Search Results (104)

Long-term EEG measurements (over eight hours) can allow a deeper understanding of everyday life brain functioning. However, most EEG systems can only be used for short periods in controlled settings, due to limitations in comfort, signal quality of EEG electrode sensors and amplifier size. In this study, we measured brain activity during day and night on ten lab member participants using our new flex-printed trEEGrid patch electrodes and a miniaturized and modularized amplifier prototype. The patch was left on participants up to 24 h while they went about their day. EEG was recorded during several exploratory tasks and overnight. Daytime scenarios included auditory and visual oddball tasks, as well as a resting-state measurement and sudoku cognitive load task. Impedances were recorded throughout all tasks. Ag/AgCl ‘ring’ electrodes were used as comparison. Daytime tasks were performed twice (i.e., once in the afternoon and again the next morning), to assess signal quality changes over time. P3 oddball, resting state and workload-related spectral effects were observed on both days. Auditory N1 amplitudes were similar between the patch and ring electrodes. The system shows promising data quality over the nearly 24 h wearing time and allows new possibilities for daytime and sleep EEG studies. Full article

Background/Objectives: Sensor-based movement assessment is increasingly used to quantify movement execution quality and support feedback-guided rehabilitation exercise, particularly in home-based and remote rehabilitation contexts. However, exercise adherence, movement execution quality, and rehabilitation progress remain difficult to monitor continuously and objectively outside direct therapist supervision. This scoping review aimed to map the current applications of sensor-based biofeedback and movement-quality assessment systems for rehabilitation exercise and to identify evidence gaps. Methods: This review followed established scoping review methodology and PRISMA-ScR guidance. PubMed/MEDLINE, Web of Science Core Collection, and IEEE Xplore were searched, and Google Scholar was used for supplementary searching. English-language studies published from January 2014 to May 2026 were eligible if they involved rehabilitation-related populations, sensor-based movement assessment, biofeedback, or training guidance. Data were charted and narratively synthesized according to rehabilitation context, sensor technology, movement-quality metrics, computational approaches, feedback strategies, real-time or remote functions, and reported outcomes. Results: Fifty-five studies published between 2015 and 2026 were included. The evidence covered neurological, musculoskeletal and orthopedic, balance and vestibular, fall-prevention, home-based, and telerehabilitation applications. Technologies included inertial sensors, smartphones, vision/depth cameras, surface electromyography, pressure/force sensors, and multisensor systems. Movement-quality metrics included range of motion, postural stability, gait characteristics, loading, muscle activation, movement correctness, repetition count, and task completion quality. Feedback was visual, auditory, vibrotactile, app-based, avatar-based, therapist-facing, or remote-platform-based. Most evidence came from feasibility, technical validation, algorithmic validation, and small-sample clinical studies. Conclusions: Sensor-based systems may help translate rehabilitation exercise performance into quantifiable and feedback-enabled information. Future research should strengthen real-world validation, standardize task-specific movement-quality metrics, and clarify how feedback mechanisms can support individualized rehabilitation progression. Full article

Electrocardiography (ECG)-only workload-state classification offers a lower-burden physiological sensing route than denser multimodal, multi-sensor physiological, or neuroimaging setups for controlled laparoscopic training research. This study evaluated whether ECG-only representations can classify condition-derived cognitive workload states during a controlled laparoscopic peg transfer task performed under Control and auditory N-back conditions (N0, N1, and N2). Twenty surgical trainees from an advanced surgical-skills course completed the task protocol, and 17 participants entered ECG modelling after ECG quality-control exclusions. The retained ECG modelling dataset comprised 268 task blocks (Control/N0/N1/N2: 68/68/68/64), evaluated as held-out task-block predictions in a known-participant four-fold leave-one-round-out (LOTO) evaluation. Branch-specific raw ECG windows, recurrence-plot sequences, and heart-rate/time-domain heart-rate-variability inputs are detailed in the Methods, and model metrics were computed after reduction to task-block predictions. The primary endpoint was Surgery Task Load Index (SURG-TLX)-aligned low/high workload, defined as Control plus N0 versus N1 plus N2. Four-class and three-level endpoints were retained as secondary views. Raw ECG, recurrence-plot (RP)-derived, hybrid score-level fusion, and conventional heart-rate/time-domain heart-rate-variability Random Forest (HRV-RF) models were compared using a locked evaluation protocol, leakage-aware train-fold-only preprocessing, participant-clustered confidence intervals, and planned paired tests for the primary endpoint. On the primary low/high endpoint, raw ECG achieved the highest macro-F1/balanced accuracy (0.865/0.865), followed by the hybrid branch (0.847/0.847) and HRV-RF (0.648/0.649). Raw ECG and hybrid were supported over RP-derived and HRV-RF under the planned paired tests. On selected secondary endpoints, hybrid achieved higher macro-F1 values than raw ECG, consistent with possible endpoint-dependent RP-derived complementarity rather than a general hybrid advantage. These findings support ECG-only block-level workload-state classification in this controlled training setting. The evidence is retrospective and based on held-out rounds from known participants rather than subject-independent or deployment validation. Full article

Virtual fencing is increasingly used in grazing systems as a flexible alternative to physical fencing, yet detailed assessments of cattle behavior within such systems remain limited. This study investigates the use of collar-mounted tri-axial accelerometers combined with supervised machine learning to characterize cattle behavior in a virtual fencing system. Seven free-ranging Angus cattle were monitored using accelerometers mounted on a virtual fencing system, GNSS positioning, and virtual fence warning logs. A random forest classifier was developed and trained to identify key behaviors (grazing/feeding, ruminating, lying, standing and locomotion) using features derived from tri-axial accelerometer data. The model achieved high classification performance for grazing/feeding, ruminating, and lying (mean accuracy = 0.87, range = 0.83–0.90), enabling estimation of individual behavioral time budgets. Daily activity patterns were generally stable over time and across individuals. Spatial analyses revealed significant differences in behavior between areas near the virtual fence boundary and interior pasture locations, with increased grazing and reduced ruminating near the boundary, potentially reflecting spatial variation in habitat type or forage availability. In the virtual fencing system, cattle are equipped with collars that emit an auditory warning when they approach a virtual boundary, followed by a low-energy electrical impulse when the warning is ignored over a directional distance of 5–10 m. Event-based analyses showed no consistent short-term changes in either movement intensity and direction nor locomotion following auditory warning events, indicating that cattle habituated to the system did not exhibit uniform behavioral disturbance in response to warnings. These results suggest that accelerometer-based behavior classification can provide fine-scale, non-invasive insights into spatio-temporal cattle behavior in virtual fencing systems. The finding indicates that, in a habituated herd, virtual fencing was not associated with pronounced disruption to the measured behavioral patterns, while highlighting the potential of embedded sensor data for animal-based behavioral monitoring. Full article

Recent advances in eye-tracking technologies have fostered growing interest in their integration with acoustic research for investigating auditory perception and human behavioral responses. This study presents a structured literature review of recent developments at the intersection of eye tracking and acoustics, with the aim of analyzing how eye-movement data can support the interpretation of auditory events, spatial listening behaviors, and multimodal human–environment interactions. The reviewed studies were organized into four main research areas focusing on the application of eye-tracking in acoustics: sound source localization and identification, sound event detection and classification, acoustic sensing and multimodal systems, and soundscape and perceptual acoustic studies. The analysis indicates that eye-movement patterns can provide useful indicators of auditory attention and perceptual processes, particularly when combined with complementary physiological, visual, and acoustic sensing modalities. Furthermore, recent methodological advances, including real-time processing, machine learning algorithms, and sensor fusion techniques, have contributed to improving the robustness and accuracy of multimodal data analysis. Nevertheless, the review also highlights several limitations in current research, such as the lack of standardized experimental protocols, inter-individual variability, and susceptibility to environmental noise and external interference. Finally, future research perspectives are discussed, emphasizing the development of standardized and adaptive multimodal frameworks for human behavior modeling and intelligent acoustic monitoring systems. Full article

This article presents a literature review aimed at outlining the state of the art in the assessment of underwater noise and in the evaluation of its effects on fish behavior and health. We examine current methodologies for characterizing the underwater soundscape, emphasizing the importance of incorporating particle motion sensors alongside pressure sensors due to the nature of fish auditory systems. Guidelines for simulating underwater acoustic environments in laboratory settings are also summarized. To characterize anthropogenic noise sources, we consider ship propellers as the primary source of continuous underwater noise, whereas we consider the equipment used in marine seismic surveys as the primary source of impulsive underwater noise. Finally, we summarize documented effects of acoustic pollution on a commercially important species, European seabass (Dicentrarchus labrax), and describe experimental setups suitable for observing these effects. Full article

Negative driving emotions constitute a significant factor compromising road safety. Current intelligent vehicle human machine interaction (HMI) systems predominantly focus on functional implementation, lacking the capability to perceive and adapt to the driver’s psychological state. To address this issue, this study investigates the intrinsic relationship between driving emotions and HMI through multimodal experiments. Experiment One reveals the distribution patterns of drivers’ visual attentional scope under different emotional states. Experiment Two establishes a color preference model for HMI interfaces corresponding to specific emotions. Experiment Three quantitatively analyzes the impact of emotional variations on the perceptual efficiency of auditory warnings. Based on the experimental data, an interaction design principle matching “Emotion-Scene-Modality” is formulated, guiding the design of a data-driven, emotion-adaptive HMI prototype system. This system can perceive the driver’s emotional state in real time via multimodal sensors and dynamically adjust interface color themes, information layout, warning sound effects, and voice interaction style according to predefined interaction strategies. Usability testing demonstrates that, compared to traditional static HMI, this affective adaptive system effectively mitigates the driver’s negative emotional load and provides alerts that are more perceptible and less likely to cause irritation during critical moments. Consequently, it offers a significant theoretical foundation and practical reference for constructing a safer and more comfortable next-generation intelligent vehicle cockpit interaction paradigm. Full article

Background: Balance impairment and falls are a major health concern in older adults. Beyond vestibular and visual factors, growing evidence indicates that age-related hearing loss contributes to postural instability through altered multisensory integration. However, interventions addressing the interaction between auditory input and postural control remain limited. This study examined whether integrating Taopatch^® nanotechnology, based on localized photobiomodulation, into conventional hearing aids could influence postural control in individuals with hearing loss. Methods: Forty experienced hearing aid users (mean age 77.3 ± 15.6 years) completed five postural assessments using a SensorMedica^® baropodometric platform. Four sessions employed a placebo patch identical in appearance to the active device, and the fifth used Taopatch^®. Static and stabilometric parameters were analyzed under open- and closed-eye conditions. Results: Significant improvements were observed with the Taopatch^®-integrated device. Sway path length (−8%, p = 0.002), mean velocity (−8%, p = 0.002), and low-frequency sway (−30%, p = 0.04) decreased, indicating smoother and more efficient postural control. A lateral redistribution of plantar load and an increase in contact surface area (up to +15%) were also found. These effects were less evident without visual input. Conclusions: Preliminary findings suggest that localized photobiomodulation integrated into hearing aids may positively influence postural stability in older adults with hearing impairment, possibly by supporting sensory integration processes. Further controlled studies are needed to confirm these effects and clarify the underlying mechanisms. Full article

Although street ambiance, understood as a multisensory and cognitive experience, has been widely discussed in theory, empirical evidence on how it is perceived across specific urban contexts remains limited. This study explores the differences in ambiance-related responses between the two contrasting streets—one historic and one non-historic—by integrating brain, body, and mind measures within a controlled immersive framework. A VR-based, multimodal experimental protocol was employed, presenting participants with 360° audiovisual representations of two real-world streets. Data were collected using EEG, a wrist-worn physiological sensor, and self-report evaluations. Subjective responses consistently differentiated between the two streets’ ambiances, reflecting a coherent, environmentally grounded appraisal shaped by each street’s combined visual and auditory attributes. Neural responses clearly differentiated between the two streets, demonstrating that the distinct experiential character of the historic street was also reflected at the level of brain activity. Within the historic street, an asymmetric relationship emerged in which subjective evaluations differentiated ambiance more robustly than corresponding psychophysiological modulation, indicating context-dependent sensitivity across modalities. Taken together, the findings suggest that subjective and psychophysiological responses do not differ merely in magnitude but also in their mode of organization, revealing street ambiance as a multisensory and relational experience rather than the sum of isolated environmental attributes or historicity alone. Full article

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications. Full article

New wearable technology opens new possibilities for low-cost, easily accessible home-based interventions as a supplement to typical clinical rehabilitation therapy. In this pilot study, we tested a new interactive adjustable Feedback training system on 14 infants at high risk of cerebral palsy between 2 and 12 months of age to facilitate increased movements. The system consists of four wireless motion sensors placed on the infant’s limbs. Inertial sensors track the infant’s movements which control auditory and visual stimuli that act as motivational feedback. A 15 min usage of the Feedback training system four days a week for approximately six months was aimed for. None of the participants reached the recommended amount of intervention, due to time limitations. Seven of the twelve participating infants (58%) achieved at least 50% of the recommended training amount. Parents found the Feedback training system easy to use with minimal need for technical assistance. Preliminary data suggest that infants engaged more actively during training sessions where their movements actively controlled the presentation of the stimuli. The Feedback training system is promising as a user-friendly add-on to the playful and interactive stimulation of motor and cognitive development in infants with neurodevelopmental disorders. Full article

The development of human-robot interfaces that support daily social interaction requires biomimetic innovation inspired by the sensory receptors of the five human senses (tactile, olfactory, gustatory, auditory, and visual) and employing soft materials to enable natural multimodal sensing. The receptors have a structure formulated by variegated shapes; therefore, the morphological mimicry of the structure is critical. We proposed a spring-like structure which morphologically mimics the roll-type structure of the Meissner corpuscle, whose haptic performance in various dynamic motions has been demonstrated in another study. This study demonstrated the gustatory performance by using the roll-type Meissner corpuscle. The gustatory iontronic mechanism was analyzed using electrochemical impedance spectroscopy with an inductance-capacitance-resistance meter to determine the equivalent electric circuit and current-voltage characteristics with a potentiostat, in relation to the hydrogen concentration (pH) and the oxidation-reduction potential. In addition, thermo-sensitivity and tactile responses to shearing and contact were evaluated, since gustation on the tongue operates under thermal and concave-convex body conditions. Based on the established properties, the roll-type Meissner corpuscle sensor enables the iontronic behavior to provide versatile multimodal sensitivity among the five senses. The different condition of the application of the electric field in the production of two-types of A and B Meissner corpuscle sensors induces distinctive features, which include tactility for the dynamic motions (for type A) or gustation (for type B). Full article

Highly sensitive bioinspired cutaneous receptors are essential for realistic human-robot interaction. This study presents a biomimetic tactile sensor morphologically modeled after the Meissner corpuscle, designed for high dynamic sensitivity achieved using a coiled configuration. Our proposed electrolytic polymerization technique with magnet-responsive hybrid fluid (HF) was employed to fabricate soft, elastic rubber sensors with embedded coiled electrodes. The coiled configuration, optimized by electrolytic polymerization, exhibited high responsiveness to dynamic motions including pressing, pinching, twisting, bending, and shearing. The mechanism of the haptic property was analyzed by electrochemical impedance spectroscopy (EIS), revealing that reactance variations define an equivalent electric circuit (EEC) whose resistance (R_p), capacitance (C_p), and inductance (L_p) change with applied force; these changes correspond to mechanical deformation and the resulting variation in the sensor’s built-in voltage. The roll-type Meissner-inspired sensor demonstrated fast-adapting behavior and broadband vibratory sensitivity, indicating its potential for high-performance tactile and auditory sensing. These findings confirm the feasibility of electrolytically polymerized hybrid fluid rubber as a platform for next-generation bioinspired haptic interfaces. Full article

Auditory cueing has become an increasingly practical tool in gait rehabilitation; however, the specific sound features that modulate gait performance remain unclear. This study investigated how tempo and auditory continuity, two fundamental acoustic features, influence spatiotemporal gait parameters in healthy adults. Thirty-five participants walked under six auditory conditions combining discrete, continuous, and hybrid feedback at slow (60 BPM) and fast (120 BPM) tempi, with gait metrics captured via a pressure-sensor walkway and subjective responses gathered through questionnaires. Compared with the silent baseline, auditory cueing significantly affected cadence [F(1.88, 63.75) = 8.95, p < 0.001, η_p² = 0.21]; velocity [F(1.69, 57.49) = 10.15, p < 0.001, η_p² = 0.23]; and stride length [F(1.74, 59.26) = 6.87, p = 0.003, η_p² = 0.17]. Slower tempi reduced gait parameters, while the combined continuous and discrete conditions produced the greatest modulation. Participants reported that they had attempted to synchronize their steps with the auditory cues, which may have led to small adjustments in their natural walking speed and stride patterns, especially during the slower tempo. This suggests that rhythmic structure and sound continuity affect both perceptual and motor processes. Overall, sound continuity exerted a stronger influence on gait than tempo alone. These findings advance understanding of sensorimotor synchronization and highlight the potential of designing tailored auditory feedback systems to enhance movement awareness and inform clinical gait-rehabilitation strategies. Full article

Visual impairment (VI) affects over two billion people globally, with prevalence increasing due to preventable conditions. To address mobility and navigation challenges, this study presents a multi-platform, multi-sensor Electronic Travel Aid (ETA) integrating a combination of ultrasonic, LiDAR, and vision-based sensing across head-, torso-, and cane-mounted nodes. Grounded in orientation and mobility (OM) principles, the system delivers context-aware haptic and auditory feedback to enhance perception and independence for users with VI. The ETA employs a hardware–software co-design approach guided by proxemic theory, comprising three autonomous components—Glasses, Belt, and Cane nodes—each optimized for a distinct spatial zone while maintaining overlap for redundancy. Embedded ESP32 microcontrollers enable low-latency sensor fusion providing real-time multi-modal user feedback. Static and dynamic experiments using a custom-built motion rig evaluated detection accuracy and feedback latency under repeatable laboratory conditions. Results demonstrate millimetre-level accuracy and sub-30 ms proximity-to-feedback latency across all nodes. The Cane node’s dual LiDAR achieved a coefficient of variation at most 0.04%, while the Belt and Glasses nodes maintained mean detection errors below 1%. The validated tri-modal ETA architecture establishes a scalable, resilient framework for safe, real-time navigation—advancing sensory augmentation for individuals with VI. Full article