Search Results (317)

Background/Objectives: Mobile health (mHealth) tools, such as smartphone apps, support person-centred care for persons with hearing loss engaging in the hearing aid management process. Hearing-aid-connected apps are increasingly common in audiological care, making it important to evaluate their availability and quality for clinicians, developers, and end-users. This scoping review aimed to identify, summarize, and synthesize information on clinically available hearing-aid-connected apps and evaluate their quality. Methods: A search of the Apple App Store (Canada) was conducted in August 2024 to identify current hearing-aid-connected apps that support hearing aid management. Metadata and features were extracted, and app quality was assessed using the Mobile Application Rating Scale (MARS). Quality was assessed across four objective domains (engagement, functionality, aesthetics, and information) and one subjective domain. Results: Apps had varying levels of metadata detail, including updates, compatibility, and target populations. All apps included common hearing aid controls (e.g., volume adjustment, microphone directionality), while more specialized features (tinnitus management, health tracking, remote clinician support) varied. High-performing apps scored significantly higher in engagement, functionality, aesthetics, and subjective quality, and all apps scored low in information quality, particularly for evidence and credibility. Conclusions: Findings highlight the need for transparent and informative metadata reporting and patient-centred design to improve clinical awareness, usability, and uptake of hearing-aid-connected apps. Full article

Nanotechnology offers powerful new tools to enhance food quality monitoring and safety assurance. In the food industry, nanoscale materials (e.g., metal, metal oxide, carbon, and polymeric nanomaterials) are being integrated into sensory systems to detect spoilage, contamination, and intentional food tampering with unprecedented sensitivity. Nanosensors can rapidly identify foodborne pathogens, toxins, and chemical changes that signal spoilage, overcoming the limitations of conventional assays that are often slow, costly, or require expert operation. These advances translate into improved food safety and extended shelf-life by allowing early intervention (for example, via antimicrobial nano-coatings) to prevent spoilage. This review provides a comprehensive overview of the types of nanomaterials used in food sensory applications and their mechanisms of action. We examine current applications in detecting food spoilage indicators and adulterants, as well as recent innovations in smart packaging and continuous freshness monitoring. The advantages of nanomaterials—including heightened analytical sensitivity, specificity, and the ability to combine sensing with active preservative functions—are highlighted alongside important toxicological and regulatory considerations. Overall, nanomaterials are driving the development of smarter food packaging and sensor systems that promise safer foods, reduced waste, and empowered consumers. However, realizing this potential will require addressing safety concerns and establishing clear regulations to ensure responsible deployment of nano-enabled food sensing technologies. Representative figures of merit include Au/AgNP melamine tests with LOD 0.04–0.07 mg L⁻¹ and minute-scale readout, a smartphone Au@carbon-QD assay with LOD 3.6 nM, Fe₃O₄/DPV detection of Sudan I at 0.001 µM (linear 0.01–20 µM), and a reusable Au–Fe₃O₄ piezo-electrochemical immunosensor for aflatoxin B1 with LOD 0.07 ng mL⁻¹ (≈15 × reuse), alongside freshness labels that track TVB-N/amine in near-real time and e-nose arrays distinguishing spoilage stages. Full article

Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance. Full article

Manual wheelchair users (MWUs) are at high risk of developing pressure injuries (PIs) from prolonged static sitting. Clinical practice guidelines suggest periodic pressure redistribution (PR) to mitigate this risk. Prior work has demonstrated that a wheelchair seat pan instrumented with force sensors can track the change in center of pressure (CoP) as MWUs perform PR and use this measurement to infer the direction and degree of a PR. This study’s objective was to quantify the relationship between change in CoP and reduction in seat interface pressure (SIP) under the ischial tuberosities for commonly practiced PR maneuvers. A theoretical model relating SIP and change in CoP for forward leaning PR was developed. Participants performed forward, leftward, and rightward leaning PRs while seated on a pressure mat on the test wheelchair with a load cell-instrumented seat pan. Linear mixed-effects models showed that the relationship of SIP and CoP varies by participant. Across participants, the change in SIP for a given change in CoP was greater with sideways than with forward leans. The type of cushion used did not affect the relationship. These findings can be used as part of her real-time smartphone-based coaching system for PI prevention. Full article

Precise kinematic positioning using low-cost android smartphones remains a significant research focus, particularly with the growing integration of Global Navigation Satellite System (GNSS) capabilities in these devices. This research explores the accuracy of the single-frequency quad-constellation carrier-phase-based real-time kinematic (RTK) and code-only relative positioning (RP) techniques using Xiaomi 11T smartphone for transportation applications. Kinematic GNSS measurements from Xiaomi 11T are acquired using vehicle trajectory in New Aswan City, Egypt; then, the acquired data are processed utilizing various constellation combinations scenarios including GPS-only, GPS/Galileo, GPS/GLONASS, GPS/BeiDou, and GPS/Galileo/GLONASS/BeiDou. The processing outputs demonstrate that sub-meter and meter-level horizontal position accuracy is achieved for both scenarios using RTK and RP, respectively. The quad-constellation processing scenario has superiority with 0.456 m and 1.541 m root mean square error (RMSE) values in the horizontal component involving RTK and RP, respectively; on the other hand, the GPS-only solution achieved 0.766 m and 1.703 m horizontal RMSE values using RTK and RP, respectively. Based on the attained accuracy, the cost-effective Xiaomi 11T provides sufficient positioning accuracy to support transportation applications such as an intelligent transportation system, urban/public transportation monitoring, fleet management, vehicle tracking, and mobility analysis, aiding smart city planning and transportation system optimization. Full article

Indoor navigation (IN) systems are increasingly essential in environments where GPS signals are unreliable, such as hospitals, airports, and large public buildings. This study explores a smartphone-based approach to indoor positioning that leverages inertial sensor data for accurate step detection and counting, which are fundamental components of pedestrian dead reckoning. A long short-term memory (LSTM) network was trained to recognize step patterns across a variety of indoor movement scenarios. The generalized model achieved an average step detection accuracy of 93%, while scenario-specific models tailored to particular movement types such as turning, stair use, or interrupted walking achieved up to 96% accuracy. The results demonstrate that incorporating activity-specific training improves performance, particularly under complex motion conditions. Challenges such as false positives from abrupt stops and non-walking activities were reduced through model specialization. Although the system performed well offline, real-time deployment on mobile devices requires further optimization to address latency constraints. The proposed approach contributes to the development of accessible and cost-effective indoor navigation systems using widely available smartphone hardware and offers a foundation for future improvements in real-time pedestrian tracking and localization. Full article

Keel bone lesions (KBLs) represent a relevant welfare concern in laying hens, arising from complex interactions among genetics, housing systems, and management practices. This study presents the development of an image analysis system for the automated detection and classification of KBLs in slaughterhouse videos, enabling scalable and retrospective welfare assessment. In addition to lesion classification, the system can track and count individual carcasses, providing estimates of the total number of specimens with and without significant lesions. Videos of brown laying hens from a commercial slaughterhouse in northeastern Italy were recorded on the processing line using a smartphone. Six hundred frames were extracted and annotated by three independent observers using a three-scale scoring system. A dataset was constructed by combining the original frames with crops centered on the keel area. To address class imbalance, samples of class 1 (damaged keel bones) were augmented by a factor of nine, compared to a factor of three for class 0 (no or mild lesion). A YOLO-based model was trained for both detection and classification tasks. The model achieved an F1 score of 0.85 and a mAP@0.5 of 0.892. A BoT-SORT tracker was evaluated against human annotations on a 5 min video, achieving an F1 score of 0.882 for the classification task. Potential improvements include increasing the number and variability of annotated images, refining annotation protocols, and enhancing model performance under varying slaughterhouse lighting and positioning conditions. The model could be applied in routine slaughter inspections to support welfare assessment in large populations of animals. Full article

Electronic quick response (e-QR) codes provide access to real-time sensor data using smartphone readers and internet connectivity. Printed electronics and hybrid integration on flexible substrates is a promising solution for wide-scale and low-cost deployment of sensor systems. This paper presents a 21 × 21-pixel e-QR display implemented on black Kapton using hybrid additive and subtractive microfabrication techniques. The process flow for the double-sided circuit allows for layer alignment using multiple fiducial markers. The steps include inkjet printing of tracks on both sides of the substrate, laser-cut via holes, stencil-aided via filling, solder paste dispensing, and final integration of discrete surface-mount components by semi-automatic pick-and-place. Full article

This study investigates the use of Autonomous Sensing Infrastructure and Connected and Autonomous Vehicles (CAV) technologies to support infrastructure-to-vehicle (I2V) and infrastructure-to-everything (I2X) communications, including the alerting of drivers and pedestrians. It describes research findings in the following CAV functionalities: (1) Intersection-based object detection and tracking; (2) Basic Safety Message (BSM) generation and transmission; and (3) In-Vehicle BSM receipt and display, including handheld (smartphone) application BSM receipt and user presentation. The study summarizes the various software and hardware components used to create the I2V and I2X prototype solutions, which include open-source and commercial software as well as industry-standard transportation infrastructure hardware, e.g., Signal Controllers. Results from in-lab testing demonstrate effective object detection (e.g., pedestrians, bicycles) based on sample traffic camera video feeds as well as successful BSM message generation and receipt using the leveraged software and hardware components. The I2V and I2X solutions created as part of this research are scheduled to be deployed in a real-world intersection in coordination with state and local transportation agencies. Full article

Driven by the demand for cost-effective vehicle access, enhanced flexibility, and sustainable transportation practices, smart car-sharing has emerged as a prominent alternative to traditional vehicle rental systems. Leveraging the Internet of Vehicles (IoV) and wireless communication, these systems feature dynamic renter-vehicle mappings, enabling users to access any available vehicle rather than being restricted to a specific one pre-assigned by the service provider. However, many existing schemes in the IoV field conflate users and vehicles, complicating the identification and tracking of the vehicle’s actual driver. Moreover, most current authentication protocols rely on a strict, initial binding between a user and a vehicle, rendering them unsuitable for the dynamic nature of car-sharing environments. To address these challenges, we propose an enhanced certificateless signature scheme tailored for smart car-sharing. By employing a biometric fuzzy extractor and the Chinese Remainder Theorem, our scheme provides a fine-grained authentication mechanism that eliminates the need for local computations on the user’s side, meaning users do not require a smartphone or other digital device. Furthermore, our scheme introduces category identifiers to facilitate vehicle selection based on specific classes within car-sharing contexts. A formal security analysis demonstrates that our scheme is existentially unforgeable against adversaries under the random oracle model. Finally, a comprehensive evaluation shows that our proposed scheme achieves competitive performance in terms of computational and communication overhead while offering enhanced practical functionalities. Full article

Physical rehabilitation is crucial for injury recovery, offering pain relief and faster healing. However, traditional methods rely heavily on in-person professional feedback, which can be time-consuming, expensive, and prone to human error, limiting accessibility and effectiveness. As a result, patients are often encouraged to perform exercises at home; however, due to the lack of professional guidance, motivation dwindles and adherence becomes a challenge. To address this, this paper proposes a smartphone-based solution that enables patients to receive exercise feedback independently. This paper reviews current Computer Vision systems for assessing rehabilitation exercises and introduces an intelligent system designed to assist patients in their recovery. Our proposed system uses motion tracking based on Computer Vision, analyzing videos recorded with a smartphone. With accessibility as a priority, the system is evaluated against the advanced Qualysis Motion Capture System using a dataset labeled by expert physicians. The framework focuses on human pose detection and movement quality assessment, aiming to reduce recovery times, minimize human error, and make rehabilitation more accessible. This proof-of-concept study was conducted as a pilot evaluation involving 15 participants, consistent with earlier work in the field, and serves to assess feasibility before scaling to larger datasets. This innovative approach has the potential to transform rehabilitation, providing accurate feedback and support to patients without the need for in-person supervision or specialized equipment. Full article

Background: Inpatient palliative care consultation services operate with an interdisciplinary team, where effective handoffs are crucial for coordinated patient care. We aimed to replace encrypted email handoffs with a more concise and uniform handoff using I-PASS (illness severity, patient summary, action list, situational awareness, contingency planning, and synthesis by receiver) integrated within the electronic medical record (EMR). Aim and Measures: Within six months of launch, our goal was to achieve 90% I-PASS utilization for hospitalized acutely ill patients with cancer receiving palliative care consultation. Intervention: In January 2021, our quality improvement team, consisting of physicians, advanced practice providers, and trainees, began implementing I-PASS using the plan–do–study–act cycle. After providing training sessions for all palliative care clinicians, I-PASS went live on October 1, 2021. I-PASS utilization was tracked via random and monthly audits of EMRs. Through anonymous surveys, both pre- and post-implementation, we gathered clinician feedback and concerns about the handoff system. Survey responses were compared using the Mann–Whitney test. Outcomes: Within six months of implementation, the I-PASS utilization rate reached > 99%. The survey participation rates were 70% (45/64) and 82% (49/60) for the pre-and post-implementation periods, respectively. Respondents provided answers on one to five scale (mean, standard deviation, SD): lower accuracy with email (3.53, SD = 0.98) vs. I-PASS (4.20, SD = 0.83), p < 0.001; handoff lengthier with email (4.17, SD = 1.05) vs. I-PASS (2.1, SD = 1.15), p < 0.001; the time required was longer with email (3.0, SD = 1.22) vs. I-PASS (1.71, SD = 0.73), p < 0.001. Overall, respondents found I-PASS to be significantly better (4.69, SD = 0.58). Conclusion: I-PASS was fully adopted by the team, with nearly 100% utilization and strong clinician endorsement as an effective communication tool. Future efforts should focus on optimizing usability, particularly by educating clinicians on smartphone EMR access and enabling the timely and streamlined editing of I-PASS. Full article

In the digital age, smartphones are often used as tools for emotion regulation. While prior research has examined affective predictors of smartphone use, few studies have considered the combined impact of short-term and long-term affective states. This study investigates how daily negative emotional states and psychological distress relate to smartphone use and whether these associations are moderated by locus of control, a core belief about perceived control. Thirty-seven participants completed a one-month daily diary study combined with objective smartphone usage tracking, which yielded 837 valid observations. Multilevel analyses showed no association between daily negative emotional state and smartphone use. However, psychological distress predicted divergent behavioral patterns based on locus of control: individuals with an internal locus of control showed reduced usage under distress, whereas those with an external locus of control exhibited increased frequency of use. These findings highlight the importance of individual control beliefs in shaping technology-mediated emotion regulation and offer implications for interventions targeting excessive smartphone use. Full article

Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, but by estimating their position based on how closely their observed behavior matches the expected progression along a predefined route. This concept, while not universally applicable, is well-suited for specific indoor navigation scenarios, such as guiding couriers or delivery personnel through complex residential buildings. We explore this idea in detail in our paper. To implement this behavior-based localization, we introduce an LSTM-based method for classifying user behavior patterns, including standing, walking, and using stairs or elevators, by analyzing velocity sequences derived from smartphone sensors’ data. The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. These results confirm the viability of real-time activity recognition as the foundation for a navigation system that aligns live user behavior with pre-recorded patterns, offering a cost-effective alternative to infrastructure-heavy indoor positioning systems. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article