Search Results (282)

The LiDAR sensor embedded in recent iPhone Pro devices offers new opportunities for rapid, low-cost, and spatially detailed assessment of outdoor recreational infrastructure. Framed within mobile sensing and IoT-based environmental monitoring, this proof-of-concept case study evaluates a smartphone-based LiDAR workflow for localized hiking trail assessment under dense canopy conditions. Four independent surveys of a degraded hiking trail section were conducted to assess inter-survey repeatability, agreement with conventional field measurements, and practical field applicability. The resulting three-dimensional models reproduced trail morphology, including incised tread sections, exposed roots, rocky surfaces, and localized irregularities relevant to trail-condition assessment. Repeated registrations demonstrated consistent cloud-to-cloud comparison metrics, while comparisons with field reference measurements showed good agreement in the representation of cross-sectional morphology and exposed root characteristics. Continuous surface reconstruction additionally supported exploratory identification of potential runoff pathways and relative surface depressions. Although limited to a single trail section and one smartphone–application combination (iPhone 13 Pro with Scaniverse), the evaluated workflow demonstrates potential as a rapid, accessible, and relatively low-cost approach for localized trail-condition assessment and provides a foundation for further evaluation in hiking trail monitoring. Full article

The accurate estimation of real-time passenger flow in urban rail transit (URT) networks plays a crucial role in optimizing the operation and management of urban rail transit systems, with profound implications for daily operation scheduling, passenger flow control, and safety management. Most existing studies have relied solely on a single data source such as Automatic Fare Collection (AFC) data for real-time passenger flow estimation. However, the inherent data uploading delay in the urban rail transit AFC system often leads to the delayed acquisition of passenger flow information. This severely limits the timeliness and accuracy of passenger flow estimation, thereby affecting the efficiency of URT operation and management. To address this critical challenge, this study proposes a multi-source data-driven estimation model to achieve the fusion of multi-source heterogeneous data covering the uploaded AFC data, historical passenger flow data and mobile phone signaling data collected from mainstream sensors such as through-beam photoelectric sensors and RFID/NFC sensors. In the proposed model, various types of information are taken into account by extracting features of the different data sources. The advantages of the proposed model are validated by utilizing multi-source data from Chengdu, China. The experimental results demonstrate that the proposed model achieves higher accuracy compared to existing benchmark models. Compared with the second-best-performing model, it reduces the MAE by 8.1%, RMSE by 10.7%, and MAPE by 17.2% at the 15 min time granularity, which shows that the proposed model has effective performance in terms of accuracy and stability. Full article

Background: Aging is marked by a series of changes that impair mobility. These changes are accompanied by declines in balance, postural control, and cognitive function. While the relationship between balance, postural control, and mobility has been extensively investigated, evidence on how different cognitive demands influence mobility performance in daily life remains limited. Accordingly, this study investigated how different cognitive demands associated with cell phone use affect mobility performance in young and older adults. Methods: In this cross-sectional study, 124 cognitively preserved participants (n = 62 young and n = 62 older adults) were enrolled. The participants were subjected to three walking conditions: without distraction, while texting, and while talking on the phone. Wearable motion sensors were used to quantify mobility performance across different task-specific components. Age and cognitive function were assessed to investigate their roles in mobility performance. Results: Older adults exhibited poorer gait performance than young adults, demonstrating longer completion times across multiple functional mobility tasks. Texting and phone conversations adversely affected gait performance to a similar extent. Age explained up to 42.4% of the variability in mobility performance during dual task, whereas cognitive function accounted for an additional 16.6% of the variance. Conclusions: In a cognitively preserved sample, the negative impact of cell phone use while walking was greater in older than in young adults. Age was the primary determinant of mobility performance while cognition played a lesser role. These findings support the need for interventions addressing age-related changes and cognitive demands to improve mobility in older adults. Full article

The Indonesian government should seriously consider the use of renewable energy, given the natural potential that can still be utilized as an environmentally friendly power source. The utilization of renewable energy can be achieved by harnessing available natural resources. Pico hydro power plants (PLTPHs) can serve as an alternative electricity generator for use in Indonesia due to the existing natural potential. The output from this power plant can be utilized directly or stored in batteries. Directly measuring the generator’s performance on-site is deemed less effective. Therefore, a monitoring system is introduced as a solution to allow remote monitoring and display parameters such as voltage, current, frequency, and power of the generator online. This system is designed to display the micro hydro generator’s output parameter data on the Blynk application. The display on the Blynk application can be monitored via a connected mobile phone. Testing of the monitoring system was carried out by comparing two sets of measurements: one through the PZEM-004T sensor system and the other through a kWh meter (Kilowatt-hour meter). For the AC output from the battery with a 12-watt lamp load (tested 4 times), the reading error values obtained were a voltage reading error of 0.2%, a current reading error of 19.4%, a frequency reading error of 0.67%, and a power reading error of 18.2%. Full article

We present a low-cost, fully reproducible software and hardware protocol for smartphone sensor battery cost tests. Our pipeline combines a rigorous hardware checklist and light-sealed enclosure, a software checklist for iOS devices, and a BatteryTest app to control sensor configurations and log battery state during tests. Methodologically, we applied this standardized protocol in 30 independent analyzed test runs using six iPhone 14 Pro and three iPhone 13 Pro devices, and compared battery-life outcomes across predefined sensor conditions (idle, TrueDepth, GPS, accelerometer, pedometer, gyroscope, and rear camera), sampling rates, and sensor-specific settings. Key findings include: (i) Baseline battery life was approximately 10% higher on the 14 Pro versus the 13 Pro models under idle conditions. Sensor activation substantially reduced battery life, with GPS and camera usage exhibiting the strongest impact. (ii) Software parameters matter: the sampling rate change from 27 s to 3 s led to significantly decreased battery life in several scenarios, while reducing the location accuracy in GPS tests increased battery life by up to 20 h on the 13 Pro devices. (iii) Cross-device-generation consistency is heterogeneous. The iPhone 14 Pro lasts up to 50% longer on GPS tests, yet drains about an hour faster than the 13 Pro in camera tests. This work introduces the first standardized, and fully reproducible protocol for quantifying sensor-specific battery consumption on iPhones, enabling consistent, comparable, and low-cost energy benchmarking across device generations. Full article

Infrared thermography provides key information for a wide range of diagnostic applications within built and natural environments. As thermal states are changing with ambient conditions, it is important to deploy thermal imaging systems and operators opportunistically. It is therefore an attractive proposition to make these systems more affordable and accessible. Low-cost thermal sensors generally produce low-resolution outputs. To increase data density across large subjects, diagnosticians may create image mosaics from multiple overlapping thermographs. The registration of individual inputs into large mosaics is aided by the acquisition of additional sensor data (photographs and depthmaps), which can provide critical spatial references. In many cases, the materials inherent to the modern built environment present challenges to traditional data registration workflows between multiple sensor streams. Mobile devices offer an opportunity to innovate in the creation of these mosaics, integrating rapid geospatial mapping functionality with radiometric thermography within a 3D context. In this paper the authors evaluate the FLIR One Pro thermal camera module along with iOS/iPhone specific rapid mapping capabilities, and present a methodology: (1) introducing a workflow for the integration of short-range (within 0.3–5 m capture distance) iPhone mobile sensor data into modeling pipelines; (2) introducing a calibration model enabling effective registration and fusion of multi-modal inputs from the iPhone mobile sensor stack and FLIR One thermographic module; and (3) detailing an alternative open-source methodology for the evaluation and translation of thermographic imagery for multi-sensor fusion. The end product of this pipeline is a 3D radiometric thermographic mosaic: a spatially continuous, textured surface model in which hundreds of individual low-resolution thermographs are fused into a single queryable output retaining full 16-bit temperature values at every point. All datasets have been made openly available and the two case studies used in this paper have been made accessible at full resolution for interactive 3D online viewing. Full article

Water supplies contaminated by heavy metals pose a serious threat to human health, especially in areas without access to centralized testing facilities. While copper is a necessary heavy metal in trace levels, high concentrations can have detrimental effects on health, such as oxidative stress, cognitive impairment, and liver damage. Due to their expense, complexity, and reliance on laboratories, conventional detection techniques are accurate but unsuitable for real-time, dispersed deployment. Machine learning offers a potent solution to these constraints by facilitating the automatic, precise, and quick interpretation of complicated sensor data. It makes it possible to make decisions in real time without requiring a large laboratory infrastructure. In this work, a dual-mode optical sensor was developed using the colorimetry and fluorometry images of carbon dots embedded in hydrogels with the Cu²⁺ concentration of 0, 20, 50, 100, 200, and 500 μM. Data augmentation was used to expand the RGB picture dataset for each modality, and these data were interpolated to provide responses at 1 µM intervals (0–500 µM). We trained a comprehensive set of supervised machine learning models, including Logistic Regression, Support Vector Machines, Random Forest, and XGBoost, to categorize water samples into five risk-informed quality levels. The system achieved classification accuracies exceeding 96%. Furthermore, we built a simple user interface to make the system practically deployable in mobile phone. Together, these results demonstrate a scalable, interpretable, cost-effective, and quick solution for real-time water quality monitoring in resource-constrained environments. Since the proposed method focuses on classifying concentration ranges rather than precise quantification, a formal limit of detection (LOD) was not calculated; instead, the lowest concentration in the experimental dataset serves as the minimum detectable level. Full article

(1) Background: The local divergence exponent (LDE) is a sensitive measure of walking stability deterioration and risk of falling in older adults. We aim to determine the validity the LDE measured using a mobile phone and to assess its ability to discriminate between healthy young and older adults; (2) Methods: 20 older adults (76.4 ± 4.6 years) and 20 young adults (29.1 ± 6.5 yrs) walked for 6 min on a 20-m walkway while wearing a research-grade inertial measurement unit (IMU) and a mobile phone placed on the sternum to record 3D acceleration data. The LDE was calculated using data from both devices for 3D, vertical (VT), mediolateral (ML), anteroposterior (AP), and norm (N) accelerations. ICC (3,1) was used to determine the validity of the mobile phone’s LDE. Mann–Whitney U tests were used to determine age-group discriminability of LDE measures; (3) Results: LDEs demonstrated excellent absolute agreement between the wearable IMU and mobile phone (ICC = 0.844). Mobile phone-derived LDEs demonstrated excellent validity relative to the wearable IMU (ICC > 0.75). No significant age-related differences in LDE were observed; wearable or mobile sensors (both p > 0.05); (4) Conclusions: LDEs measures obtained with a mobile phone are valid. No age group differences were identified. Full article

The emergency evacuation of visually impaired individuals during fire incidents presents critical challenges that require innovative technological solutions. While existing evacuation systems provide static route guidance, they fail to adapt dynamically to evolving fire conditions, blocked passages, or dangerous zones in buildings with multiple routes and exits. This paper presents a comprehensive implementation of a mobile application built with Flutter/Dart that addresses these limitations by enabling real-time, dynamic route computation based on live sensor data. The presented system operates in a decentralized manner, performing all critical computations on-device to ensure its functionality even when some parts of the building infrastructure fail. A dynamic route calculation modified Dijkstra’s algorithm was implemented on each user’s phone for guidance. If initial path adjustments are needed, they are computed from sensor data to evaluate fire evolution and other relevant factors, including the user’s current position and crowd congestion. An audio–visual interface was designed to provide navigation instructions and to help users follow safety routes efficiently. Field testing with visually impaired participants demonstrated significant improvements in evacuation efficiency, with shorter evacuation times than traditional static guidance approaches. The system architecture complies with international fire safety standards while maintaining user privacy through a no-tracking design philosophy. This work contributes to both theoretical advances in adaptive evacuation algorithms and practical insights for deploying assistive technologies in emergency scenarios. Full article

Creating a digital 3D model of museum artefacts has been a common practice for many years. Such models can be used for archiving, research, and marketing purposes, as well as to counteract various types of exclusion. A digital copy created using professional 3D scanners using 3D structured-light scanning (3D SLS) or terrestrial laser scanning technology requires expensive equipment, specialised software for postprocessing, and a trained team. The introduction of mobile phones with Light Detection and Ranging (LiDAR) sensors and the development of appropriate open-access software have enabled the use of phones to generate digital 3D models. This study compares the quality of 3D models created with 3D SLS and mobile LiDAR technologies using three identical small museum artefacts from the Silk Road area of the Samarkand State University museum in Uzbekistan. They were digitised in 2017 and 2025. The results indicate that digital 3D models generated with an iPhone 16 PRO MAX device using Scaniverse LiDAR software are incomplete and thus less versatile. Therefore, they cannot serve as archival models. Their accuracy and quality (mesh density, size, and texture quality), as well as the speed of generating 3D models, make them ideal for marketing purposes and digital tourism. Full article

Facial features hold information about a person’s emotions, motor function, or genetic defects. Since most current mobile devices are capable of real-time face detection using cameras and depth sensors, real-time facial analysis can be utilized in several mobile use cases. Understanding the real-time emotion recognition capabilities of device sensors and frameworks is vital for developing new, valid applications. Therefore, we evaluated on-device emotion recognition using Apple’s ARKit on an iPhone 14 Pro. A native app elicited 36 blend shape-specific movements and 7 discrete emotions from $N=31$ healthy adults. Per frame, standardized ARKit blend shapes were classified using a prototype-based cosine similarity metric; performance was summarized as accuracy and area under the receiver operating characteristic curves. Cosine similarity achieved an overall accuracy of $68.3\%$, exceeding the mean of three human raters ($58.9\%$; $+9.4$ percentage points, ≈16% relative). Per-emotion accuracy was highest for joy, fear, sadness, and surprise, and competitive for anger, disgust, and contempt. AUCs were ≥0.84 for all classes. The method runs in real time on-device using only vector operations, preserving privacy and minimizing compute. These results indicate that a simple, interpretable cosine-similarity classifier over ARKit blend shapes delivers human-comparable, real-time facial emotion recognition on commodity hardware, supporting privacy-preserving mobile applications. Full article

Accurate and efficient forest inventory methods are crucial for monitoring forest ecosystems, assessing carbon stocks, and supporting sustainable forest management. Traditional field-based techniques, which rely on manual measurements such as diameter at breast height (DBH) and tree height (TH), remain labour-intensive and time-consuming. In this study, we introduce and validate a fully open-source, low-cost terrestrial laser scanning system (LCA-TLS) built from commercially available components and based on the Livox Avia sensor. With a total cost of €2050, the system responds to recent technological developments that have significantly reduced hardware expenses while retaining high data quality. This trend has created new opportunities for broadening access to high-resolution 3D data in ecological research. The performance of the LCA-TLS was assessed under controlled and field conditions and benchmarked against three reference devices: the RIEGL VZ-1000 terrestrial laser scanner, the Stonex X120GO handheld mobile laser scanner, and the iPhone 15 Pro Max structured-light device. The LCA-TLS achieved high accuracy for estimating DBH (RMSE: 1.50 cm) and TH (RMSE: 0.99 m), outperforming the iPhone and yielding results statistically comparable to the Stonex X120GO (DBH RMSE: 1.32 cm; p > 0.05), despite the latter being roughly ten times more expensive. While the RIEGL system produced the most accurate measurements, its cost exceeded that of the LCA-TLS by a factor of about 30. The hardware design, control software, and processing workflow of the LCA-TLS are fully open-source, allowing users worldwide to build, modify, and apply the system with minimal resources. The proposed solution thus represents a practical, cost-effective, and accessible alternative for 3D forest inventory and LiDAR-based ecosystem monitoring. Full article

Smart phones have become an integral part of our lives in modern society, as we carry and use them throughout a day. However, this “body part” may maliciously collect and leak our personal information without our knowledge. When we install mobile applications on our smart phones and grant their permission requests, these apps can use sensors embedded in the smart phones and the stored data to gather and infer our personal information, preferences, and habits. In this paper, we present our preliminary results on quantifying the privacy risk of mobile applications by assessing whether requested permissions are necessary based on app descriptions through textual entailment decided by language models (LMs). We observe that despite incorporating various improvements of LMs proposed in the literature for natural language processing (NLP) tasks, the performance of the trained model remains far from ideal. Full article

With the widespread adoption of smartphones and wearable devices, localization systems have become increasingly important in modern society. While Global Positioning System (GPS) technology is widely accepted as a standard outdoors, accurately determining user location indoors remains a significant challenge despite extensive research efforts. Indoor positioning systems (IPSs) play a critical role in various sectors, including retail, tourism, transportation, healthcare, and emergency services. However, existing solutions require costly infrastructure deployments, complex area mapping, or offer suboptimal user experiences without achieving satisfactory accuracy. This paper introduces FloorTag, a scalable, low-cost, and minimally invasive hybrid IPS designed specifically for smartphone platforms. FloorTag leverages a combination of 2D visual markers placed on floor surfaces at key locations, and inertial sensor data from mobile devices. Each marker is associated with a unique identifier and precise spatial coordinates, enabling an immediate reset of accumulated localization errors upon detection. Between markers, a pedometer-based dead reckoning module maintains continuous location tracking. The localization process is designed to be seamless and unobtrusive to the user. When activated by the app during navigation, the phone’s rear camera, naturally angled toward the floor during walking, captures markers. This solution avoids explicit user scans while preserving the performance benefits of visual positioning. To model the indoor environment, FloorTag introduces the concept of Path-Points, which discretize the walkable space, and Informative Layers, which add semantic context to the navigation experience. This paper details the proposed methodology and the client–server system architecture and presents experimental results obtained from a prototype deployed in an academic building at the University of Catania, Italy. The findings demonstrate reliable localization at approximately 2 m spatial granularity and near-real-time performance across varying lighting conditions, confirming the feasibility of the approach and the effectiveness of the system. Full article

Anaerobic reactors are often used to control emissions and capture greenhouse gas (GHG) (biogas, a mixture of carbon dioxide and methane) from waste such as dairy manure. However, real-time monitoring of biogas production during in vitro anaerobic experiments is often challenging mainly due to the unpredictable and low levels of biogas production in a lab reactor system. The application of Internet of Things (IoT) technologies can enhance real-time monitoring of biogas production and GHG emissions from livestock waste. Integration of IoT to anaerobic reactors provides transformative solutions for low-cost monitoring. In this study, an IoT based sensor system that included a highly sensitive Renesas mass flow sensor module for biogas monitoring, Adafruit ported pressure sensor for monitoring of reactor pressure, and ultra-small DROK temperature probe for temperature monitoring was built and implemented for determining the biogas production in anaerobic reactors. Further, impacts of anaerobic process on the reduction of pathogenic organisms such as E. coli were determined using the conventional culture-based method. Results showed that the application of the IoT based system was able to monitor biogas production in real-time, and transmit the data to mobile phone using the ThingSpeak IoT platform offered by MathWorks (MATLAB) (Natick, MA, USA). The difference between the sensor’s biogas volume readings and actual observations over a 30-day time interval was 5–6% indicating the high level of accuracy and low error levels of the system. Further, results showed 1.6–4.8 log reductions of E. coli in effluent of anaerobic reactors indicating substantial impacts of the anaerobic process on pathogen indicator reduction. We anticipate that the system we used in this study has a substantial potential to enhance monitoring of anaerobic reactors and GHG emissions from livestock waste. Full article