Search Results (4,493)

Accurate indoor–outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98–99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor–outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness. Full article

Several scientific studies have highlighted the negative impact of new technologies (NTs) on children’s psychological development, both in terms of emotional and cognitive development. NTs, such as smartphones, tablets, and video games, have a significant impact on children’s development, both in terms of social relationships and cognitive functions. This study aims to identify and explore the cultural models that shape children’s exposure to new technologies in early childhood. This study involved 48 subjects between parents and infant educators. Unstructured interviews were conducted. Emotional Text Analysis was applied. The findings reveal the existence of three cultural repertoires (clusters): Connected but isolated (45.2), Technology education (30%), and Mistrust (24.8%). Their placement in the factorial space explains how the negative effects on children’s psychological development are determined. Technology education seems to be a protective factor for the cognitive and affective development of children. These findings are discussed, comparing them with Musk’s recent experiment and the rapid loss of social ties due to the lack of an educational plan. Full article

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps’ features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia’s Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: −1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods. Full article

Humans are exposed to significant amounts of blue light from computers and smartphones. However, the effects of blue light on ulcerative colitis remain unclear. In this study, we assessed blue-light-irradiation-induced alterations in colonic symptoms using a dextran sodium sulfate (DSS)-induced ulcerative colitis model mice. Both male and female institute of cancer research (ICR) mice were administered DSS (5%) ad libitum for 5 days while irradiated with 40 kJ/m² blue light daily. Additionally, tranexamic acid (TA) was administered daily throughout the study. Male mice treated with DSS/blue light exhibited exacerbated colitis compared to those treated with DSS alone. In contrast, female mice treated with DSS/blue light exhibited enhanced symptoms compared to those treated with DSS alone. Additionally, in male mice exposed to blue light, the clock/brain and muscle Arndt-like 1 (Bma1)/noradrenaline/macrophage or beta2-adrenergic receptor (β2-AR) pathways were activated. In female mice, the Bmal1/acetylcholine/macrophage/nicotinic acetylcholine receptor alpha 7 (α7nAChR) pathway was activated. These findings highlight sex differences in the effects of blue light on DSS-induced ulcerative colitis. Moreover, the worsening of symptoms in males was ameliorated through TA administration. Full article

This study investigates user experience (UX) sharing behaviors in the context of smartphone user interface (UI) design, emphasizing their significance for UI enhancement and effective marketing strategies. Grounded in the Stimulus–Organism–Response (SOR) framework, we examine how design perception attributes—perceived usability, novelty, enjoyment, and brand image—influence UX sharing, with a spotlight on the mediating role of individual motivation. A quantitative analysis (N = 472), Structural Equation Modeling (SEM), and mediation analysis were conducted. Our findings confirm that these components can positively impact UX sharing by bolstering personal expectations and self-efficacy in knowledge sharing, with perceived usability being an exception as it unexpectedly showed a negative association with sharing frequency. Moreover, perceived brand image and individual self-expectancy and self-efficacy enhance sharing outcomes. This research enriches our understanding of the strategic importance of user interface (UI) design in the context of smartphones, furnishing empirical grounding for devising sustainable UI design strategies and productive marketing tactics. Consequently, it bears considerable relevance to both theoretical insights and practical applications. Full article

A low-cost, handheld centrifugal microfluidic system for multiplexed visual detection based on recombinase polymerase amplification (RPA) was developed. A concise centrifugal microfluidic chip featuring four reaction units was developed to run multiplexed RPA amplification in parallel. Additionally, a significantly shrunk-size and cost-effective handheld companion device was developed, incorporating heating, optical, rotation, and sensing modules, to perform multiplexed amplification and visual detection. After one-time sample loading, the metered sample was equally distributed into four separate reactors with high-speed centrifugation. Non-contact heating was adopted for isothermal amplification. A tiny DC motor on top of the chip was used to drive steel beads inside reactors for active mixing. Another small DC motor, which was controlled by an elaborate locking strategy based on magnetic sensing, was adopted for centrifugation and positioning. Visual fluorescence detection was optimized from different sides, including material, surface properties, excitation light, and optical filters. With fluorescence intensity-based visual detection, the detection results could be directly observed through the eyes or with a smartphone. As a proof of concept, the handheld device could detect multiple targets, e.g., different genes of African swine fever virus (ASFV) with the comparable LOD (limit of detection) of 75 copies/test compared to the tube-based RPA. Full article

Roads are the most heavily affected aspect of urban infrastructure given the ever-increasing number of vehicles needed to provide mobility to residents, supply them with goods, and help sustain urban growth. An important indicator of degrading road infrastructure is the so-called bump features of the road surface (BFRS), which have affected transportation safety and driving experience. To collect BFRS, we can collect discrete-sampled, non-homogeneous multi-sensor stream data. We propose a BFRS detection method based on spectrum modeling and multi-dimensional features. With the sampling rate of GPS at 1 Hz and a gyroscope and accelerometer at 100 Hz, multi-sensor stream data are recorded at three different urban areas of Nanjing, China, using the smartphone mounted on a vehicle. The recorded stream data captures a geometric feature modeling movement and the respective driving conditions. Derived features also include acceleration, orientation, and speed information. To capture bump features, we develop a deep-learning-based approach based on so-called spectrum features. BFRS detection experiments using multi-sensor stream data from smartphones are conducted, and 4, 14, and 17 BFRS are correctly detected in three different areas, with the precision as 100%, 70.00%, and 77.27%, respectively. Then, comparisons are conducted between the proposed method and three other methods, and the F-score of the proposed method is computed as 1.0000, 0.6363, and 0.7555 at three different areas, which hold the highest value among all results. Finally, it shows that the proposed method performs well in different geographic areas. Full article

Color distortion in an image presents a challenge for machine learning classification and regression when the input data consists of pictures. As a result, a new algorithm for color standardization of photos is proposed, forming the foundation for a deep neural network regression model. This approach utilizes a self-designed color template that was developed based on an initial series of studies and digital imaging. Using the equalized histogram of the R, G, B channels of the digital template and its photo, a color mapping strategy was computed. By applying this approach, the histograms were adjusted and the colors of photos taken with a smartphone were standardized. The proposed algorithm was developed for a series of images where the entire surface roughly maintained a uniform color and the differences in color between the photographs of individual objects were minor. This optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for the deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the vitamin C concentration range from 0 to 87.72 µg·mL⁻¹, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL⁻¹, in comparison to the non-standardized variant, where this indicator was at the level of 1.48–2.29 µg·mL⁻¹. The consistency of the predicted concentration results with actual data, expressed as R², ranged between 0.9956 and 0.9999 for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surfaces of solutions, which is a common problem in liquid samples. This color-matching algorithm has universal character, and its scope of application is not limited. Full article

Traditional leaf chlorophyll estimation using Soil Plant Analysis Development (SPAD) devices and spectrophotometers is a high-cost mechanism in agriculture. Recently, research on chlorophyll estimation using leaf camera images and machine learning has been seen. However, these techniques use self-defined image color combinations where the system performance varies, and the potential utility has not been well explored. This paper proposes a new method that combines an improved contact imaging technique, the images’ original color parameters, and a 1-D Convolutional Neural Network (CNN) specifically for tea leaves’ chlorophyll estimation. This method utilizes a smartphone and flashlight to capture tea leaf contact images at multiple locations on the front and backside of the leaves. It extracts 12 different original color features, such as the mean of RGB, the standard deviation of RGB and HSV, kurtosis, skewness, and variance from images for 1-D CNN input. We captured 15,000 contact images of tea leaves, collected from different tea gardens across Assam, India to create a dataset. SPAD chlorophyll measurements of the leaves are included as true values. Other models based on Linear Regression (LR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and K-Nearest Neighbor (KNN) were also trained, evaluated, and tested. The 1-D CNN outperformed them with a Mean Absolute Error (MAE) of 2.96, Mean Square Error (MSE) of 15.4, Root Mean Square Error (RMSE) of 3.92, and Coefficient of Regression (R2) of 0.82. These results show that the method is a digital replication of the traditional method, while also being non-destructive, affordable, less prone to performance variations, and simple to utilize for sustainable agriculture. Full article

Functional mobility tests, such as the L test of functional mobility, are recommended to provide clinicians with information regarding the mobility progress of lower-limb amputees. Smartphone inertial sensors have been used to perform subtask segmentation on functional mobility tests, providing further clinically useful measures such as fall risk. However, L test subtask segmentation rule-based algorithms developed for able-bodied individuals have not produced sufficiently acceptable results when tested with lower-limb amputee data. In this paper, a random forest machine learning model was trained to segment subtasks of the L test for application to lower-limb amputees. The model was trained with 105 trials completed by able-bodied participants and 25 trials completed by lower-limb amputee participants and tested using a leave-one-out method with lower-limb amputees. This algorithm successfully classified subtasks within a one-foot strike for most lower-limb amputee participants. The algorithm produced acceptable results to enhance clinician understanding of a person’s mobility status (>85% accuracy, >75% sensitivity, >95% specificity). Full article

Older people often report functional limitations and low digital skills, with the latter hampering the use of Information and Communication Technologies (ICTs) and having potentially negative consequences on their social isolation and loneliness. Against this background, we present some findings from the “Inclusive ageing in place” (IN-AGE) study, carried out in 2019 in Italy. This study explored seniors’ abilities and difficulties with the independent use of mobile phones, smartphones, and tablets/personal computers (PCs). Qualitative/semi-structured interviews involved 120 seniors aged 65 years and over, living alone in three Italian regions (Lombardy, Marche, and Calabria). Purposive sampling was conducted, and quantitative/qualitative analyses were performed. The main results showed that smartphones and PCs/tablets were used by older respondents living mainly in the north and in urban sites to talk with family members and less for other functionalities (e.g., internet). Those more educated and without serious functional limitations were more capable of utilising ICTs. Seniors using ICTs reported mainly low/moderate loneliness and less social isolation. Therefore, technological tools have the potential to mitigate both, even though some barriers (e.g., poor health, low education) can hinder this opportunity. These results can offer insights for policymakers to design adequate policies (e.g., e-training programs) for seniors, to facilitate their inclusion in digital society, thus enabling social sustainability in an ageing population. Full article

The comparative evaluation of the performance of a mobile device camera and an affordable full-frame mirrorless camera in close-range photogrammetry applications involves assessing the capabilities of these two types of cameras in capturing images for 3D measurement purposes. In this study, experiments are conducted to compare the distortion levels, the accuracy performance, and the image quality of a mobile device camera against a full-frame mirrorless camera when used in close-range photogrammetry applications in various settings. Analytical methodologies and specialized digital tools are used to evaluate the results. In the end, generalized conclusions are drawn for using each technology in close-range photogrammetry applications. Full article

This study presents PlanteSaine, a novel mobile application powered by Artificial Intelligence (AI) models explicitly designed for maize, tomato, and onion farmers in Burkina Faso. Agriculture in Burkina Faso, like many developing nations, faces substantial challenges from plant pests and diseases, posing threats to both food security and economic stability. PlanteSaine addresses these challenges by offering a comprehensive solution that provides farmers with real-time identification of pests and diseases. Farmers capture images of affected plants with their smartphones, and PlanteSaine’s AI system analyzes these images to provide accurate diagnoses. The application’s offline functionality ensures accessibility even in remote areas with limited Internet connectivity, while its messaging feature facilitates communication with agricultural authorities for guidance and support. Additionally, PlanteSaine includes an emergency alert mechanism to notify farmers about pest and disease outbreaks, enhancing their preparedness to deal with these threats. An AI-driven framework, featuring an image feature extraction phase with EfficientNetB3 and an artificial neural network (ANN) classifier, was developed and integrated into PlanteSaine. The evaluation of PlanteSaine demonstrates its superior performance compared to baseline models, showcasing its effectiveness in accurately detecting diseases and pests across maize, tomato, and onion crops. Overall, this study highlights the potential of PlanteSaine to revolutionize agricultural technology in Burkina Faso and beyond. Leveraging AI and mobile computing, PlanteSaine provides farmers with accessible and reliable pest and disease management tools, ultimately contributing to sustainable farming practices and enhancing food security. The success of PlanteSaine underscores the importance of interdisciplinary approaches in addressing pressing challenges in global agriculture Full article

This paper proposes a broadband eight-antenna array design suitable for Fifth Generation New Radio (5G NR) smartphone applications. To cover the 5G NR bands n77/n78/n79 (3300–5000 MHz) and 5G NR-U n46 band (5150–5925 MHz), the single antenna array unit applied is a modified loop antenna element (MLAE) that can generate three different loop modes. To yield good multi-input multi-output (MIMO) performances, the designed MLAE is further arranged as an eight-antenna array, and the experimental results show that the overlapping 6 dB bandwidth can cover the bands-of-interest (3300–5925 MHz) with good isolation and total efficiency of >10 dB and 51–84%, respectively. Finally, good MIMO performances, such as an envelope correlation coefficient (ECC) of lower than 0.1 and desirable channel capacity (CC) of 37–40 bps/Hz, were calculated across the bands-of-interest. Full article

Exercise therapy is the first-line intervention recommended for those with chronic musculoskeletal pain (CMP). Smartphone technologies (mHealth) represent a feasible means for exercise prescription and individualization. This systematic review with meta-analysis aimed to identify factors associated with changes in pain and function following mHealth-based exercise therapy in patients with CMP. CINAHL (via EBSCOhost), Embase, PubMed, Scopus, and SPORTdiscus were searched from inception to February 2023. Observational and controlled clinical trials with correlation or regression analysis of factors associated with the effect of mHealth exercise interventions on pain and function were included. The risk of bias, completeness of interventions, spin of information, and certainty in the evidence were evaluated. Eight studies with 51,755 participants were included. Reduced pain intensity after intervention was associated with higher physical function: r (95% CI) = −0.55 (−0.67 to −0.41); I² = 86%, Tau² = 0.02; p < 0.01. Meta-regression identified the Body Mass Index (BMI), exercise dose, and completion rate as potential moderators between changes in pain and physical function following mHealth exercise therapy. No association was found between pain and anxiety: r (95% CI) = 0.15 (−0.08 to 0.37); I² = 87%, Tau² = 0.02; p = 0.19. Very low certainty in the evidence was observed due to serious concerns regarding the risk of bias, inconsistency, and indirectness. The limited available evidence detracts from the clinical interpretation of the findings. Full article