Search Results (566)

Background: Sensorineural hearing loss represents a significant global health burden affecting over 1.5 billion individuals worldwide. Modern hearing aids, equipped with digital signal processing and smart connectivity features, constitute a cornerstone of neuro-sensory rehabilitation. However, the psychosocial impact of these assistive smart technologies on patient self-esteem remains incompletely characterized. Methods: A cross-sectional multivariate study was conducted with 245 participants, divided into three groups: normal-hearing controls (NH, n = 73), hearing-impaired patients using smart hearing aid technology (HA users, n = 86), and hearing-impaired patients not using hearing aid technology (HA non-users, n = 86). Self-esteem was measured using the Rosenberg Self-Esteem Scale (SES). Hearing disability and tinnitus severity were assessed with the Hearing Handicap Inventory for Adults (HHIA) and Tinnitus Handicap Inventory (THI), respectively. Data analysis included one-way ANOVA, Tukey’s HSD post hoc tests, Pearson correlations, and multivariate regression. Results: Hearing aid users showed significantly higher SES scores (35.41 ± 5.32) compared to non-users (22.99 ± 4.53; p < 0.001, Cohen’s d = 2.515). One-way ANOVA indicated highly significant differences among groups (F = 299.00, p < 0.001, η² = 0.712). SES was negatively correlated with HHIA (r = −0.573, p < 0.001) and THI (r = −0.443, p < 0.001), while HHIA and THI were strongly positively correlated (r = 0.729, p < 0.001). In multivariate analysis, HA use remained a strong independent predictor of self-esteem (β ≈ 11.9, p < 0.001), even after adjustment for age, sex, HHIA, and THI. Perceived hearing handicap was independently associated with lower self-esteem, whereas tinnitus severity was not a significant predictor in the fully adjusted model. The model explained approximately 65% of the variance in self-esteem scores. Conclusions: Smart hearing-aid use is strongly and independently associated with higher self-esteem in patients with sensorineural hearing loss. These results support the inclusion of modern audiological rehabilitation devices in comprehensive management strategies for long-term conditions and highlight psychosocial benefits that extend beyond hearing restoration. Full article

Neuromorphic technologies are attracting increasing interest in neuroengineering, as they provide an event-driven, spike-based computational framework that is well suited to temporally structured, sparse, and resource-constrained biological systems. Compared with conventional computing pipelines, neuromorphic approaches enable tighter integration of sensing, encoding, inference, feedback, and actuation under low-power and low-latency conditions. These features make them particularly relevant for wearable, implantable, and other edge-native neuroengineering applications. This review examines neuromorphic neuroengineering from four closely related perspectives: neuromorphic neurostimulation and adaptive actuation; tactile and sensory biointerfaces; spiking neural network (SNN)-based biosignal processing and state decoding; and wearable or implantable neuromorphic platforms. Across these domains, we highlight how neuromorphic systems may facilitate edge-native, closed-loop architectures that operate closer to the body and respond selectively to meaningful state changes. Neurorehabilitation is further discussed as an important translational context, as it involves long-term use, multimodal sensing, adaptive intervention, and substantial real-world deployment constraints. At present, however, the evidence base remains fragmented and is still largely dominated by device demonstrations and proof-of-concept studies rather than robust translational validation. Overall, neuromorphic approaches offer a promising systems-level pathway toward neuroengineering platforms that are not only computationally efficient but also adaptive, deployable, and responsive in real-world settings. Full article

Background/Objectives: Percutaneous cryoneurolysis (CNL) has emerged as a minimally invasive neuromodulatory technique for focal spasticity management, with growing international clinical adoption since 2018. Its application to upper limb motor nerve targets—including branches of the musculocutaneous, radial, median, ulnar, pectoral, and thoracodorsal nerves—is of direct relevance to clinicians involved in the surgical and non-surgical management of hand and upper extremity spasticity. The existing literature lacks a comprehensive systematic appraisal of its evidence base. This systematic scoping review aimed to map all published evidence on CNL for spasticity across all aetiological groups and anatomical regions, with particular attention to upper limb and hand-relevant targets; appraise methodological quality using design-appropriate tools; characterise the safety profile; apply the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework to key outcome domains; and identify critical evidence gaps. Methods: A systematic scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines (search through February 2026). PubMed/MEDLINE, Embase (via Ovid), Scopus, and the Cochrane Library were searched. Methodological quality was assessed using JBI Critical Appraisal Checklists, Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I), and A MeaSurement Tool to Assess systematic Reviews-2 (AMSTAR-2). Certainty of evidence was evaluated using GRADE. Results: Twenty-five studies met inclusion criteria; no randomised controlled trials (RCTs) were identified. In the largest prospective observational cohort (n = 59, 12-month follow-up), CNL produced statistically significant improvements in passive range of motion (ROM), Modified Ashworth Scale (MAS) scores, and pain in patients with upper limb spasticity refractory to botulinum toxin type A (BoNT-A). A prospective safety study (n = 113 patients; 277 nerves) documented that 96.75% of nerve treatments produced no post-procedural sensory disturbance; the risk was approximately 10-fold higher for mixed sensorimotor than purely motor nerve targets (7.1% vs. 1.1%). Certainty of evidence was Very Low (⊕◯◯◯) for all efficacy outcomes and Low (⊕⊕◯◯) for safety. Conclusions: CNL represents a mechanistically sound second-line or complementary intervention for refractory focal spasticity. In the upper extremity context, it may additionally serve as a reversible functional evaluation tool before irreversible surgical decisions—including selective neurotomy—are made. The evidence base is critically constrained by the absence of RCTs, confirmed cohort overlap between the two largest primary studies, financial conflicts of interest with the primary device manufacturer identified in ≥48% of included studies (≥12/25), and single-institution concentration of primary evidence (≥69% of primary clinical studies from one research group). Multiple ongoing controlled trials are expected to provide higher-quality evidence to inform guideline development. Full article

The von Neumann architecture faces a “memory wall” problem due to the physical separation of memory and processor, posing major challenges to energy efficiency and latency in the era of artificial intelligence. To overcome these bottlenecks, artificial synaptic devices inspired by biological systems have emerged as an important research direction. By integrating sensing and computing functions at the device level, these architectures provide a promising approach for the efficient processing of natural physical signals. Supported by advances in functional materials and artificial neural network (ANN) algorithms, artificial synaptic devices are capable of perceiving and processing various external stimuli, showing strong potential for applications in intelligent electronic skins, robotics, and edge computing. This review provides a comprehensive overview of recent advances in artificial synaptic devices, with particular emphasis on tactile and visual sensing applications. We discuss representative device types and operating mechanisms, analyze critical challenges from the perspectives of material engineering and functional integration, and further summarize potential solutions and future trends toward multimodal sensory–memory–computing systems. Full article

Effective post-harvest management of avocados is essential for reducing supply chain losses. This requires an accessible, cost-effective method for accurately predicting ripeness under real-world conditions. This study developed a non-destructive framework for predicting avocado ripeness using portable visible–near-infrared (Vis-NIR) spectrometers and analyzed the storage temperature dependencies. A 10-point sensory-based ripeness index was correlated with second-derivative reflectance spectra using partial least squares (PLS) regression. To ensure model robustness, we employed repeated 10-fold cross-validation. The broadband PLS model achieved a residual predictive deviation (RPD) of 1.36, while a simplified model using six specific wavelengths (570, 977, 1120, 1161, 1398, and 1655 nm) demonstrated an RPD of 1.43, confirming its feasibility as a preliminary screening tool. Key wavelengths identified were associated with chlorophyll degradation and lipid accumulation. Furthermore, a significant logarithmic relationship (r = 0.9965) was observed between storage temperature (15–35 °C) and the daily ripening rate. Our results suggest that ripening progression is significantly suppressed at temperatures of approximately 12 °C or below. These findings provide quantitative guidelines for distributors to optimize logistics and shelf-life management using portable technology, contributing to the digitalization of consumer-aligned ripeness assessment. Full article

The SensaSea System is a responsive multisensory environment, specifically, a room-sized interactive installation that incorporates wearable devices, interactive visual floor projections and auditory and tactile modalities. SensaSea is designed as a physical environment for embodied interaction and free play suitable for multiple players; the system uses social proximity as the primary mechanism. Our objective is to promote active peer interaction and social connectedness among elementary school children through sensory-guided approaches which include digitized and projected interactive sea creatures. The multi-modal system also features an interactive soundscape and innovative real-time haptic feedback. We conducted eight group user studies (24 children in total). Our usability and feasibility tests demonstrated that the system results in positive emotions and elicits multiple pro-social behaviors. Full article

We investigated the effects of manipulating visual information on motor control indicators during the Waiter’s Bow Test. The results suggested that visual information occlusion reduced the maximum flexion angles of the lumbar spine and upper lumbar region. Furthermore, subjects who tested negative under the open-eye condition tested positive under the closed-eye condition. Regarding muscle activity in the rectus abdominis and erector spinae muscles, it was suggested that this activity was not affected by visual information. These findings indicate that visual sensory feedback is one factor influencing lumbar motor control. The integration of electromyography and accelerometer systems in this study highlights the role of wearable sensor technologies in quantifying neuromuscular function in Bioengineering. By restricting visual information, a model for sensory reweighting can be established for the design of biofeedback systems, rehabilitation robotics, and assistive devices. The results of this study demonstrate how sensor-based evaluation and sensory manipulation can inform the engineering of diagnostic and therapeutic technologies for motor control assessment. Full article

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order $\left(\nu \right)$ and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication. Full article

Background: This paper presents a systematic review and meta-analysis to identify the effects of Rhythmic Auditory Stimulation (RAS) delivered via wearable devices on the gait and balance in patients with Parkinson’s disease. Method: The PICO criteria were established according to the PRISMA 2020 guidelines, and literature searches were performed across five databases covering studies published between 2015 and 2025: PubMed, Embase, Cochrane, Scopus, and Web of Science. After applying the inclusion criteria, eleven randomized controlled trials (RCTs) were selected. The quality of the studies was evaluated using the PEDro Scale and ROB-2. Statistical analyses were performed using Review Manager 5.4 based on the number of samples, means, and standard deviations to calculate the effect sizes. Result: The analysis results showed that wearable RAS significantly improved the gait speed (SMD = 0.49, p < 0.05) and balance ability (SMD = 0.40, p < 0.05), while no significant differences in the gait pattern, FOG-Q, or UPDRS-III were observed. The heterogeneity among studies was low, and the funnel plots were distributed symmetrically, indicating minimal publication bias. The average PEDro score was 7.33, suggesting moderate-to-high methodological quality. Conclusion: wearable RAS was identified as an evidence-based intervention effective in improving the gait speed and balance in patients with Parkinson’s disease. Full article

The brain’s primary sensory processing areas often present a topographical organization and are distributed following hierarchical architecture, permitting the integration of the information in higher levels of its hierarchy: a process referred to as multisensory integration. A system with such characteristics naturally computes in a parallel and distributed manner and is based in associations between the different symbols built from our perceptions of the environment. In this work, we take inspiration from the sensory processing areas of the brain and propose proof-of-concept of a multi-layered neuromorphic system with parallel and distributed computing capabilities by means of simulation. The proposed neuromorphic architecture is constituted by identical self-organizing modules which are trained with on-line unsupervised-friendly learning rules, such as the spike-timing-dependent plasticity (STDP). These self-organizing modules are constituted by oxide-based resistive random access memory (OxRAM) devices, which play the analog synaptic role. The different modules display a topographical organization according to the input dataset features they have been trained with and are organized following a hierarchical system. The system exhibits conceptual associative behavior between inputs with clustering capabilities, able to classify inputs which have never been seen before by the system, according to their similarity with the ones it has been trained with. Full article

Background: Hearing impairment affects a significant portion of the global population. The development of assistive technologies, particularly wearable devices, has been pivotal in mitigating these challenges. Methods: We present a systematic literature review on wearable assistive technologies for individuals with hearing impairment, analyzing 106 scientific articles identified from diverse sources (IEEE Xplore, ACM Digital Library, and Web of Science). Our comprehensive analysis is structured around device types, body locations, user study methodologies, sensory modalities, and application domains. Results: Findings reveal a strong emphasis on auditory and visual feedback, a mix of traditional hearing aids complemented by smart wearable devices, and experimental evaluations focusing on speech comprehension and usability. Visual analysis highlights a significant anatomical shift towards body-worn and wrist-worn haptic devices. While speech accuracy is rigorously reported, user-centric metrics like comfort and battery life are frequently neglected. Conclusions: Addressing these disparities, we propose the HEAR framework (Hybrid Architectures, Engaging Experiences, Adaptive Systems, Real-world Validation). This strategic roadmap advocates for a diversification of sensory outputs, more extensive longitudinal user studies, and the development of adaptive, multi-modal solutions that seamlessly integrate into users’ everyday lives. Full article

Background/Objectives: With increasing survival rates following oncologic mastectomies, loss of breast sensation can negatively impact a patient’s quality of life. Methods: PubMed, Embase, and Web of Science were searched in April 2025 for studies reporting sensory outcomes after neurotized breast reconstruction. Eligible studies included patients undergoing autologous or implant-based reconstruction with any neurotization technique. Forty studies were included, and outcomes involved objective sensory testing (e.g., Semmes-Weinstein monofilaments, pressure-specified sensory devices, and thermal thresholds) and patient-reported quality of life (e.g., BREAST-Q). Results: Neurotization consistently accelerated and improved recovery of tactile, thermal, and protective sensation compared with non-neurotized controls, particularly in DIEP and TRAM flaps. Direct coaptation was most frequently employed, while nerve allografts, conduits, and autologous grafts offered effective alternatives when direct repair was not feasible. Implant-based reconstructions using allografts also demonstrated significant improvements in the nipple–areola complex and breast skin sensation. Across studies, earlier and more uniform sensory return was reported, with improved sensation often associated with high patient satisfaction and quality of life. Conclusions: The preponderance of observational evidence suggests that nerve coaptation, whether by direct suture, conduit, allograft, or autograft, represents a promising adjunct to breast reconstruction in both autologous and implant-based reconstruction. However, many studies were retrospective in design, had small sample sizes, and lacked randomization. Full article

Background/Objectives: Dental anxiety represents a significant barrier to oral care in children with neurodevelopmental disorders (NDDs), whose sensory sensitivities and behavioral challenges often complicate clinical management and limit access to treatment. Virtual reality (VR) has emerged as a supportive tool to improve the feasibility of dental procedures in this vulnerable population. This study aims to evaluate whether a VR-based distraction approach could facilitate the completion of dental treatment in children with mild intellectual disability (ID). Methods: A prospective comparative observational study was conducted between February and September 2025 involving 56 children aged 11–15 years with mild ID and moderate dental anxiety (Corah Dental Anxiety Scale, DAS: 9–12). Participants were allocated to two groups of distraction approaches—VR distraction (n = 28) using the Oculus Quest 3^® headset or a monitor-based cartoon (n = 28)—according to device availability and to maintain balanced group sizes. The primary outcome was treatment success, defined as completion of the restorative dental procedure under local anesthesia within 50 min. Results: Treatment success was achieved in 78.6% of the VR group versus 46.4% of the monitor group (p = 0.026). The odds of successful treatment were more than four times higher with VR compared to monitor distraction (OR 4.12; 95% CI: 1.16–16.47), with a risk ratio of 2.50 (95% CI: 1.14–5.50). Stratified analysis suggested a stronger effect in females (OR 12.25; 95% CI: 1.27–118.36) than in males (OR 2.56; 95% CI: 0.53–12.43). Conclusions: VR-based distraction significantly improved dental treatment success in children with mild ID compared with conventional distraction. Although gender differences were observed, they should be interpreted with caution due to the small sample size. This work lays the foundation for developing both short- and long-term protocols to facilitate dental treatment management and cooperation in patients with NDDs. Full article

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

Background: Objective assessment of postural control is central to the clinical evaluation of vestibular disorders. Although force-platform-based posturography is considered the gold standard, its use may be limited by cost and infrastructural requirements. Wearable inertial measurement units (IMUs) represent a promising alternative; however, their clinical validation should account for intrinsic differences in measurement paradigms rather than strict metric equivalence. Objective: To preliminarily evaluate the within-session reliability of a wearable IMU-based medical device for balance assessment (Gravity), and its agreement with established static (SBP) and computerised dynamic posturographic systems (CDP) in healthy subjects. Methods: Sixty-three healthy adults were enrolled in two independent method comparison studies: a wearable IMU-based balance system versus a static stabilometric platform (GRAVITY vs. SVEP; n = 42) and a wearable IMU-based balance system versus computerised dynamic posturography (Gravity vs. EquiTest; n = 21). Gravity measurements were obtained simultaneously with reference systems across standardised sensory conditions. Within-session reliability and method agreement were assessed. Results: Within-session reliability of Gravity was outcome-dependent. Length-based components demonstrated higher repeatability (ICC (single) = 0.25–0.35; ICC (average) = 0.41–0.52), with narrower limits of agreement (LoA = ±9–12%) and lower measurement error (SEM = 3.3–4.3%). In comparison with SBP, length-based measures exhibited narrower limits (LoA = ±12–17) and more consistent relationships. Comparison with CDP revealed moderate agreement for composite and preferential scores (LoA: −2.20–7.07; −5.54–8.12). Conclusions: Gravity sensor may represent a clinically meaningful, outcome-dependent performance, with superior reliability and comparability for length-based postural measures compared with area-based measures. The device could provide balance assessments compatible with both static and dynamic posturographic systems, accounting for physiological variability. These findings support the potential clinical use of wearable IMU-based posturography, particularly in settings where conventional force-platform systems are not readily available, and warrant further validation in larger, more clinically diverse populations. Full article