology, the teaching innovation mode of combining college sports courses with artificial intelligence has gradually received widespread attention. This paper is based on artificial intelligence technology for the design of intelligent sports detection wearable devices, through which students’ sports data are collected, low-pass filters are used to reduce the noise of the collected data, normalization is carried out, and the AlphaPose algorithm is combined with the assessment and extraction of the human movement posture of college sports. The artificial intelligence sports teaching framework is built to innovate college sports teaching based on artificial intelligence. Finally, the impact of sports recognition is examined, and a comparison experiment is carried out to examine the practical implications of this teaching method. The four experimental targets had sports recognition errors that were less than 10% on average according to the results. The two classes under the experimental control have a P-value of less than 0.05 in the comparison of physical skills and physical fitness test data, and there is an improvement of 5-20 in all scores, which indicates that the experimental class has a higher teaching effect and is significantly helpful in performance improvement. Based on the above, this paper researches the practice of artificial intelligence technology in college sports teaching to provide an innovative path for the transformation of traditional sports teaching to artificial intelligence sports teaching.

Building an efficient and reliable small target motion detection visual system is challenging for artificial intelligence robotics, because a small target only occupies few pixels and hardly displays visual features in images. Biological visual systems that have evolved over millions of years could be ideal templates for designing artificial visual systems. Insects benefit from a class of specialized neurons, called small target motion detectors (STMDs), which endow them with excellent ability to detect small moving targets against cluttered dynamic environment. Some bio-inspired models featured in feed-forward information processing architectures have been proposed to imitate functions of the STMD neurons. However, feedback, a crucial mechanism for visual system regulation, has not been investigated deeply in the STMD-based neural circuits and its roles in small target motion detection remain unclear. In this paper, we propose a time-delay feedback STMD model for small target motion detection in complex background. The main contributions of this study are as follows. First, a feedback pathway is designed by transmitting information from output-layer neurons to lower-layer interneurons in the STMD pathway and the role of the feedback is analyzed from the view of mathematical analysis. Second, to estimate the feedback constant, the existence and uniqueness of solutions for nonlinear dynamical systems formed by feedback loop are analyzed via Schauder's fixed point theorem and contraction mapping theorem. Finally, an iterative algorithm is designed to solve the nonlinear problem and the performance of the proposed model is tested via experiments. Experimental results demonstrate that the feedback is able to weaken background false positives while maintaining a minor effect to small target. It outperforms existing STMD-based models regarding the accuracy of fast-moving small target detection in visual clutter. The proposed feedback approach could inspire the relevant modeling of robust motion perception robotics visual systems.

In dynamic and complex real-world environments, artificial intelligence (AI) vision systems continue to face significant challenges in accurately detecting and tracking small objects. The core difficulty lies in the fact that small targets usually exhibit limited spatial and textural features, while dynamic backgrounds often generate numerous misleading motion cues, thereby interfering with reliable discrimination between targets and backgrounds. Inspired by the remarkable capability of the insect brain in detecting small moving objects, this study proposes a visual neural network model enhanced by a feedback mechanism. By adaptively responding to temporal variations, the proposed model is able to more precisely distinguish small targets from background-induced false targets. The network architecture consists of two main pathways: a motion detection pathway that extracts motion-related features from minute targets, and a feedback attention pathway that enhances the focus on true targets by leveraging the feature differences between real and false motion signals. In addition, a global inhibition module is incorporated to reduce the false alarm rate by filtering out background-induced false positives, thereby improving the overall detection performance of the model. Experimental results demonstrate that the proposed model achieves a detection rate of 0.81 in complex visual scenarios, whereas the compared models all achieve detection rates below 0.59, indicating a significant improvement in detection performance. Meanwhile, in terms of Precision and F1-score, the proposed model achieves values of 0.0648 and 0.12, respectively, while the compared models obtain values lower than 0.0077 and 0.015, further validating the superiority of the proposed method in detection accuracy and robustness.