Search Results (117)

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

A textile woven presence sensor was previously presented to demonstrate its functionality in eliminating some false positives on car seat presence sensors. After studying the functionality, the next characteristic that the textile sensor should demonstrate is its reliability. The woven sensor was prepared to be tested against ageing. The ageing cycle was prepared according to the UNE-EN ISO 17228:2015 standard. Nine woven sensors are prepared, seven of them face the aging test, and two are selected as reference sensors. The characterization of the woven sensor has been carried out through a microcontroller measurement circuit that obtains the cycles to charge the sensor. Comparison of the results obtained shows that the effects of ageing are negligible. The behavior of the aged sensors is similar to that of the reference sensors, indicating that the variations in the values of both aged and reference sensors are provoked by the environmental conditions during the measurements. To support this argument, a statistical study based on a t-Student analysis was carried out. After 4 ageing cycles, the functionality of the sensors remains unaffected. This research proves the reliability of the woven textile sensor, which encourages its use in automotive applications. Full article

Objectives: Wheelchair seat sagging is hypothesized to increase pressure and shear forces, potentially leading to pressure injuries. The objective of this study was to assess the impact of correcting wheelchair seat sagging on ischial pressure, shear force, and posture in a population of healthy adults. Methods: A total of twenty-two participants who met the study requirements were included in the study. Participants were evaluated under two conditions: with seat base correction (With Correction) and without it (No Correction). Correction was achieved using insert panels. Ischial pressure was measured using a pressure-mapping system (CONFORMat), shear force with a specialized sensor (iShear), and posture with accelerometers (TSND151). The primary analysis compared peak pressure index (PPI), shear force, slide, and postural changes between conditions. The subgroup analysis was conducted as an exploratory approach to assess potential variation among participants with elevated shear forces. Results: There was no statistically significant difference in ischial pressure between the No Correction and With Correction conditions (p = 0.37). However, shear force and slide were significantly reduced when seat sagging was corrected (p < 0.05). Accelerometer data showed no significant difference in postural changes between conditions (p ≥ 0.05), although the With Correction condition displayed a slight trend toward greater positional variability over time. Conclusions: These findings indicate that correcting seat sagging can reduce shear force and slide, potentially lowering the risk of pressure injuries. However, because this study targeted healthy adults, further research involving older or at-risk populations is necessary. Addressing seat sagging could be an important component of comprehensive pressure injury prevention strategies. Full article

Uneven roads pose significant challenges to vehicle stability, passenger comfort, and safety, especially in snowy and mountainous regions. These problems are often complex and challenging to resolve with traditional detection and stabilization methods. This paper presents a dual-method approach to improving vehicle stability by identifying road irregularities and dynamically adjusting the balance. The proposed solution combines YOLOv8 for real-time road anomaly detection with a GY-521 sensor to track the speed of servo motors, facilitating immediate stabilization. YOLOv8 achieves a peak precision of 0.99 at a confidence threshold of 1.0 rate in surface recognition, surpassing conventional sensor-based detection. The vehicle design is divided into two sections: an upper passenger seating area and a lower section that contains the engine and wheels. The GY-521 sensor is strategically placed to monitor road conditions, while the servomotor stabilizes the upper section, ensuring passenger comfort and reducing the risk of accidents. This setup maintains stability even on uneven terrain. Furthermore, the proposed solution significantly reduces collision risk, vehicle wear, and maintenance costs while improving operational efficiency. Its compatibility with various vehicles and capabilities makes it an excellent candidate for enhancing road safety and driving experience in challenging environments. In addition, this work marks a crucial step towards a safer, more sustainable, and more comfortable transportation system. Full article

In-vehicle physiological sensing is emerging as a vital approach to enhancing driver monitoring and overall automotive safety. This pilot study explores the feasibility of a pressure-based system, repurposing commonplace occupant classification electronics to capture respiration signals during real-world driving. Data were collected from a driver-seat-embedded, fluid-filled pressure bladder sensor during normal on-road driving. The sensor output was processed using simple filtering techniques to isolate low-amplitude respiratory signals from substantial background noise and motion artifacts. The experimental results indicate that the system reliably detects the respiration rate despite the dynamic environment, achieving a mean absolute error of 1.5 breaths per minute with a standard deviation of 1.87 breaths per minute (9.2% of the mean true respiration rate), thereby bridging the gap between controlled laboratory tests and real-world automotive deployment. These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. This study contributes to the development of robust and cost-effective in-cabin sensor systems that have the potential to improve road safety and health monitoring in automotive settings. Full article

Car safety can be enhanced by enabling the Airbag Control Unit (ACU) to adaptively deploy different charges based on the occupant’s position once the crash occurs. In this context, monitoring the occupant’s position using a sensorized seat integrated with an Inertial Measurement Unit (IMU) offers a practical and cost-effective solution. However, certain challenges still need to be addressed. The adoption of sensorized seats in research and vehicle set-up is still under consideration. This study investigates an interface device that can be reconfigured to suit almost any seat model. This reconfigurability makes it easily adaptable to new vehicles under development and applicable to any passenger seat in the vehicle. This paper details the device’s design, including its programming using calibration and monitoring features, which significantly improves its reliability compared to earlier prototypes. Extensive testing through real driving experiments with multiple participants demonstrated an accuracy range of 45–100%. The testing involved both drivers and passengers, showcasing the device’s ability to effectively monitor various in-car scenarios. Full article

Human activity recognition in privacy-sensitive environments, such as bathrooms, presents significant challenges due to the need for non-invasive and anonymous monitoring. Traditional vision-based methods raise privacy concerns, while wearable sensors require user compliance. This study explores a radar-based approach for recognizing the activities of daily living in a bathroom setting, utilizing a BGT60TR13C Xensiv 60 GHz radar, manufactured by Infineon Technologies AG (Munich, Germany, EU), to classify human movements without capturing identifiable biometric features. A dataset was collected from seven volunteers performing ten activities which are part of daily living, including activities unique to bathroom environments, such as face washing, teeth brushing, dressing/undressing, and resting on the toilet seat. Deep learning models based on pre-trained feature extractors combined with bidirectional long short-term memory networks were employed for classification. Among the 16 pre-trained networks evaluated, DenseNet201 achieved the highest overall accuracy (97.02%), followed by ResNet50 (94.57%), with the classification accuracy varying by activity. The results highlight the feasibility of Doppler radar-based human activity recognition in privacy-sensitive settings, demonstrating strong recognition performance for most activities while identifying lying down and getting up as more challenging cases due to their motion similarity. The findings suggest that radar-based human activity recognition is a viable alternative to other more invasive monitoring systems (e.g., camera-based), offering an effective, privacy-preserving solution for smart home and healthcare applications. Full article

This study leverages physical fitness assessment sensors to investigate the effects of a brief in-season break (detraining period) on the physical performance of female basketball players. Sixty-seven players (Senior (n = 19), U18 (n = 19), and U14 (n = 29)) were evaluated before and after a 3-week break using sensor-derived data from a countermovement jump (CMJ), an Abalakov jump (ABK), a linear speed test (20 m sprint), a seated medicine ball throw test (SMBT), and a Basketball-Specific Agility Test (TEA-Basket). The Total Score of Athleticism (TSA), computed as the mean Z-Score across tests, served as a composite indicator of physical fitness. Data obtained from performance sensors revealed significant interactions between time and category for the CMJ, ABK, 20 m sprint, and SMBT, while TEA-Basket measurements showed no significant changes. Time and baseline fitness level interactions were also significant for the CMJ, ABK, and SMBT but not for sprint time or the TEA-Basket. Despite observed declines in explosive strength, speed, and upper-body power across all groups, TSA scores remained stable. These findings underscore the utility of sensor-based evaluation methods in highlighting the adverse effects of short-term detraining and emphasize the necessity of tailored training strategies during competitive breaks. Full article

The advent of geomatic techniques and novel sensors has opened the road to new approaches in mapping, including morphological ones. The evolution of a land portion and its graphical representation constitutes a fundamental aspect for scientific and land planning purposes. In this context, new paradigms for geomorphological mapping, which are useful for modernizing traditional, geomorphological mapping, become necessary for the creation of scalable digital representation of processes and landforms. A fully remote mapping approach, based on multi-source and multi-sensor applications, was implemented for the recognition of landforms and processes. This methodology was applied to a study site located in central Italy, characterized by the presence of ‘calanchi’ (i.e., badlands). Considering primarily the increasing availability of regional LiDAR products, an automated landform classification, i.e., Geomorphons, was adopted to map landforms at the slope scale. Simultaneously, by collecting and digitizing a time-series of historical orthoimages, a multi-temporal analysis was performed. Finally, surveying the area with an unmanned aerial vehicle, exploiting the high-resolution digital terrain model and orthoimage, a local-scale geomorphological map was produced. The proposed approach has proven to be well capable of identifying the variety of processes acting on the pilot area, identifying various genetic types of geomorphic processes with a nested hierarchy, where runoff-associated landforms coexist with gravitational ones. Large ancient mass movement characterizes the upper part of the basin, forming deep-seated gravity deformation, highly remodeled by a set of widespread runoff features forming rills, gullies, and secondary shallow landslides. The extended badlands areas imposed on Plio-Pleistocene clays are typically affected by sheet wash and rill and gully erosion causing high potential of sediment loss and the occurrence of earth- and mudflows, often interfering and affecting agricultural areas and anthropic elements. This approach guarantees a multi-scale and multi-temporal cartographic model for a full-coverage representation of landforms, representing a useful tool for land planning purposes. Full article

The present paper investigates the comparative impact of vibrations on operator safety for two diesel and electric agricultural tractors under real operating conditions. Vibrations were measured using four triaxial accelerometers installed at critical points, including the seat base, backrest, floor, and operator’s head. Tests were conducted on two comparable tractor models, a diesel New Holland TCE 50 and an electric prototype TE-0, across four types of terrains (concrete, grass, uneven agricultural road, and plowed land) and at two working speeds (5 km/h and 10 km/h). The root mean square (RMS) accelerations, seat-to-head transmissibility, and isolation efficiency were calculated in compliance with ISO 2631 standards to evaluate the effects on operator health and comfort. The results showed superior vibration isolation efficiency for the electric tractor, particularly within the critical frequency range of 4–12 Hz, where human health risks are most significant and a better isolation efficiency of 98%, significantly reducing operator exposure to harmful vibrations. These findings highlight the potential of electric tractors to improve operator comfort, safety, and long-term health in agricultural applications. Full article

The purpose of the current study was to determine the test–retest reliability and concurrent validity of a photoplethysmography (PPG) finger sensor when collecting heart rate variability (HRV) metrics in reference to electrocardiography (ECG) and heart rate monitor (HRM) devices. Five minutes of R-R interval data were collected from 45 participants (23 females; age: 23.13 ± 4.45 yrs; body mass index: 25.39 ± 4.13 kg/m²) in the supine and seated positions in testing sessions 48 h apart. Moderate-to-excellent test–retest reliability of the HRV data collected from the PPG sensor was identified (ICC_2,1 = 0.60–0.93). Additionally, similar standard errors of the mean, coefficient of variation, and minimal detectable change metrics were observed across all devices. Statistically significant (p < 0.05) differences were identified in the HRV data between the PPG sensor and ECG and HRM devices; however, these differences were interpreted as trivial-to-small (g = 0.00–0.59). Further, the PPG sensor tended to only overestimate HRV metrics by <0.5 ms and near perfect relationships (r = 0.91–1.00) and very large-to-near perfect agreement (CCC = 0.81–1.00) were identified between collection methods. The PPG sensor demonstrated adequate test–retest reliability and concurrent validity in both the supine and seated resting positions. Full article

Integrating autonomous shuttles into public transportation systems holds immense potential to revolutionize urban mobility and enhance accessibility. This paper focuses on a comprehensive analysis of the perceptions of practitioners and industry experts and proposes best practices for effectively integrating autonomous shuttles into public transportation systems. Perceptions of stakeholders have been collected, and a two-fold analysis was performed. Critical barriers for the adoption of autonomous shuttles were identified using the Garette ranking method and principal component analysis (PCA). Recommendations covering different aspects, including underutilization, safety concerns, seating arrangements, reliability, data security, operational aspects, sensor technology, and lane use, are provided. They encompass operational adjustments, infrastructure enhancements, safety measures, policy considerations, and economic foresight. The findings emphasize the importance of extending pilot deployment trial periods, improving autonomy, strategically positioning sensors, enhancing road signage, and providing dedicated lanes for autonomous shuttles. Data-security policies, operator training, and stakeholder responsibilities are also highlighted to build trust and facilitate a seamless transition to autonomous shuttles. This paper concludes by providing recommendations to ensure the successful integration of autonomous shuttles, fostering widespread acceptance and shaping the future of urban transportation. Full article

This research explores the design of a system for monitoring driver drowsiness and supervising seat belt usage in interprovincial buses. In Peru, road accidents involving long-distance bus transportation amounted to 5449 in 2022, and the human factor plays a significant role. It is essential to understand how the use of non-invasive sensors for monitoring and supervising passengers and drivers can enhance safety in interprovincial transportation. The objective of this research is to develop a system using a Raspberry Pi 4 and Arduino Nano that allows for the storage of monitoring data. To achieve this, a conventional camera and MediaPipe were used for driver drowsiness detection, while passenger supervision was carried out using a combination of commercially available sensors as well as custom-built sensors. RS485 communication was utilized to store data related to both the driver and passengers. The simulations conducted demonstrate a high level of reliability in detecting driver drowsiness under specific conditions and the correct operation of the sensors for passenger supervision. Therefore, the proposed system is feasible and can be implemented for real-world testing. The implications of this research suggest that the system’s cost is not a barrier to its implementation, thus contributing to improved safety in interprovincial transportation. Full article

With the rapid rise in technological advancements, health can be tracked and monitored in multiple ways. Tracking and monitoring healthcare gives the option to give precise interventions to people, enabling them to focus more on healthier lifestyles by minimising health issues concerning long screen time. Artificial Intelligence (AI) techniques like the Large Language Model (LLM) technology enable intelligent smart assistants to be used on mobile devices and in other cases. The proposed system uses the power of IoT and LLMs to create a virtual personal assistant for long-time screen users by monitoring their health parameters, with various sensors for the real-time monitoring of seating posture, heartbeat, stress levels, and the motion tracking of eye movements, etc., to constantly track, give necessary advice, and make sure that their vitals are as expected and within the safety parameters. The intelligent system combines the power of AI and Natural Language Processing (NLP) to build a virtual assistant embedded into the screens of mobile devices, laptops, desktops, and other screen devices, which employees across various workspaces use. The intelligent screen, with the integration of multiple sensors, tracks and monitors the users’ vitals along with various other necessary health parameters, and alerts them to take breaks, have water, and refresh, ensuring that the users stay healthy while using the system for work. These systems also suggest necessary exercises for the eyes, head, and other body parts. The proposed smart system is supported by user recognition to identify the current user and suggest advisory actions accordingly. The system also adapts and ensures that the users enjoy proper relaxation and focus when using the system, providing a flexible and personalised experience. The intelligent screen system monitors and improves the health of employees who have to work for a long time, thereby enhancing the productivity and concentration of employees in various organisations. Full article

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject’s waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement. Full article