Accurate Perception Research Articles

Distortion of body image perception in the Prader-Willi syndrome: Relationship with the perceptual reasoning index

IntroductionSelf-perception of body image has been scarcely evaluated in people with Prader-Willi syndrome (PWS), who, in addition to intellectual disability, are often obese. Therefore, we explored whether people with PWS can accurately identify their true image and how this self-perception is impacted by their neuropsychological profile. MethodologyThis observational study included patients with PWS with regular attendance to transdisciplinary treatment at a center specialized in the management of rare diseases. All patients were evaluated with the Stunkard scale (including silhouettes ranging from extremely skinny to extremely obese) and the WISC-IV and WAIS-III questionnaires, specifically the perceptual reasoning index (PRI). ResultsAmong the 21 participants, 62% misperceived their body image, most underestimating their body dimensions (actual BMI 28.0±8.3kg/m2 vs self-perceived BMI 23.2±4.7kg/m2, p=0.03). While BMI differences between accurate and inaccurate body image perception were nonsignificant (accurate 26.6±8.8kg/m2 vs inaccurate 28.9±8.1kg/m2, p=0.56), individuals with accurate perception showed both higher PRI scores (accurate 67.6±8.2 vs inaccurate 60.2±7.2, p=0.043) and, to a lesser extent, intelligence quotients (IQ) (accurate 60.0±4.9 vs inaccurate 55.0±7.5, p=0.079). ConclusionIn this study, we identified distortion of body image perception as a very common finding among PWS patients, in most cases as underestimation, and influenced by the neuropsychological profile.

An Adaptive Multimodal Fusion 3D Object Detection Algorithm for Unmanned Systems in Adverse Weather

Unmanned systems encounter challenging weather conditions during obstacle removal tasks. Researching stable, real-time, and accurate environmental perception methods under such conditions is crucial. Cameras and LiDAR sensors provide different and complementary data. However, the integration of disparate data presents challenges such as feature mismatches and the fusion of sparse and dense information, which can degrade algorithmic performance. Adverse weather conditions, like rain and snow, introduce noise that further reduces perception accuracy. To address these issues, we propose a novel weather-adaptive bird’s-eye view multi-level co-attention fusion 3D object detection algorithm (BEV-MCAF). This algorithm employs an improved feature extraction network to obtain more effective features. A multimodal feature fusion module has been constructed with BEV image feature generation and a co-attention mechanism for better fusion effects. A multi-scale multimodal joint domain adversarial network (M2-DANet) is proposed to enhance adaptability to adverse weather conditions. The efficacy of BEV-MCAF has been validated on both the nuScenes and Ithaca365 datasets, confirming its robustness and good generalization capability in a variety of bad weather conditions. The findings indicate that our proposed algorithm performs better than the benchmark, showing improved adaptability to harsh weather conditions and enhancing the robustness of UVs, ensuring reliable perception under challenging conditions.

Robust lithium-ion battery state of health estimation based on recursive feature elimination-deep Bidirectional long short-term memory model using partial charging data

Accurate perception of the state of health (SOH) of lithium-ion batteries is crucial for their safety and reliable operation. To meet this demand, a recursive feature elimination-deep bidirectional long short-term memory (RFE-DBiLSTM) model suitable for partial charging data is proposed to effectively estimate the SOH of lithium-ion batteries. In this study, the recursive feature elimination (RFE) method is used to screen multiple charging features for obtaining the key features that best represent the SOH under two scenarios with different charging segment lengths. Due to the robust noise-filtering capability and strong ability to capture complex and multi-level temporal dependencies, the deep bidirectional long short-term memory (DBiLSTM) model is used for time series data training, verification, and testing during aging. Experimental results show that compared with benchmark time series models such as long short-term memory (LSTM) and gated recurrent unit (GRU), the proposed method significantly reduces the estimated mean absolute error (MAE) and root mean square error (RMSE) on diverse batteries in the above scenarios. In the scenario for missing partial constant current (CC) charging data, the MAE and RMSE of B0005 cell are 0.0062 and 0.0094, the MAE and RMSE of B0006 cell are 0.0294 and 0.0314, the MAE and RMSE of CS2_36 cell are 0.0510 and 0.0601, the MAE and RMSE of B0029 cell are 0.0057 and 0.0072, and the MAE and RMSE of B0030 cell are 0.0088 and 0.0102. This research innovatively combines the RFE method with the DBiLSTM model to improve the accuracy and robustness of SOH estimation.

Effectiveness of a Novel Web-Based Intervention to Enhance Therapeutic Relationships and Treatment Outcomes in Adult Individual Psychotherapy: Randomized Controlled Trial and Analysis of Predictors of Dropouts.

Routine process and outcome monitoring interventions added to psychotherapy are known to improve treatment outcomes, although they vary in format and effectiveness. This study aimed to evaluate whether a therapist-independent, internet-based routine process monitoring and feedback system could significantly reduce psychological distress and enhance the quality of the therapeutic relationship compared with a treatment-as-usual control group among individuals already engaged in individual psychotherapy. We randomized 475 participants into either the intervention group, which received access to an internet-based routine process monitoring and feedback system in addition to psychotherapy, or the control group, which received only psychotherapy. The trial lasted for 10 weeks. Follow-up assessments at 5 weeks and 10 weeks used the Clinical Outcomes in Routine Evaluation-Outcome Measure as the primary outcome, with the Working Alliance Inventory-Short Revised and the Real Relationship Inventory-Client form as secondary outcomes. Per-protocol analyses (n=166) showed that psychological distress decreased in both groups, but there was no significant advantage for the intervention group. The intervention group experienced a decline in the genuineness dimension score of the real relationship, with an effect size of d=-0.27, compared with d=0.01 in the control group. In the intervention group (but not in the control group), dropouts showed significantly lower real relationship levels (P=.002), working alliance quality (P=.051), and emotional disclosure (P=.01) compared with those who completed the study. Additionally, logistic regression revealed distinct predictors of dropout within the control group and intervention group. The findings do not provide conclusive evidence for the efficacy of the new internet-based intervention in enhancing self-monitoring and prompting reflection on patients' emotional responses to their therapists. However, the intervention appears to influence patients' perceptions of the genuineness dimension in the therapeutic relationship, warranting further investigation. We hypothesize that this alteration in the genuineness dimension could be attributed to the intervention facilitating a more realistic and accurate perception of the therapeutic relationship among participants. ClinicalTrials.gov NCT06038747; https://clinicaltrials.gov/study/NCT06038747. RR2-10.2196/55369.

Multi-Sensor Information Fusion Technology in Robots

Multi-sensor information fusion is a technology that collects and processes data from multiple sensors to obtain more comprehensive and accurate information. In the field of robotics, multi-sensor information fusion technology is frequently applied to robots since the use of a single sensor in robots is often limited, this will also improve the accuracy and robustness of robot environment perception. After researching and organizing multi-sensor information fusion technology, robotics technology and other related materials, this paper provides a comprehensive introduction to the application of multi-sensor information fusion technology in the field of robotics, describes the logical structure of multi-sensor information fusion technology in robotics, explains the algorithms of multi-sensor information fusion technology that are mainly applied to robots. Finally, this paper summarizes and exemplifies the main applications of multi-sensor information fusion technology in the field of robotics.

A Review of Gait Coordination Control Methods for Lower Limb Exoskeleton Robots Based on Visual Models

The ongoing evolution of exoskeleton technology has rendered the exclusive reliance on a single vision technology an impractical approach to addressing the complexities of diverse environments and individual requirements. This paper therefore explores the potential of combining traditional visual models with movement intention prediction based on surface electromyography (sEMG) and virtual reality (VR) technology in the context of gait control. The study concluded that traditional visual models are capable of providing accurate environmental perception and gait planning; however, they are deficient in forecasting user intentions. Surface electromyography (sEMG) technology can supplement this deficiency with muscle activity signals, enabling the system to respond dynamically to user needs. Concurrently, virtual reality (VR) technology furnishes users with a more personalized training program through immersive environmental simulation and feedback. The combination of these three technologies not only enhances the system's real-time responsiveness but also exhibits commendable adaptability in complex terrain and rehabilitation training. This paper examines the three fundamental technologies of lower limb exoskeleton robots in gait control and their fusion applications, with the objective of enhancing the system's intelligence and adaptability, providing a direction for the future development of lower limb exoskeleton technology, and highlighting the challenges and potential of multimodal data fusion and computational optimisation.

Optimization of a passive roll absorber for robotic fish based on tune mass damper

The robotic fish utilizes a bio-inspired undulatory propulsion system to achieve high swimming performance. However, significant roll motion has been observed at the head when the tail oscillates at certain frequencies, adversely affecting both perception accuracy and propulsion efficiency. In this paper, the roll torque acting on the robotic fish is theoretically analyzed and decomposed into gravitational, inertial, and hydrodynamic components. Resonance is identified as a key factor amplifying the roll response. To mitigate this roll and enhance stability, a passive roll absorber based on tuned mass damper is designed. A simplified rolling structure is dynamically modeled to optimize absorber parameters. Experiments are conducted to quantify the roll torque experienced by the robotic fish, with the effectiveness of the absorber verified on both the simplified model and the robotic fish. Results show that the maximum roll angle of the simplified system under harmonic load decreases from 98° to29∘, representing a reduction of over 70%, while a 25.1% reduction is achieved on the robotic fish.

Body size estimation is influenced by actual-ideal body size discrepancy: a series of studies among Chinese adolescent girls.

Many adolescent girls desire to have a thinner body, including those of normal weight. However, it is not fully known if this reflects solely a preference for a particular body type/size, or also is influenced by holding a distorted perception of one's body size. The current series of studies aimed to examine the (in)accuracy of body size perception among adolescent girls, as well as the extent to which distorted perceptions of body size extended to perceptions of other girls' bodies. In Study 1, 48 pairs of girls who desired to be thinner were asked to estimate the circumferences of three parts (arm, waist and thigh) of their own body and that of a study partner. Participants generally overestimated the circumferences of both their own and their partner's bodies, with this overestimation stable over a four-month follow-up. In contrast, in Study 2, no body size overestimation was observed for self or partners among 44 pairs of girls who were satisfied with their body size. Study 3 further revealed significant correlations between body size overestimation and disordered eating symptomology among 43 pairs of girls who desired to be thinner. As a whole, findings demonstrated that body size overestimation was limited to adolescent girls who were dissatisfied with their body size, and that body size overestimation was associated with disordered eating symptomology. Thus, perceptual distortions in body size may play a role in both body size dissatisfaction and disordered eating among adolescents.

Predicting Discrimination in L3 Portuguese by Hungarian Speakers: The Effect of Perceptual Overlap

Perceptual overlap has been attested as significantly contributing to difficulties in L2 speech perception. The current study aims at investigating whether this effect is also observable in the context of L3, specifically in the perception of European Portuguese oral vowels by Hungarian listeners. We crossed the results of two experiments—a categorization task and a discrimination task—and found that perceptual overlap is also a significant factor in L3 perception. Furthermore, we compared different measures of perceptual overlap as predictors for discrimination abilities of L3 vowel contrasts. Namely, we compared perceptual overlap scores calculated on group means and scores based on individual results. None of the measures was conclusively more reliable than another in predicting differences in discrimination difficulties. However, accuracy in perception of EP contrasts or vowels absent from the Hungarian vocalic system was significantly lower than for the other vowels, suggesting that non-nativeness can cause difficulties in L3 perception. Additionally, participants who also reported knowledge of German performed more accurately in discrimination of contrasts that included the vowel [ɐ], a vowel absent from their L1 but present in the German vocalic system, indicating a positive effect of knowledge of languages previously acquired on L3 perception.

Improved 3D laser point cloud reconstruction for autonomous mobile robot applications by using SVM-R technique

PurposeIn autonomous mobile robots, high-level accuracy and precision in 3D perception are required for object detection, shape estimation and obstacle distance measurement. However, the existing methods suffer from limitations like inaccurate point clouds, noise in sensor data and synchronization problems between 2D LiDAR and servomotor. These factors can lead to the wrong perception and also introduce noise during sensor registration. Thus, the purpose of this study is to address these limitations and enhance the perception in autonomous mobile robots.Design/methodology/approachA new sensor mounting structure is developed for 3D mapping by using a 2D LiDAR and servomotor. The proposed method uses a support vector machine regression (SVM-R) technique to optimize the waypoints of the servomotor for the point cloud reconstruction process and to obtain a highly accurate and detailed representation of the environment.FindingsThe study includes an analysis of the SVM-R model, including Linear, radial basis function (RBF) and Polynomial kernel. Results show that the Linear kernel performs better with the lowest 3.67 mean absolute error (MAE), 26.24 mean squared error (MSE) and 5.12 root mean squared error (RMSE) values than the RBF and Polynomial kernels. The 2D to 3D point cloud reconstruction shows that the proposed method with a new sensor mounting structure performs better in perception accuracy and achieves an error of 0.45% in measuring the height of the target objects whereas in previous techniques the error was very large.Originality/valueThe study shows the effectiveness of SVM-R in the 3D point cloud reconstruction process and exhibits remarkable performance for object height measurement. Further, the proposed technique is applicable for future advanced visual applications and has a superior performance over the other conventional methods.

Evaluating Visual Perception of Object Motion in Dynamic Environments

Precisely understanding how objects move in 3D is essential for broad scenarios such as video editing, gaming, driving, and athletics. With screen-displayed computer graphics content, users only perceive limited cues to judge the object motion from the on-screen optical flow. Conventionally, visual perception is studied with stationary settings and singular objects. However, in practical applications, we---the observer---also move within complex scenes. Therefore, we must extract object motion from a combined optical flow displayed on screen, which can often lead to mis-estimations due to perceptual ambiguities. We measure and model observers' perceptual accuracy of object motions in dynamic 3D environments, a universal but under-investigated scenario in computer graphics applications. We design and employ a crowdsourcing-based psychophysical study, quantifying the relationships among patterns of scene dynamics and content, and the resulting perceptual judgments of object motion direction. The acquired psychophysical data underpins a model for generalized conditions. We then demonstrate the model's guidance ability to significantly enhance users' understanding of task object motion in gaming and animation design. With applications in measuring and compensating for object motion errors in video and rendering, we hope the research establishes a new frontier for understanding and mitigating perceptual errors caused by the gap between screen-displayed graphics and the physical world.

Self-reconfigurable multifunctional memristive nociceptor for intelligent robotics

Abstract Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribute, which indicates that the threshold of determination of a potentially hazardous stimulus relies on environmental knowledge. This critical attribute has been currently omitted, but it is highly desired for artificial nociceptors. Inspired by these shortcomings, this article presents, for the first time, a Self-Directed Channel (SDC) memristor-based self-reconfigurable nociceptor, capable of perceiving hazardous pressure stimuli under different temperatures and demonstrates key features of tactile nociceptors, including ‘threshold,’ ‘no-adaptation,’ and ‘sensitization.’ The maximum amplification of hazardous external stimuli is 1000%, and its response characteristics dynamically adapt to current temperature conditions by automatically altering the generated modulation schemes for the memristor. The maximum difference ratio of the response of memristors at different temperatures is 500%, and this adaptability closely mimics the functions of biological tactile nociceptors, resulting in accurate danger perception in various conditions. Beyond temperature adaptation, this memristor-based nociceptor has the potential to integrate different sensory modalities by applying various sensors, thereby achieving human-like perception capabilities in real-world environments.

Interactions between the fovea and the periphery shape misbinding of visual features in a continuous report paradigm

Human object perception depends on the proper integration of multiple visual features, such as color and motion. When features are integrated incorrectly, they are perceptually misbound and can cause illusions. This study investigates the phenomenon of continuous misbinding of color and motion features in peripheral vision, addressing the role of spatial continuity and color configuration in binding processes. Using a novel continuous report task, human subjects reported the perceived movement direction of color-coded dots in the peripheral visual field that were either congruent or incongruent with a foveal stimulus. Results indicate that spatial continuity is crucial for feature misbinding, with a marked decrease in peripheral perceptions bound to match foveal perception when the foveal and peripheral stimuli are spatially disjointed. Additionally, performance improved significantly when peripheral and foveal features were aligned, suggesting a ‘positive illusion’ effect where congruent features enhance perceptual accuracy. This effect was independent of the color pairings used, challenging the notion that color processing discrepancies might drive erroneous perception in this stimulus. These findings highlight the complex interplay between spatial configuration and perceptual accuracy in visual feature integration, with implications for understanding the neural basis of vision and developing applications to address perceptual inaccuracies in visual disorders.

BEV-TinySpotter: A novel BEV perception method considering multi-dimensional feature fusion of small target

The BEV perceptual integrity depends on the ability to accurately capture multi-scale targets. However, the detection of multi-scale targets, especially small targets, is often affected by perceptual noise, information sparsity, and target occlusion, which subsequently affecting the accuracy of BEV perception and causing certain hidden danger to driving safety. To address these issues, we propose a novel BEV perception method BEV-TinySpotter (BEV-TS), which introduced two feature detection enhancement modules and one analytical causal reasoning module to highlighting small object features and improving the accuracy and robustness of small object detection. Specifically, we first enhance the feature recognition of small targets using the multi-scale spatial information acquisition module, and introduce the distribution and migration weights of small targets reflected in the time-series information. Then, we jointly employ global pooling and local pooling to differentiate the acquisition paths of the information to improve the sensitivity of capturing information for small targets. Additionally, we design causal inference graphs to post-fuse the recognition results of BEV, which utilizes causal reasoning to construct a state description mechanism for small targets, further enhancing the accuracy of BEV perception from a logical reasoning perspective. Compared with other SOTA BEV methods, the proposed method effectively enhances the ability to capture and infer the status of small targets during BEV perception, thus significantly improves the completeness of driving environment sensing.

Research on Fault Diagnosis of Agricultural IoT Sensors Based on Improved Dung Beetle Optimization–Support Vector Machine

The accuracy of data perception in Internet of Things (IoT) systems is fundamental to achieving scientific decision-making and intelligent control. Given the frequent occurrence of sensor failures in complex environments, a rapid and accurate fault diagnosis and handling mechanism is crucial for ensuring the stable operation of the system. Addressing the challenges of insufficient feature extraction and sparse sample data that lead to low fault diagnosis accuracy, this study explores the construction of a fault diagnosis model tailored for agricultural sensors, with the aim of accurately identifying and analyzing various sensor fault modes, including but not limited to bias, drift, accuracy degradation, and complete failure. This study proposes an improved dung beetle optimization–support vector machine (IDBO-SVM) diagnostic model, leveraging the optimization capabilities of the former to finely tune the parameters of the Support Vector Machine (SVM) to enhance fault recognition under conditions of limited sample data. Case analyses were conducted using temperature and humidity sensors in air and soil, with comprehensive performance comparisons made against mainstream algorithms such as the Backpropagation (BP) neural network, Sparrow Search Algorithm–Support Vector Machine (SSA-SVM), and Elman neural network. The results demonstrate that the proposed model achieved an average diagnostic accuracy of 94.91%, significantly outperforming other comparative models. This finding fully validates the model’s potential in enhancing the stability and reliability of control systems. The research results not only provide new ideas and methods for fault diagnosis in IoT systems but also lay a foundation for achieving more precise, efficient intelligent control and scientific decision-making.

Effects of global information on the estimation of point-light walker directions.

Studies have shown that observers can accurately estimate the point-light walker (PLW) directions and bias their estimates towards the previous directions, exhibiting a serial dependence. Additionally, it has been demonstrated that the inversion of PLWs disrupts their global information and impairs the perception of certain attributes of PLWs. However, it remained unclear whether the global information of PLWs affected the estimation accuracy and serial dependence in the PLW direction estimation. In the current study, participants were presented with either a single or four PLW directions in each trial. In one condition, the PLW was upright; while in another condition, the PLW was inverted. Participants were asked to estimate the direction of the single PLW or the average direction of the four PLWs (i.e., ensemble direction), and each condition was finished by an independent group of participants. The results showed that the estimates of PLW directions were systematically compressed towards the reference direction (0°), showing a reference-attraction bias, and the inversion of the PLWs significantly decreased the estimation accuracy in the ensemble direction perception but not in the single direction perception. Moreover, a stronger serial dependence was observed when four PLWs were presented in previous trials as opposed to just one, and its magnitude was also reduced when the four PLWs were inverted. Therefore, the current study demonstrated the effects of global information on the estimation of PLW directions.

"No doctor ever asked me…so I thought it wasn't a valid concern":endometriosis patients' perspectives of barriers and facilitators to sexual health communication in general practice.

Sexual health communication between general practitioners (GPs) and individuals with chronic diseases is well documented as an important component of sexual well-being. However, a notable gap exists in understanding factors that contribute to sexual health communication in the endometriosis context. Endometriosis-related research has focused on the assessment of sexual functioning and its role in influencing health-related quality of life, with a limited understanding of the management of sexual well-being in primary healthcare settings. To explore how individuals with endometriosis perceive their experiences of sexual health communication with GPs to understand the barriers and facilitators of this communication in general practice. This study formed part of a larger online quantitative survey measuring psychological and sexual well-being in individuals with endometriosis. In total 141 participants provided responses via open-ended unlimited free-text questions focused on their experiences of sexual health communication with GPs, including their perspectives on barriers and facilitators. Four overarching themes regarding barriers to sexual health communication were identified through thematic template analysis: (1) systemic factors; (2) practitioner factors; (3) patient factors; and (4) interpersonal factors. Facilitators mirrored strategies to address these barriers. Additionally, participants offered recommendations for developing resources to improve sexual health communication. Despite patients' interest, sexual health concerns in endometriosis are commonly overlooked in general practice due to various barriers, which individuals suggest could be addressed through comprehensive GP education and practical aids including communication tools. Although the sample is large for a qualitative study, the potential selection bias-stemming from participants' high levels of sexual dysfunction and motivation to discuss sexual health with a GP-may limit the diversity of perspectives. GP perspectives were not evaluated, precluding any assessment of the accuracy of individuals' perceptions of practitioner factors. Individuals with endometriosis encounter various systematic, practitioner, personal, and interpersonal factors that hinder or facilitate sexual health communication in general practice. Future research should prioritize strategies to improve sexual health communication for both GPs and patients.

Influence of habituation on pseudo-haptic weight perception of virtual objects

As the demand for realistic sensations in virtual reality (VR) environments escalates, interfaces that exploit cross-modal interactions between vision and haptics are gaining prominence. Pseudo-haptics, offering a tactile illusion without mechanical feedback devices, has emerged as a viable solution to enhance user immersion by simulating various sensations such as texture, fluid resistance, and weight. However, a potential issue in the long-term effectiveness of these illusions, particularly habituation, where the illusion diminishes after prolonged exposure, remains a concern. This study investigates whether habituation to pseudo-haptic weight illusions occurs with extended use. We conducted quantitative measurements of pseudo-haptic weight perception before and after prolonged exposure. The findings indicate a diminishing effect of pseudo-haptic weight illusion with repeated exposure, particularly among participants with high accuracy in weight perception. This study contributes important insights into the design of VR experiences, highlighting the need to consider the habituation effects when implementing pseudo-haptic illusions for weight perception.

Caregivers' cognitions about infants' mental and emotional states

AbstractGiven the relevance of caregivers' perceptions, cognitions, and emotions about their child's mental states for caregiving behavior and children's development, researchers from multiple theoretical perspectives have developed constructs to assess caregivers' cognitions, resulting in a large but scattered body of literature. In this article, we highlight the conceptual overlap among and uniqueness of six constructs assessing caregivers' cognitions about their child at 36 months and younger: infant intentionality, mental representations, mind‐mindedness, parental embodied mentalizing, parental empathy, and parental reflective functioning. We define constructs, present approaches to measurement, and propose elements of importance that fall under the umbrella of caregivers' cognitions and that may be associated differentially with children's early cognitive and social–emotional development. We conclude with recommendations for researchers aiming to capture caregivers' cognitions about their child's mental states, whether focusing on one of the six reviewed constructs or on specific elements (e.g., awareness of the child's mind or accuracy of caregivers' perceptions of their child) under the umbrella of caregivers' cognitions.

Low power tactile sensory neuron using nanoparticle-based strain sensor and memristor

Endowing strain sensors with neuromorphic computing capabilities could permit the efficient processing of tactile information on the edge. The realization of such functionalities from a simple circuit without software processing holds promise for attaining skin-based perception. Here, leveraging the intrinsic neuronal plasticity of memristive neurons, various firing patterns induced by the applied strain were demonstrated. More specifically, tonic, bursting, transition from tonic to bursting, adaptive, and nociceptive activities were captured. The implementation of these patterns permits the facile translation of the analog pressure signals into digital spikes, attaining accurate perception of various tactile characteristics. The tactile sensory neuron consisting of an RC circuit was composed of a SiO2-based conductive bridge memristor exhibiting leaky integrate-and-fire properties and a Pt nanoparticles (NPs)-based strain sensor with a gauge factor of ∼270. A dense layer of Pt NPs was also used as the bottom electrode for the memristive element, yielding the manifestation of a threshold switching mode with a switching voltage of only ∼350 mV and an exceptional switching ratio of 107. Our work provides valuable insights for developing low power neurons with tactile feedback for prosthetics and robotics applications.