Search Results (1,933)

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis. This equipment can minimize the risk of serious accidents or severe health hazards. The objective of the present research project is to design an armband with an accurate location tracking system. This is of particular importance for individuals with dementia and Alzheimer’s disease, who frequently leave their homes and are unable to find their way back. The proposed armband also includes a fingerprint identification system that allows only authorized personnel to use it. Furthermore, in hospitals and healthcare facilities the biometric identification system can be used to trace periodic medical or nursing visits. This process improves the reliability and transparency of healthcare. The test results indicate that the armband functions in accordance with the desired design specifications, with performance evaluation of the main features including fall detection, where a hit rate of 100% was obtained, a fingerprint recognition test demonstrating accuracy from 88% to 100% on high-quality samples, and a GPS tracking test determining position with a difference of between 1.8 and 2.1 m. The proposed solution may be of benefit to healthcare professionals, supported housing providers, elderly people as target users, or their family members. Full article

As an innovative branch of electronics, intelligent electronic textiles (e-textiles) have broad prospects in applications such as e-skin, human–computer interaction, and smart homes. However, it is still a challenge to distinguish multiple stimuli in the same e-textile. Herein, we propose a dual-parameter smart e-textile that can detect human pulse and body temperature in real time, with high performance and no signal interference. The doping of SWCNTs in PEDOT:PSS improves the electrical conductivity and Seebeck coefficient of the prepared composites, which results in excellent pressure and temperature-sensing properties of the PEDOT:PSS/SWCNTs/CS@PET-textile (PSCP) sensor. The dual-mode sensor has high sensitivity (32.4 kPa⁻¹), fast response time (~21 ms), and excellent durability (>2000 times) in pressure detection. Concurrently, this sensor maintains a high Seebeck coefficient of 25 μV/K in the 0–120 K temperature range with a tremendous linear relationship. Based on impressive dual-mode sensing characteristics and independent temperature-difference- and pressure-sensing mechanisms, smart e-textile sensors realize the real-time simultaneous monitoring of weak pulse signals and human body temperature, showing great potential in medical healthcare. In addition, the potential energy is excited by the temperature gradient between the human skin and the environment, which provides a novel idea for wearable self-powered devices. Full article

Background/Objectives: Gait retraining is widely used in orthopedic rehabilitation to address abnormal movement patterns. However, retaining walking modifications can be challenging without guidance from physical therapists. Real-time auditory biofeedback can help patients learn and maintain gait alterations. This study piloted the feasibility of the musification of feedback to medialize the center of pressure (COP). Methods: To provide musical feedback, COP and plantar pressure were captured in real time at 100 Hz from a wireless 16-sensor pressure insole. Twenty healthy subjects (29 ± 5 years old, 75.9 ± 10.5 Kg, 1.73 ± 0.07 m) were recruited to walk using this system and were further analyzed via marker-based motion capture. A lowpass filter muffled a pre-selected music playlist when the real-time center of pressure exceeded a predetermined lateral threshold. The only instruction participants received was to adjust their walking to avoid the muffling of the music. Results: All participants significantly medialized their COP (−9.38% ± 4.37, range −2.3% to −19%), guided solely by musical feedback. Participants were still able to reproduce this new walking pattern when the musical feedback was removed. Importantly, no significant changes in cadence or walking speed were observed. The results from a survey showed that subjects enjoyed using the system and suggested that they would adopt such a system for rehabilitation. Conclusions: This study highlights the potential of musical feedback for orthopedic rehabilitation. In the future, a portable system will allow patients to train at home, while clinicians could track their progress remotely through cloud-enabled telemetric health data monitoring. Full article

Generally, the electrocardiography (ECG) system plays an important role in preventing and diagnosing heart diseases. To further improve the amenity and convenience of using an ECG system, we built a customized capacitive electrocardiography (cECG) system with one wet electrode, sixteen non-contact electrodes, two ADS1299 chips, and one STM32F303-based microcontroller unit (MCU). This new cECG system could acquire, save, and display the ECG data in real time. The bias feedback as a critical technique was routed to the left hand with the wet Ag/AgCl electrode, which could greatly suppress the power line noise. After all artifacts were removed, the ECG signals could be discerned clearly. We demonstrated that the ECG signals acquired with the capacitive electrodes were similar to those with the wet electrode. Thus, we successfully provide a new configuration for cECG monitoring at home or in a clinical setting. Full article

Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labeled training data, we propose to use prototypical networks, which can embed domain expertise about the heterogeneous tremor and non-tremor sub-classes. We evaluated our approach using data from the Parkinson@Home Validation study, including 8 PD patients with tremor, 16 PD patients without tremor, and 24 age-matched controls. We used wrist accelerometer data and synchronous expert video annotations for the presence of tremor, captured during unscripted daily life activities in and around the participants’ own homes. Based on leave-one-subject-out cross-validation, we demonstrate the ability of prototypical networks to capture free-living tremor episodes. Specifically, we demonstrate that prototypical networks can be used to enforce robust performance across domain-informed sub-classes, including different tremor phenotypes and daily life activities. Full article

Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring. Full article

One approach to improve long-term coral restoration success utilizes naturally stress-tolerant corals from the wild. While the focus has primarily been on thermal stress, low oxygen is a growing threat to coral reefs and restoration efforts should also consider hypoxia tolerance. Here we determine if Siderastrea siderea and Agaricia tenuifolia populations from a reef with a historical record of low oxygen exhibit evidence of local adaptation to hypoxic events, compared to populations from a reference reef. We employed a laboratory-based reciprocal transplant experiment mimicking a severe 14-night hypoxic event and monitored bleaching responses, photo-physiology, metabolic rates, and survival of all four populations during, and for two weeks following the event. In both species, we found the populations from the hypoxic reef either fully persisted or recovered within 3 days of the event. In contrast, the conspecific naïve populations from the well-oxygenated reference reef experienced bleaching and death. This showcases the vulnerability of naïve corals exposed to low oxygen but also suggests that corals from the hypoxic reef locally adapted to survive severe episodic hypoxia. Other reefs with past episodic low oxygen may also be home to corals with adaptation signatures to hypoxia and may be useful for restoration efforts. Full article

There are several systems for patient care, including elderly healthcare, which rely on sensor data acquisition and analysis. These sensors are typical vital-monitoring sensors and are coupled with Artificial Intelligence (AI) models to quickly analyze emergency situations or even predict them. These systems are deployed in hospitals and require expensive monitoring and analysis equipment. Eldercare specifically encompasses monitoring, smart analysis, and even the emotional aspects of care. Existing systems do not provide a portable, easy-to-use system for at-home eldercare. Further, existing systems do not address advanced analysis capabilities around mood/sentiment/mental state/mental disorder analysis or the analysis of issues around sleep disorders, apnea, etc., based on sound capture and analysis. Also, existing systems disregard the emotional needs of elderly patients, which are a critical aspect of patient wellbeing. A low-cost and effective solution is therefore required for extended use in eldercare. In this paper, the CareTaker.ai system is proposed to address the shortcomings of the existing systems and build a comprehensive caretaker assistant using sensors, audio, video, and AI. It consists of smart bed sheets, pillow covers with embedded sensors, and a processing unit with GPUs, conversational AI, and generative AI capabilities, with associated functional modules. Compared to existing systems, the proposed system has advanced monitoring and analysis capabilities with potential for low-cost mass manufacturing and a widespread commercial application. Full article

Smart homes offer promising opportunities for risk prevention in private households, especially concerning safety and health. For instance, they can reduce safety risks by detecting water leakages quickly and support health by monitoring air quality. Current research on smart home technology predominantly focuses on usability, performance expectations, and cyber risks, overlooking the potential importance of risk prevention benefits to prospective users. We address this gap by utilizing data from a recent survey to construct a structural equation model. Our overarching hypothesis is that prevention benefits and comfort considerations positively influence adoption. The results confirm the relevance of comfort, as suggested by previous research. In addition, the results reveal significant prevention benefits in safety and health, which are positively related to technology expectations and the intention to adopt smart homes. Furthermore, newly included variables such as technology affinity and active aging lifestyle emerge as indicators of potential smart home users, extending the knowledge of user characteristics beyond traditional sociodemographic indicators. The findings contribute to filling a gap in the current risk and technology literature and are also relevant for smart home device manufacturers and risk and insurance practitioners looking to evolve their business models. Full article

WiFi-based human sensing has emerged as a transformative technology for advancing sustainable living environments and promoting well-being by enabling non-intrusive and device-free monitoring of human behaviors. This offers significant potential in applications such as smart homes and sustainable urban spaces and healthcare systems that enhance well-being and patient monitoring. However, current research predominantly addresses single-user scenarios, limiting its applicability in multi-user environments. In this work, we introduce “MultiSenseX”, a cutting-edge system leveraging a multi-label, multi-view Transformer-based architecture to achieve simultaneous localization and activity recognition in multi-occupant settings. By employing advanced preprocessing techniques and utilizing the Transformer’s self-attention mechanism, MultiSenseX effectively learns complex patterns of human activity and location from Channel State Information (CSI) data. This approach transcends traditional sequential methods, enabling accurate and real-time analysis in dynamic, multi-user contexts. Our empirical evaluation demonstrates MultiSenseX’s superior performance in both localization and activity recognition tasks, achieving remarkable accuracy and scalability. By enhancing multi-user sensing technologies, MultiSenseX supports the development of intelligent, efficient, and sustainable communities, contributing to SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) through safer, smarter, and more inclusive urban living solutions. Full article

This study examines the psychological and social impacts of the April 2024 Iranian combined attack on Israel—a new, globally unprecedented experience for civilians. Aware of incoming missiles and drones, Israelis followed real-time television coverage, including countdowns and visual simulations, which allowed them to anticipate the impacts of potential strikes on their homes and communities. The attack and its coverage blurred the boundaries between crisis and media spectacle, creating a rare convergence of immediate personal threat with real-time media framing. This paper explores how this unique format influenced public anxiety, news consumption, and crisis perception. The results reveal the profound psychological effects of this real-time threat monitoring, raising important questions about the media’s impact on framing crises such as live events and the corresponding effects on public mental health. Full article

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties. Full article

Radon is a naturally occurring radioactive gas that causes lung cancer. It has been measured extensively in homes and mines but research in other workplaces has been limited. The present study examined 453 workplaces in Ontario, Canada, to characterize radon levels. Radon monitors (n = 687) were placed in occupied ground floor and basement workplace locations for a minimum of three months. The radon measurements ranged from <4 to 566 Bq/m³, with a median of 26 Bq/m³, arithmetic mean of 40.2 Bq/m³, and geometric mean of 26.9 Bq/m³. Using the Health Canada and Ontario labor guideline of 200 Bq/m³, 2.5% of participating workplaces had at least one measurement above this level; 7.2% were above the World Health Organization guideline. Workplaces were also asked to fill out questionnaires to identify possible determinants of exposure. Radon levels varied significantly based on municipality and background radon zone, highlighting the importance of geography in influencing radon levels. Radon levels also varied significantly based on window-opening behavior, business access type, the presence of an elevator, air conditioning, additions to the building, and cracks and/or gaps in the foundation/wall and around drains, indicating building characteristics with some influence on air circulation may impact overall radon levels. Full article

Objective: This study aimed to investigate the utilization of patterns derived from physical activity monitoring (PAM) for the identification of individuals at risk of type 2 diabetes mellitus (T2DM) through an at-home screening approach employing machine learning techniques. Methods: Data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) were scrutinized, focusing on the PAM component. The primary objective involved the identification of diabetes, characterized by an HbA1c ≥ 6.5% (48 mmol/mol), while the secondary objective included individuals with prediabetes, defined by an HbA1c ≥ 5.7% (39 mmol/mol). Features derived from PAM, along with age, were utilized as inputs for an XGBoost classification model. SHapley Additive exPlanations (SHAP) was employed to enhance the interpretability of the models. Results: The study included 7532 subjects with both PAM and HbA1c data. The model, which solely included PAM features, had a test dataset ROC-AUC of 0.74 (95% CI = 0.72–0.76). When integrating the PAM features with age, the model’s ROC-AUC increased to 0.79 (95% CI = 0.78–0.80) in the test dataset. When addressing the secondary target of prediabetes, the XGBoost model exhibited a test dataset ROC-AUC of 0.80 [95% CI; 0.79–0.81]. Conclusions: The objective quantification of physical activity through PAM yields valuable information that can be employed in the identification of individuals with undiagnosed diabetes and prediabetes. Full article

The paper presents an innovative virtual reality (VR)-based environment for personalized telerehabilitation programs. This environment integrates a parallel robotic structure designed for the lower limb rehabilitation of patients with neuromotor disabilities and a virtual patient. The robotic structure is controlled via a user interface (UI) that communicates with the VR environment via the TCP/IP protocol. The robotic structure can also be operated using two controllers that communicate with a VR headset via the Bluetooth protocol. Through these two controllers, the therapist demonstrates to the patient various exercises that the robotic system can perform. With the right-hand controller, the therapist guides exercises for the hip and knee, while the left-hand controller manages ankle exercises. The therapist remotely designs a rehabilitation plan for patients at home, defining exercises, interacting with the rehabilitation robot in real-time via the VR headset and the two controllers, and initiating therapy sessions. The user interface allows monitoring of patient progress through video feedback, electromyography (EMG) sensors, and session recording. Full article