Search Results (130)

Aim: Brain training games offer a promising avenue for promoting cognitive engagement and healthy aging among older adults. However, little is known about how design features align with the specific needs of this demographic to promote sustained usage and thereby cognitive intervention. The aim of this study was to characterize how all aspects of the game design and player experience might influence adherence mechanisms, and assess the feasibility and acceptability of a wearable brain-activity measuring device. Methods: We use an exploratory mixed-methods approach with n = 6 community-dwelling older adults (mean age 68 ± 3.94) within a smart-home-style Living-Lab. Participants played two commercially available brain-training games. One of the games uses a wearable brain-activity measuring device. We collected System Usability Scale (SUS) and User Experience Questionnaire (UEQ) scores and conducted focus-group interviews and structured observations. We performed a qualitative theory-informed analysis through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Results: Participants reported high motivation to play brain-training games for dementia prevention. They preferred large, high-contrast text, intuitive navigation, touch-based controls, and a relaxed pacing. The wearable device was acceptable and comfortable for home use. There were requests for a clearer meaning of brain activity scores and the integration of personalized brain data with other health apps and broader health metrics. Quantitative scales (SUS and UEQ) showed similar ratings for both games, with both meeting the threshold for acceptability. Conclusions: In this formative study, concrete design features that plausibly increase engagement, persistence and adherence were identified, alongside evidence for the feasibility of integrating a wearable brain-sensor. Our findings motivate a follow-on trial testing whether an adherence-optimized design increases the training dose and downstream cognitive outcomes. Full article

Introduction: Step counts are increasingly used to assess mobility and track recovery following total knee arthroplasty (TKA). The purpose of this study was to assess the convergent validity of step count data captured by a smart implantable device (SID) in comparison with step counts derived from established, validated sensor-based technology. Methods: A secondary analysis of an anonymized commercial database (N = 7861, median age: 68, female: 59%, median BMI: 31.7) of patients who received an SID and used a digital care management application (App) with or without a smart watch. The SID recorded “qualified steps”, defined as periods of walking for at least seven steps that met predefined acceleration and cadence thresholds between 7 am and 10 pm. The App collected total daily step counts via smartwatch and/or smartphone. Pearson correlations were calculated between SID and App data at 30, 90, and 180 days post-operative. Step counts at 30, 90, and 180 days post-operative were compared between groups with the Mann–Whitney U test. Statistical significance was assessed at p < 0.001. Results: Step counts increased throughout the recovery period as measured by all three devices. SID-captured fewer qualified steps than App-captured step counts from watch-wearers throughout the post-operative period (p ≤ 0.001). SID step counts were similar to App step counts at 30 days post-operative and greater than App step counts at 90 and 180 days post-operative (p < 0.001). There were significant (p < 0.001), moderate correlations (r = 0.62 to r = 0.74) between step counts collected by the SID and App for both watch-wearers and smartphone-carriers at 30, 90, and 180 days post-operative. Conclusions: The SID’s qualified step metric demonstrated consistent, moderate, correlations with app-based step counts across 30, 90, and 180 days. While smartwatch-based tools recorded higher absolute step counts, both technologies reflected similar recovery trajectories. Full article

Gun violence in U.S. schools not only causes loss of life and physical injury but also leaves enduring psychological trauma, damages property, and results in significant economic losses. One way to reduce this loss is to detect the gun early, notify the police as soon as possible, and implement lockdown procedures immediately. In this project, a novel gun detector Internet of Things (IoT) system is developed that automatically detects the presence of a gun either from images or from gunshot sounds, and sends notifications with exact location information to the first responder’s smartphones using the Internet within a second. The device also sends wireless commands using Message Queuing Telemetry Transport (MQTT) protocol to close the smart door locks in classrooms and announce to act using public address (PA) system automatically. The proposed system will remove the burden of manually calling the police and implementing the lockdown procedure during such traumatic situations. Police will arrive sooner, and thus it will help to stop the shooter early, the injured people can be taken to the hospital quickly, and more lives can be saved. Two custom deep learning AI models are used: (a) to detect guns from image data having an accuracy of 94.6%, and (b) the gunshot sounds from audio data having an accuracy of 99%. No single gun detector device is available in the literature that can detect guns from both image and audio data, implement lockdown and make PA announcement automatically. A prototype of the proposed gunshot detector IoT system, and a smartphone app is developed, and tested with gun replicas and blank guns in real-time. Full article

The accelerated development of smart devices and the increased demand for technological services have given rise to new services with great potential for development in the market. Applications for museums are no exception, and more and more institutions are including such solutions in the cultural industry. However, there is still much to be developed, given the difficulties that people with disabilities have in accessing them. In this work were studied the characteristics that the future application (App) of the Helga de Alvear Museum in Cáceres should have so that it can be used satisfactorily by the maximum number of visitors, regardless of their sensory, intellectual, or motor capacity. Kansei Engineering has identified the emotions and sensations that favour the interaction of users with the application and which have been converted into functionalities and design requirements in order to present a graphic proposal and structure for the App. The appearance and functioning of this App are presented visually, supported by an initial theoretical and research part that has helped to identify the rest of the specific objectives. Some specifications to take into account are functional, non-functional, programming, sequence diagrams, and basic interface requirements. This application has two generic and five specific itineraries to solve the disabilities mentioned in this paper, making it accessible to the different groups. The importance of obtaining an equivalence between the essential requirements of the standard and the basic design specifications that should regulate the work process resides not only in having a direct equivalence but also in obtaining guidelines for other designers who want to face extensive regulation and need help to interpret it and be able to make decisions straightforwardly. Full article

Smart assistive technologies such as sensor-based footwear and walking aids offer promising opportunities for gait rehabilitation through real-time feedback and patient-centered monitoring. While biofeedback applications show great potential, current research rarely explores integrated closed-loop systems with device- and modality-specific feedback. In this work, we present a modular sensor-based system combining a smart foot orthosis and an instrumented forearm crutch to deliver real-time vibrotactile biofeedback. The system integrates plantar pressure and motion sensing, vibrotactile feedback, and wireless communication via a smartphone application. We conducted a user study with eight participants to validate the system’s feasibility for mobile gait detection and app usability, and to evaluate different vibrotactile feedback types across the orthosis and forearm crutch. The results indicate that pattern-based vibrotactile feedback was rated as more useful and suitable for regular use than simple vibration alerts. Moreover, participants reported clear perceptual differences between feedback delivered via the orthosis and the forearm crutch, indicating device-dependent feedback perception. The findings highlight the relevance of feedback strategy design beyond hardware implementation and inform the development of user-centered haptic biofeedback systems. Full article

This study is to explore information synthesis on research topics and emerging trends in privacy within the context of technology adoption. A search for the terms privacy and technology adoption in the Web of Science database yielded information on 2910 publications from 2005 to 2025. The analysis was conducted using CiteSpace, incorporating cluster analysis, timeline analysis, and burst detection to identify key patterns and developments. Fifteen sub-areas of privacy related to technology adoption were identified, including health information exchange, blockchain adoption, artificial intelligence, Internet banking, smart home devices, location-based services, mobile commerce, ubiquitous commerce adoption, tracing apps, metaverse adoption, and facial recognition payment. Timeline analysis provided insights into the growth or decline of these research clusters over time. Based on the findings, a framework was developed to illustrate key insights and their interconnections, offering guidance for future research. The study concludes by discussing its implications, limitations, and recommendations for further research. Full article

Today, healthcare systems face many challenges due to the increasing number of elderly people and the complex needs of patients with multiple diseases. Previous research has shown that assistive technologies (ATs), like wearable devices, mobile health (mHealth) apps, and smart monitoring systems, can help improve patient care and make healthcare services more efficient. However, many of these studies do not focus so much on hospitals and do not clearly show the effects on clinical outcomes. In this study, the authors conducted a bibliometric analysis using the Scopus database to determine how much research has been carried out on assistive technologies in hospitals, especially for patient profiling and treatment. The authors chose articles from the last 20 years using specific inclusion and exclusion criteria, and VOSviewer software (version 1.6.20) was used to study keywords, co-authorship, and citation networks to find research trends and missing areas. The results show that even if assistive technologies are growing fast, there are not many studies that focus on hospitals or on important outcomes like quality of care and treatment results. Most of the research is in computer science and engineering, and many keywords for hospital use are not common. This study discusses how assistive technologies can help change healthcare and also shows the current problems, like system integration, data privacy, cost, and whether users accept the technologies. The authors suggest that future research must look at personal solutions, international standards, and better cooperation between doctors, engineers, and policymakers. Full article

Alzheimer’s disease is a growing global health concern, demanding innovative solutions for early detection, continuous monitoring, and patient support. This article reviews recent advances in Smart Wearable Medical Devices (SWMDs), Internet of Things (IoT) systems, and mobile applications used to monitor physiological, behavioral, and cognitive changes in Alzheimer’s patients. We highlight the role of wearable sensors in detecting vital signs, falls, and geolocation data, alongside IoT architectures that enable real-time alerts and remote caregiver access. Building on these technologies, we present LumiCare, a conceptual, context-aware mobile system that integrates multimodal sensor data, chatbot-based interaction, and emerging 6G network capabilities. LumiCare uses machine learning for behavioral analysis, delivers personalized cognitive prompts, and enables emergency response through adaptive alerts and caregiver notifications. The system includes the LumiCare Companion, an interactive mobile app designed to support daily routines, cognitive engagement, and safety monitoring. By combining local AI processing with scalable edge-cloud architectures, LumiCare balances latency, privacy, and computational load. While promising, this work remains at the design stage and has not yet undergone clinical validation. Our analysis underscores the potential of wearable, IoT, and mobile technologies to improve the quality of life for Alzheimer’s patients, support caregivers, and reduce healthcare burdens. Full article

Optical sensors have emerged as a popular technology for sensing biological and chemical analytes in various fields, including environmental monitoring, toxicology, disease/infection screening, and food processing, due to their ease of use, high sensitivity, and specificity. In this study, we introduce ColorX, an ultra-portable and smart spectrophotometric device based on a commercially available fitness tracker. ColorX exploits the in-built LEDs and photodiodes of a fitness tracker for wavelength-specific absorption measurements and can be controlled wirelessly using a companion smartphone app. The device’s raw data are transmitted via Bluetooth and stored on the app for analysis and data visualisation. We validated the performance of ColorX against a standard benchtop spectrophotometer by experimentally testing five different measurements related to water quality: nitrite (>0.07 mg/L, %avgCV: 1.06)), sulphate (>18 mg/L, %avgCV: 0.39), chromium (>0.002 mg/L, %avgCV: 0.51), free chlorine (>0.005 mg/L, %avgCV: 0.68), and turbidity (>2.97 NTU, %avgCV: 1.04). Our results showed that ColorX had comparable performance to the benchmark spectrophotometer (R² values > 0.9 in all cases). Due to its ultra-portability, water-proof design, wireless control, and smartphone-aided data analysis, we believe ColorX will be highly beneficial for a wide range of on-field spectrophotometric applications. Our work demonstrates the potential of frugal science to develop affordable and accessible technology for optical sensing. Full article

In Korea, the Public Health Center Mobile Healthcare Project was implemented in 2016. This project utilizes Information and Communication Technology (ICT) and big data to establish a health-related service foundation and a healthcare service operation system. Equipment and methods: This study recruited 1261 adolescents (660 males (13.40 ± 1.14 years, 156.12 ± 10.59 cm) and 601 females (13.51 ± 1.23 years, 154.45 ± 7.48 cm)) from 22 public health centers nationwide. Smart bands were provided, and the ‘Future Health’ application (APP) was installed on personal smartphones to assess body composition, physical fitness, and physical activity. Results: A paired sample t-test revealed height, 20 m shuttle run, grip strength, and long jump scores significantly differed after 24 weeks in males. Females exhibited significant height, 20 m shuttle run, grip strength, sit-ups, and long jump differences. Moderate physical activity (MPA, p < 0.001), vigorous physical activity (VPA, p < 0.001), and moderate-to-vigorous physical activity (MVPA, p < 0.001) were significantly different after 24 weeks in adolescents. These results establish that an ICT-based health promotion service can provide adolescent students with individual information from a centralized organization to monitor health behaviors and receive feedback regardless of location in South Korea. Full article

Platform support for mobile collaboration among multiple smart devices has been an active research issues in the computing community. Using platform-level collaboration functionalities, a mobile device can share its resources, I/O events, and even apps easily with other devices, which enables developing a new kind of application that runs across multiple devices. In this work, we further extend the collaboration functionalities in mobile platforms by developing a novel platform service, remote intent service (RIS),which enables a running application in a device to outsource the execution of a specific task to another application in a remote device. Using the remote intent service, for example, we can view an attached document to an email, using a document viewer application in a remote device that has a larger screen, or conveniently browse an audio file that exists on another mobile device and play it locally. We implemented the remote intent service to the Android platform and measured the latency for executing such tasks in a remote device. The experimental results confirm that the remote intent service, for sending the intent plus retrieving the result, incurs an additional delay of less than 250 ms in total, and thus, it is practical. Full article

Accessible tools for post-stroke motor rehabilitation are critically needed to promote recovery beyond clinical settings. This pilot study evaluated the impact of a posture correction intervention using the Postural SmartVest, a wearable device that delivers multisensory feedback via a smartphone app. Forty individuals with post-stroke hemiparesis participated in a single supervised session, during which each patient completed the same four-phase functional protocol: multidirectional walking, free walking toward a refrigerator, an upper-limb reaching and object-handling task, and walking back to the starting point. Under the supervision of their therapists, each patient performed the full protocol twice—first without feedback and then with feedback—which allowed within-subject comparisons across multiple metrics, including upright posture duration, number and frequency of posture-related events, and temporal distribution. Additional analyses explored associations with demographic and clinical variables and identified predictors through regression models. Wilcoxon signed-rank and Mann–Whitney U tests showed significant improvements with feedback, including an increase in upright posture time ($p<0.001$), an increase in the frequency of upright posture events ($p<0.001$), and a decrease in the total task time ($p=0.038$). No significant subgroup differences were found for age, sex, lateralization, or stroke chronicity. Regression models did not identify significant predictors of improvement. Full article

There are over 300 million smart homes worldwide and 60.4 million smart homes in the US, using devices like smart thermostats, smart plugs, smart door locks, etc. Yet in this age of smart and connected devices, we still use paper-based sticky notes on doors to display messages such as “Busy, do not disturb”, “In a Zoom meeting”, etc. In this project, a novel IoT-connected digital sticky note system was developed where the user can wirelessly send messages from a smartphone to a sticky note display. The sticky note displays can be hung on the doors of offices, hotels, homes, etc. The display could be updated with the user’s message sent from anywhere in the world. The key design challenge was to develop the display unit to consume as little power as possible to increase battery life. A prototype of the proposed system was developed comprising ultra-low-power sticky note display units consuming only 404 µA average current and having a battery life of more than six months, with a Wi-Fi-connected hub unit, an MQTT server, and a smartphone app for composing the message. Full article

The home is an ideal setting for long-term sleep monitoring. This review explores a range of home-based sleep monitoring technologies, including smartphone apps, smartwatches, and smart mattresses, to assess their accuracy, usability, limitations, and how well they integrate with existing healthcare systems. This review evaluates 21 smartphone apps, 16 smartwatches, and nine smart mattresses through systematic data collection from academic literature, manufacturer specifications, and independent studies. Devices were assessed based on sleep-tracking capabilities, physiological data collection, movement detection, environmental sensing, AI-driven analytics, and healthcare integration potential. Wearables provide the best balance of accuracy, affordability, and usability, making them the most suitable for general users and athletes. Smartphone apps are cost-effective but offer lower accuracy, making them more appropriate for casual sleep tracking rather than clinical applications. Smart mattresses, while providing passive and comfortable sleep tracking, are costlier and have limited clinical validation. This review offers essential insights for selecting the most appropriate home sleep monitoring technology. Future developments should focus on multi-sensor fusion, AI transparency, energy efficiency, and improved clinical validation to enhance reliability and healthcare applicability. As these technologies evolve, home sleep monitoring has the potential to bridge the gap between consumer-grade tracking and clinical diagnostics, making personalized sleep health insights more accessible and actionable. Full article

Alzheimer’s disease (AD) and Alzheimer’s Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline and is traditionally monitored through infrequent clinical tests. In this research, the Smart Driving System, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases, is presented. The system comprises a multi-modal biosensing array (MMS) and AI algorithms, including driving performance and driver’s biometrics, offering insights into a driver’s cognitive function. This publication is the first work reported towards the ultimate goal of developing the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies. Full article