Search Results (29)

High noise levels in industrial workplaces pose significant challenges to occupational safety, particularly with hearing protection and effective communication. Traditional hearing protection devices, while effectively attenuating harmful noise, often compromise situational awareness by excessively isolating workers from the acoustic environment and preventing the perception of critical auditory cues (e.g., emergency alarms), thereby introducing additional safety risks. This paper presents a smart industrial safety system that integrates Internet of Things (IoT) and artificial intelligence (AI) and is based on intelligent hearing protection devices to (a) selectively attenuate hazardous industrial noise while (b) preserving human speech and (c) reproduce targeted audio notifications to workers near malfunctioning or hazardous machinery. A real-time voice activity detection (VAD) model is employed to distinguish vocal components from background noise to adaptively control digital signal processing filters. Furthermore, indoor localization enables the delivery of targeted audio messages to workers in proximity to relevant events. Experimental evaluations on embedded hardware demonstrate that the selected VAD model operates well within real-time constraints and effectively supports dynamic noise filtering. Objective evaluation of the filtering stage using Mean Opinion Score (MOS), signal-to-noise ratio (SNR), and Harmonics-to-Noise Ratio (HNR) shows consistent quality improvements across all tested conditions, with MOS gains up to +118%, SNR increases between +10.4 and +29.0 dB, and HNR improvements up to +6.22 dB, indicating enhanced speech intelligibility and preservation of voice harmonic structure even under high-noise scenarios. Robustness validation of the VAD module across varying acoustic conditions confirms reliable speech detection performance, achieving perfect classification at +10 dB SNR, very high accuracy at 0 dB (98.3%, ROC AUC 0.998), and stable operation even at $-7$ dB SNR (79.8% accuracy, ROC AUC 0.878). The proposed architecture achieves a balanced trade-off between hearing protection and speech intelligibility while enhancing the effectiveness of safety communications in noisy industrial environments. Full article

Modern geo-information platforms commonly adopt multi-window map interfaces that integrate heterogeneous data, such as dynamic maps and live camera feeds. These interfaces impose high cognitive load and slow spatial event detection. Operators must rapidly locate the source of visual alarms, a task often leading to delays under high visual workload. To address this challenge, this study investigated whether spatialized auditory cues can improve alarm localization in such complex monitoring interfaces. A controlled experiment with 24 participants used a within-subjects design to test factors of auditory spatial cueing (none, binaural, monaural), display dynamics (dynamic, static), and interface complexity (4, 8, 12 panes). Behavioral and eye-tracking data measured detection accuracy, efficiency, and gaze patterns. Results showed that dynamic displays and high interface complexity impaired performance, indicating increased cognitive load. In contrast, monaural lateralized auditory alarms substantially improved detection efficiency and mitigated visual overload. Interaction analyses revealed that binaural cues reduced the performance costs of dynamic displays, whereas monaural cues compensated for high-density layouts. These findings demonstrate that spatialized auditory alarms effectively support spatiotemporal situational awareness and improve operator performance in high-load geo-surveillance systems. The study offers empirical and practical implications for designing cognitively ergonomic, multimodal interfaces that move beyond purely visual alarm designs. Full article

Background: Auditory alarms in patient monitoring are vital for clinical safety, but their harsh acoustic properties and high frequency contribute to stress, alarm fatigue, and reduced acceptance among healthcare staff. In collaboration with Sen Sound, Philips redesigned its alarm sounds to reduce auditory harshness, particularly for low- and medium-priority alarms, while preserving the salience of high-priority alerts. This study evaluated the impact of these refined alarm sounds in a real-world clinical setting. Objective: The goal was to determine whether anesthesia professionals perceive the refined Philips alarm sounds as more pleasant, clinically appropriate, and reliably recognizable compared with the traditional sounds. Methods: We conducted a single-center, pre–post intervention study at the University Hospital Zurich, Switzerland. Anesthesia providers assessed traditional and refined Philips alarm sounds with respect to perceived sound appeal, perceived functionality, and recognition accuracy. The primary outcome (sound appeal) was tested for superiority; using mixed-effects regression models. Results: Seventy-seven participants completed both study phases. Refined alarm sounds significantly improved perceived sound appeal (mean difference +0.51; 95% CI, 0.37–0.64; p < 0.001), while perceived functionality showed a small decrease (mean difference −0.15; 95% CI, −0.27 to −0.03). Recognition accuracy for low- and medium-priority alarms was higher with traditional sounds (low: 95.2% vs. 87.5%, p = 0.002; medium: 81.1% vs. 62.0%, p < 0.001), while high-priority alarms were more accurately identified with refined sounds (89.0% vs. 81.4%, p = 0.002). Overall, 71% of participants preferred the refined sounds, and 92% supported further development. Conclusions: Refined alarm sounds reduced perceived harshness and improved auditory comfort for anesthesia providers, but were associated with slightly lower perceived functionality and mixed recognition accuracy. High-priority alarms were identified more reliably, whereas low- and medium-priority alarms were less distinctly recognized, indicating a limited trade-off between sound appeal and clarity that primarily affected lower-priority signals. These findings suggest that while refinement can enhance the auditory environment, further development, potentially incorporating auditory icons or voice-based alerts, will be needed to optimize both user experience and patient safety in clinical practice. Full article

Landmines remain a significant hazard in contemporary warfare and post-conflict areas, jeopardizing the safety of both civilians and military personnel. This work suggests “Defence Pal,” a cost-effective and portable robotic prototype for landmine detection and environmental monitoring. Its primary objective is to minimize human risk while improving detection speed and accuracy. The system consists of a wireless-controlled vehicle equipped with a metal detector, gas sensors, and obstacle avoidance features, enabling real-time terrain surveillance while ensuring operator safety. Built with components including a Flysky FS-i6 transmitter and receiver, the prototype was tested under hazardous conditions. It demonstrated reliable detection of buried metallic objects and dangerous gases such as methane and carbon dioxide. The autonomous response system halts the robot and activates visual and auditory alarms upon detecting threats. Our experiments achieved average detection accuracies of 83% for metallic objects and 87% for hazardous gases, validating their performance. These results highlight the practicality and effectiveness of Defence Pal compared to conventional manual detection methods. The results confirm that Defence Pal is a practical, scalable, and cost-effective alternative to traditional manual detection methods for improving landmine identification and environmental hazard monitoring. Therefore, the novelty of this work lies in a low-cost dual-sensing prototype that enables simultaneous detection of gas and metal, providing a practical alternative to conventional single-target, high-cost systems. Full article

This paper presents the design, development, and evaluation of an embedded hardware and digital signal processing (DSP)-based real-time gesture-controlled system. The system architecture utilizes an MPU6050 inertial measurement unit (IMU), Arduino Uno microcontroller, and Python-based audio interface to recognize and classify directional hand gestures and transform them into auditory commands. Wrist tilts, i.e., left, right, forward, and backward, are recognized using a hybrid algorithm that uses thresholding, moving average filtering, and low-pass smoothing to remove sensor noise and transient errors. Hardware setup utilizes I²C-based sensor acquisition, onboard preprocessing on Arduino, and serial communication with a host computer running a Python script to trigger audio playing using the playsound library. Four gestures are programmed for basic needs: Hydration Request, Meal Support, Restroom Support, and Emergency Alarm. Experimental evaluation, conducted over more than 50 iterations per gesture in a controlled laboratory setup, resulted in a mean recognition rate of 92%, with system latency of 120–150 milliseconds. The approach has little calibration costs, is low-cost, and offers low-latency performance comparable to more advanced camera-based or machine learning-based methods, and is therefore suitable for portable assistive devices. Full article

Unrecognized mechanical faults and emergency sounds in vehicles can compromise safety, particularly for individuals with hearing impairments and in sound-insulated or autonomous driving environments. As intelligent transportation systems (ITSs) evolve, there is a growing need for inclusive, non-intrusive, and real-time diagnostic solutions that enhance situational awareness and accessibility. This study introduces an interpretable, sound-based machine learning framework to detect vehicle faults and emergency sound events using acoustic signals as a scalable diagnostic source. Three purpose-built datasets were developed: one for vehicular fault detection, another for emergency and environmental sounds, and a third integrating both to reflect real-world ITS acoustic scenarios. Audio data were preprocessed through normalization, resampling, and segmentation and transformed into numerical vectors using Mel-Frequency Cepstral Coefficients (MFCCs), Mel spectrograms, and Chroma features. To ensure performance and interpretability, feature selection was conducted using SHAP (explainability), Boruta (relevance), and ANOVA (statistical significance). A two-phase experimental workflow was implemented: Phase 1 evaluated 15 classical models, identifying ensemble classifiers and multi-layer perceptrons (MLPs) as top performers; Phase 2 applied advanced feature selection to refine model accuracy and transparency. Ensemble models such as Extra Trees, LightGBM, and XGBoost achieved over 91% accuracy and AUC scores exceeding 0.99. SHAP provided model transparency without performance loss, while ANOVA achieved high accuracy with fewer features. The proposed framework enhances accessibility by translating auditory alarms into visual/haptic alerts for hearing-impaired drivers and can be integrated into smart city ITS platforms via roadside monitoring systems. Full article

The impact of cycling-related traffic crashes on public health has increased significantly in recent decades, with cyclists being among the most vulnerable road users. The risk of severe injury in traffic crashes is notably high for cyclists, especially when distracted. Research indicates that distraction while cycling significantly increases the crash risk. This study investigates cycling distraction through a field operational test involving 100 participants. Riders followed a predetermined course while being exposed to external visual and auditory stimuli, including alarms, advertising signs, and car horns. Distraction levels were measured using eye-tracking technology. Data were analyzed by means of descriptive statistics, cluster, and correlation analyses. Our findings showed that auditory stimuli distract a higher percentage of cyclists, while audiovisual stimuli from road-related factors cause longer-lasting distractions. Additionally, five distraction clusters were identified based on stimulus duration. Lastly, it was found that males were more likely to belong to high-distraction clusters, whereas females and daily cyclists were more likely to fall into the lowest-distraction group. Full article

Firefighters experience high noise levels from various sources, such as sirens, alarms, pumps, and emergency vehicles. Unlike industrial workers who experience continuous noise exposure, firefighters are subject to intermittent high-intensity noise, increasing their risk of noise-induced hearing loss (NIHL). Despite global concerns regarding firefighters’ auditory health, research on Korean firefighters remains limited. This study aimed to assess personal noise exposure among Korean firefighters across three primary job roles—fire suppression, rescue, and emergency medical services (EMS)—and to predict worst-case noise exposure scenarios. This study included 115 firefighters from three fire stations (one urban, two suburban). We measured personal noise exposure using dosimeters attached near the ear following the Korean Ministry of Employment and Labor (MOEL) and International Organization for Standardization (ISO) criteria. Measurements included threshold levels of 80 dBA, exchange rates of 5 dB (MOEL) and 3 dB (ISO), and a peak noise criterion of 140 dBC. We categorized firefighters’ activities into routine tasks (shift handovers, equipment checks, training) and emergency responses (fire suppression, rescues, EMS calls). We performed statistical analyses to compare noise levels across job roles, vehicle types, and specific tasks. The worst-case exposure scenarios were estimated using 10th percentile recorded noise levels. The average 8 h time-weighted noise exposure levels varied significantly by job role. Rescue personnel exhibited the highest mean noise exposure (MOEL: 71.4 dBA, ISO: 81.2 dBA; p < 0.05), whereas fire suppression (MOEL: 66.5 dBA, ISO: 74.2 dBA) and EMS personnel (MOEL: 68.6 dBA, ISO: 73.0 dBA) showed no significant difference. Peak noise levels exceeding 140 dBC were most frequently observed in rescue operations (33.3%), followed by fire suppression (30.2%) and EMS (27.2%). Among vehicles, noise exposure was the highest for rescue truck occupants. Additionally, EMS personnel inside ambulances had significantly higher noise levels than drivers (p < 0.05). Certain tasks, including shift handovers, equipment checks, and firefighter training, recorded noise levels exceeding 100 dBA. Worst-case scenario predictions indicated that some work conditions could lead to 8 h average exposures surpassing MOEL (91.4 dBA) and ISO (98.7 dBA) limits. In this study, Korean firefighters exhibited relatively low average noise levels. However, when analyzing specific tasks, exposure was sufficiently high enough to cause hearing loss. Despite NIHL risks, firefighters rarely used hearing protection, particularly during routine tasks. This emphasizes the urgent need for hearing conservation programs, including mandatory hearing protection during high-noise activities, noise exposure education, and the adoption of communication-friendly protective devices. Future research should explore long-term auditory health outcomes and assess the effectiveness of noise control measures. Full article

The risk of serious crashes is notably higher among young and novice drivers. This increased risk is due to several factors, including a lack of recognition of dangerous situations, an overestimation of driving skills, and vulnerability to peer pressure. Recently, advanced driver assistance systems (ADAS) have been integrated into vehicles to help mitigate crashes linked to these factors. While numerous studies have examined ADAS broadly, few have specifically investigated its effects on young and novice drivers. This study aimed to address that gap by exploring ADAS’s impact on these drivers. Most studies in this review conclude that ADAS is beneficial for young and novice drivers, though some research suggests its impact may be limited or even negligible. Tailoring ADAS to address the unique needs of young drivers could enhance both the system’s acceptance and reliability. The review also found that unimodal warnings (e.g., auditory or visual) are as effective as multimodal warnings. Of the different types of warnings, auditory and visual signals proved the most effective. Additionally, ADAS can influence young drivers’ car-following behavior; for instance, drivers may maintain greater safety buffers or drive closely to avoid alarm triggers, likely due to perceived system unreliability. Aggressive drivers tend to benefit most from active ADAS, which actively intervenes to assist the driver. Future research could explore the combined effects of multiple ADAS functions within a single vehicle on young and novice drivers to better understand how these systems interact and impact driver behavior. Full article

Alarm sounds significantly influence a user’s sensory perception while driving, directly affecting driving judgement and safety. Personal experience and the environment play an important role in information cognition, but they are rarely considered in the current warning design. We propose a methodology enabling engineers and designers to locally optimize the advanced driver-assistance system (ADAS) functions and applied it to the Shanghainese ecosystem to improve performance. The alarm sound content is studied and sorted out to conduct user research and spatial sound collection evaluation. Local optimization and the subdivision of data are carried out to generate a user perception set on which the experimental tests and evaluation analysis are implemented. The framework increases the overall efficiency of auditory warning systems and minimizes Human–Machine Interface misunderstandings, thus providing the optimal security scheme for users. Full article

Determination of health hazards of noise pollution is a challenge for any developing city intersection. The people working at roadside open-air shops or near the congested roads of any intersection face intense noise pollution. It becomes very difficult to efficiently determine the hazards of noise on the health of people living near the intersection. An attempt was made to determine the noise-induced health hazards of the developing city of Bahadurpur, UP, India. The noise levels were monitored over 17 station points of the intersection for three months at different times of the day. Equivalent noise level (L_eq) maps were determined within an accuracy of ±4dB. Areas adjacent to intersections indicated noise exposure levels close to 100 dB. Health hazards for the people of the intersection were determined through the testing of auditory and non-auditory health parameters for 100 people. A total of 75–92% of the people who work/live near the noisy intersection were found to be suffering from hearing impairment, tinnitus, sleep disturbance, cardiovascular diseases, hypertension, etc. Whether the recorded health hazards were indeed related to noise exposure was confirmed by testing the health parameters of people from the nearby and less noisy area of Pure Ganga. The nearby site reported mild hazards to the health of the population. An alarming level of hearing impairment was prevalent in the noisy Bahadurpur intersection (79–95%) compared to the same in Pure Ganga (13–30%). The estimated noise-induced health hazards were also compared for noisy and less-noisy study sites using ANOVA statistics. The results suggested that the health hazards reported in the two sites are not similar. Further, the severe hazards to people’s health at the underdeveloped intersection were found to be primarily caused by the intense exposure to noise. Full article

Noise in hospital wards adversely affects the physiological processes of both patients and staff and it is a potential risk for communication breakdowns and errors, causing discomfort and problems regarding the healing of patients, as well as stress, fatigue, and annoyance for staff. Several noise sources are present in the wards, such as HVAC systems, alarms, paging, speech, calls, diagnostic equipment, medical devices, and so forth. This paper describes two surveys carried out at an Italian hospital in Rome to investigate the noise in some wards and to collect self-reported assessments from staff about their working environments, even if such assessments were not required for occupational noise exposure evaluation. Self-reported staff evaluations of the working environment quality and the effects of noise on their performances should be investigated. For this purpose, in this study, questionnaires were designed and submitted to staff members. In addition, noise measurements were taken from short-, medium-, and long-term audio recordings processed to determine psychoacoustic parameters, e.g., loudness, sharpness, roughness, and fluctuation strength. Their applications in enclosed spaces can provide additional information on some features of the noise observed in hospital wards, which may influence the perceptions and relevant extra-auditory effects. Even though the results cannot be generalized, they encourage the development of a methodology for noise surveys in hospital wards, including noise measurements and “ad hoc” questionnaires to collect self-reported reactions from exposed staff members. Full article

Virtual fencing is an innovative alternative to conventional fences. Different systems have been studied, including electric-impulse-free systems. We tested the potential of self-applied acoustic stimulus in deterring the horses from further movement. Thirty warmblood horses were individually introduced to a designated corridor leading toward a food reward (variant F) or a familiar horse (variant S). As the subject reached a distance of 30, 15 or 5 m from a finish line, an acute alarming sound was played. Generally, a sudden and unknown sound was perceived by horses as a threat causing an increase in vigilance and sympathetic activation. Horses’ behaviour and barrier effectiveness (80% for F vs. 20% for S) depended on motivator (F/S), while the cardiac response indicating some level of stress was similar. The motivation for social interactions was too strong to stop the horses from crossing a designated boundary. Conversely, the sound exposure distance did not vary the barrier effectiveness, but it differentiated HRV responses, with the strongest sympathetic activation noted at a distance of 5 m. Thus, the moment of a sound playback has important welfare implications. Due to the limited potential of sound as a virtual barrier, auditory cues cannot be used as an alternative for conventional fencing. Full article

Proactive inhibition is divided into two components: action postponing (AP), which refers to slowing the onset of response, and action restraint (AR), which refers to preventing the response. To date, several studies have reported alterations in proactive inhibition and its associated neural processing among sensory modalities; however, this remains inconclusive owing to several methodological issues. This study aimed to clarify the differences in AP and AR and their neural processing among visual, auditory, and somatosensory modalities using an appropriate experimental paradigm that can assess AP and AR separately. The postponing time calculated by subtracting simple reaction time from Go signal reaction time was shorter in the visual modality than in the other modalities. This was explained by faster neural processing for conflict monitoring induced by anticipating the presence of the No-go signal, supported by the shorter latency of AP-related N2. Furthermore, the percentage of false alarms, which is the reaction to No-go signals, was lower in the visual modality than in the auditory modality. This was attributed to higher neural resources for conflict monitoring induced by the presence of No-go signals, supported by the larger amplitudes of AR-related N2. Our findings revealed the differences in AP and AR and their neural processing among sensory modalities. Full article

In autonomous driving vehicles, the driver can engage in non-driving-related tasks and does not have to pay attention to the driving conditions or engage in manual driving. If an unexpected situation arises that the autonomous vehicle cannot manage, then the vehicle should notify and help the driver to prepare themselves for retaking manual control of the vehicle. Several effective notification methods based on multimodal warning systems have been reported. In this paper, we propose an advanced method that employs alarms for specific conditions by analyzing the differences in the driver’s responses, based on their specific situation, to trigger visual and auditory alarms in autonomous vehicles. Using a driving simulation, we carried out human-in-the-loop experiments that included a total of 38 drivers and 2 scenarios (namely drowsiness and distraction scenarios), each of which included a control-switching stage for implementing an alarm during autonomous driving. Reaction time, gaze indicator, and questionnaire data were collected, and electroencephalography measurements were performed to verify the drowsiness. Based on the experimental results, the drivers exhibited a high alertness to the auditory alarms in both the drowsy and distracted conditions, and the change in the gaze indicator was higher in the distraction condition. The results of this study show that there was a distinct difference between the driver’s response to the alarms signaled in the drowsy and distracted conditions. Accordingly, we propose an advanced notification method and future goals for further investigation on vehicle alarms. Full article