Search Results (79)

Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems. Full article

Background/Objectives: Viral diseases remain a major threat to global public health, particularly during outbreaks when limited therapeutic resources must be rapidly and fairly distributed to large populations. Although Convalescent Plasma (CP) transfusion has shown clinical promise, existing allocation frameworks treat patient prioritization, donor selection, and validation as separate processes. This study proposes a credible, converged smart framework integrating multicriteria decision-making (MCDM) and regression-based validation within a telemedicine environment to enable transparent, data-driven CP allocation. Methods: The proposed framework consists of three stages: (i) Analytic Hierarchy Process (AHP) for weighting five clinically relevant biomarkers, (ii) dual prioritization of patients and donors using Order Preference by Similarity to Ideal Solution (TOPSIS) and Višekriterijumsko Kompromisno Rangiranje (VIKOR) with Group Decision-Making (GDM), and (iii) regression-based model selection to identify the most robust prioritization model. An external dataset of 80 patients and 80 donors was used for independent validation. Results: The external GDM AHP-VIKOR prediction model demonstrated strong predictive performance and internal consistency, with R² = 0.971, MSE = 0.0010, RMSE = 0.032, and MAE = 0.025. Correlation analysis confirmed biomarker behavior consistency and stability in ranking, thereby reinforcing the reliability of the prioritization outcomes. Conclusions: The proposed patient–donor matching framework is accurate, interpretable, and timely. This work presents an initial step toward realizing safe AI-enabled transfusion systems within telemedicine, supporting transparent and equitable CP allocation in future outbreak settings. Full article

The Internet of Things (IoT) has influenced the healthcare industry by enabling real-time monitoring, data-driven decision-making, and automation of medical activities. IoT in healthcare comprises a network of interconnected medical devices, sensors, and software systems that gather, analyse, and transmit patient data, enhancing the efficiency, accuracy, and accessibility of healthcare services. Despite its benefits, the deployment and impact of IoT in healthcare vary between countries due to differences in healthcare infrastructure, regulatory frameworks, and technical advancements. This review highlights how IoT technologies underpin the efficiency of EHR and HIE systems by enabling continuous data flow, interoperability, and real-time patient care. It also addresses the problems involved with IoT adoption, including data privacy concerns, interoperability issues, high implementation costs, and cybersecurity dangers. Additionally, the paper examines future trends in IoT healthcare, including 5G integration, AI-enhanced healthcare analytics, blockchain-based security solutions, and the creation of energy-efficient IoT medical equipment. Through an analysis of worldwide trends and obstacles, this research offers suggestions for policies, methods, and best practices to close the digital healthcare gap and make sure that healthcare solutions powered by the IoT are available, safe, and effective everywhere. Full article

Due to their limited mobility and vocal limitations, paralysed individuals frequently struggle with communication and health monitoring. This work introduces an Internet of Things (IoT)-based system that combines continuous health monitoring with a sensor-based smart glove to enhance patient care. The glove detects falls, sends emergency messages via hand gestures, and monitors vital indicators, including SpO₂, heart rate, and body temperature. The smart glove uses Arduino UNO (RoboCraze, Bengaluru, India) and ESP8266 (RoboCraze, Bengaluru, India) modules with MPU6050 (RoboCraze, Bengaluru, India), MAX30100 (RoboCraze, Bengaluru, India), LM35 (Bombay Electronics, Mumbai, India), and flex sensors for these functions. MPU6050 detects falls precisely, while MAX30100 and flex sensors measure gestures, SpO₂, heart rate, and body temperature. The flex sensor interprets hand motions as emergency alerts sent via Wi-Fi to a cloud platform for remote monitoring. The experimental results confirmed the superiority and validated the efficacy of the suggested module. Scalability, data logging, and real-time access are guaranteed by IoT integration. The actual test cases were predicted using a Support Vector Machine, achieving an average accuracy of 81.98%. The suggested module is affordable, non-invasive, easy to use, and appropriate for clinical and residential use. The system meets the essential needs of disabled people, enhancing both their quality of life and carer connectivity. Advanced machine learning for dynamic gesture detection and telemedicine integration is a potential future improvement. Full article

Background: Chronic Brain Injury (CBI) is a lifelong condition requiring continuous adaptation by patients, families, and healthcare professionals. Transitioning rehabilitation toward patient-centered and self-management approaches is essential, yet remains limited in Spain. Methods: We conducted a two-phase consensus study in collaboration with the Spanish Society of Physical Medicine and Rehabilitation (SERMEF) and the Spanish Federation of Brain Injury (FEDACE). In Phase 1, surveys were distributed to patients (214 invited; 95 complete responses, 44.4%) and physiatrists (256 invited; 106 valid responses, 41.4%) to capture perceptions of current rehabilitation practices, including tele-rehabilitation. Differences and convergences between groups were analyzed using a Synthetic Factor (F). In Phase 2, a panel of 21 experts applied a real-time eDelphi process (SmartDelphi) to assess the feasibility of proposed innovations, rated on a six-point Likert scale. Results: Patients and professionals showed both alignment and divergence in their views. Patients reported lower involvement of rehabilitation teams and expressed more reluctance toward replacing in-person care with telemedicine. However, both groups endorsed hybrid models and emphasized the importance of improved communication tools. Expert consensus prioritized feasible interventions such as online orthopedic renewal services, hybrid care models, and educational video resources, while less feasible options included informal communication platforms (e.g., WhatsApp) and bidirectional teleconsultations. Recommendations were consolidated into five domains: (R1) systemic involvement of rehabilitation teams in chronic care, (R2) patient and caregiver education, (R3) self-management support, (R4) communication tools, and (R5) socialization strategies. Conclusions: This study demonstrates the value of combining patient and professional perspectives through digital Delphi methods to co-design innovation strategies in CBI rehabilitation. Findings highlight the need to strengthen communication, provide structured education, and implement hybrid care models to advance patient-centered rehabilitation. The methodology itself fostered engagement and consensus, underscoring its potential as a tool for participatory healthcare planning. Full article

Background and objectives: Numerous studies and meta-analyses have established the efficacy of telemonitoring for blood pressure and other components of metabolic syndrome in improving disease management. Nevertheless, the adoption of telemonitoring technologies is often hindered by personal, technological, and systemic barriers. In Slovakia, where patient–physician contact rates are high, there is limited research on patients’ perspectives regarding telemedicine adoption for cardiovascular risk management. The objective of this study was to examine patients’ perspectives on and perceived obstacles to the use of telemonitoring for arterial hypertension and dyslipidemia in Slovakia. Methods: This cross-sectional, questionnaire-based survey targeted a cohort of 18,053 patients. The survey instrument was designed to gather data on several key areas: patient demographic characteristics, blood pressure measurement habits, the utilization of smart technologies, perceived benefits and barriers to telemonitoring, and patients’ knowledge of their lipid profiles and cardiovascular risk factors. Statistical analysis included chi-square tests, ANOVA, and effect size calculations with 95% confidence intervals (CI). Results: A total of 1787 patient responses (9.9%) were collected. Among the respondents, 67.4% (n = 1204) had arterial hypertension, while 7.9% (n = 95) were on non-pharmacological therapy. Only 21.2% (n = 255) of hypertensive patients measured their blood pressure daily, with a significantly higher proportion of men than women (28.6% vs. 12.7%, p = 0.011, Cohen’s d = 0.42). The most frequent users of blood pressure monitoring were in the 31–45 age group (p = 0.001, η² = 0.08). A total of 19.4% (n = 347) of respondents used wearable devices, and 6.3% (n = 113) used blood pressure monitors connected to an application. Smart technology use was significantly more common in the 31–45 age group (p = 0.01, Cramer’s V = 0.15). Moderate interest in telemedicine was expressed by 69.8% (n = 1247) of respondents, though only 27.4% (n = 490) showed strong interest. The majority of patients (73.8%, n = 1319) did not know their LDL-C levels, and 45.7% (n = 817) of those who did had elevated levels. Conclusions: The findings suggest that while interest in telemedicine methods for the management of arterial hypertension and dyslipidemia exists among Slovak patients, it is more moderate than initially assumed. Importantly, expressed willingness to participate in a study should not be directly equated with readiness to adopt new technologies in daily practice. Successful integration of telemonitoring into the Slovak healthcare system will therefore require not only patient engagement but also active support from healthcare providers to overcome practical and motivational barriers. These findings highlight the need for targeted implementation strategies that address the specific barriers identified in the Central and Eastern European healthcare context. Full article

From the corona virus pandemic in Japan that started with the “Diamond Princess” accident, it became clear that rapid detection, a high sensitivity, multiple diagnostic items, one-step one-base point mutation detection, a fast speed of system development, portability (small size and light weight), full automation, random access, and other conditions are required for future point-of-care testing systems. The Eprimer-SmartAmp technology that was developed possesses characteristics fully aligned with these requirements. Building upon this platform, the “GenPad” system was subsequently established. The GenPad system is widely applicable not only to emerging foreign infectious diseases, but also to cancer, lifestyle-related diseases, and other areas of healthcare through telemedicine and intraoperative nucleic acid diagnoses. In collaboration with telecommunication systems, GenPad is expected to contribute to the establishment of a smart medical city with a countermeasure against emerging foreign infectious diseases, where individuals can check their own health conditions in all healthcare areas. Full article

The integration of robotics and mobile networks (5G/6G) through the Internet of Robotic Things (IoRT) is revolutionizing telemedicine, enabling remote physician participation in scenarios where specialists are scarce, where there is a high risk to them, such as in conflicts or natural disasters, or where access to a medical facility is not possible. Nevertheless, touching a human safely with a robotic arm in non-engineered or even out-of-hospital environments presents substantial challenges. This article presents a novel IoRT approach for healthcare in or from remote areas, enabling interaction between a specialist’s hand and a robotic hand. We introduce the IoRT-in-hand: a smart, lightweight end-effector that extends the specialist’s hand, integrating a medical instrument, an RGB camera with servos, a force/torque sensor, and a mini-PC with Internet connectivity. Additionally, we propose an open-source Android app combining MQTT and ROS for real-time remote manipulation, alongside an Edge–Cloud architecture that links the physical robot with its Digital Twin (DT), enabling precise control and 3D visual feedback of the robot’s environment. A proof of concept is presented for the proposed tele-robotic system, using a 6-DOF manipulator with the IoRT-in-hand to perform an ultrasound scan. Teleoperation was conducted over 2300 km via a 5G NSA network on the operator side and a wired network in a laboratory on the robot side. Performance was assessed through human subject feedback, sensory data, and latency measurements, demonstrating the system’s potential for remote healthcare and emergency applications. The source code and CAD models of the IoRT-in-hand prototype are publicly available in an open-access repository to encourage reproducibility and facilitate further developments in robotic telemedicine. Full article

Diabetic foot ulcers (DFUs) are a serious complication of diabetes, associated with high recurrence and amputation rates. Adherence to offloading devices is critical for wound healing but remains inadequately monitored in real-world settings. This study evaluates the SmartBoot edge-computing system—a wearable, real-time remote monitoring solution integrating an inertial measurement unit (Sensoria Core) and smartwatch—for its validity in quantifying cadence and step count as digital biomarkers of frailty, and for detecting adherence. Twelve healthy adults wore two types of removable offloading boots (Össur and Foot Defender) during walking tasks at varied speeds; system outputs were validated against a gold-standard wearable and compared with staff-recorded adherence logs. Additionally, user experience was assessed using the Technology Acceptance Model (TAM) in healthy participants (n = 12) and patients with DFU (n = 81). The SmartBoot demonstrated high accuracy in cadence and step count across conditions (bias < 5.5%), with an adherence detection accuracy of 96% (Össur) and 97% (Foot Defender). TAM results indicated strong user acceptance and perceived ease of use across both cohorts. These findings support the SmartBoot system’s potential as a valid, scalable solution for real-time remote monitoring of adherence and mobility in DFU management. Further clinical validation in ongoing studies involving DFU patients is underway. Full article

Bacterial vaginosis (BV) affects 30% of women annually, but many face barriers to in-person care. Here we present real-world outcomes of remote BV diagnosis and management through self-collected vaginal microbiome (VMB) testing and telemedicine visits, focusing on symptom resolution, recurrence, and overall microbial shifts. Among the 1159 study participants, 75.5% experienced symptom resolution at four weeks when managed with our algorithm-guided treatment protocol. At a median follow-up of 4.4 months after the initial visit, 30.0% of patients experienced recurrent BV, which is lower than the typical recurrence rates seen in historical in-person cohorts. Across the entire cohort, metagenomic data demonstrated a significant increase in Lactobacillus abundance (mean of 32.9% to 48.4%, p < 0.0001) and a corresponding decrease in BV-associated taxa such as Gardnerella, Prevotella, and Fannyhessea. A PERMANOVA of pairwise Bray–Curtis distances showed significant separation between pre-and post-treatment samples (pseudo-F = 37.6, p < 0.0001), driven by an increase in Lactobacillus-dominated samples. Treatment adherence was high (a total of 78% reported perfect or near-perfect adherence), and adverse events were generally mild (in total, 22% reported vaginal irritation, and 13% reported abnormal discharge). These results demonstrate that Evvy’s at-home metagenomic platform, paired with telemedicine and a smart treatment algorithm, delivers robust clinical and microbial outcomes. This work offers a novel approach to managing bacterial vaginosis, a challenging condition characterized by persistently high recurrence rates. Full article

Stroke-induced hand dysfunction substantially impairs patients’ quality of life, creating an urgent need for portable, adaptive rehabilitation devices. This study introduces a smart rehabilitation glove actuated by shape-memory alloy (SMA) wires, leveraging their high power-to-weight ratio, controllable strain recovery, and reversible phase transformation to overcome the limitations of conventional motor-driven or pneumatic gloves. The glove incorporates SMA-based actuation units achieving 50 mm contraction (5% strain) within 7 s, enabling finger flexion to ~34° for personalized rehabilitation protocols. A mobile application provides wireless regulation of SMA actuation modes and facilitates real-time telemedicine consultations. The prototype demonstrates an ultra-lightweight, compact design enabled by SMA’s intrinsic properties, offering a promising solution for home-based post-stroke rehabilitation. This work establishes the transformative potential of SMAs in wearable biomedical technologies. Full article

Scoliosis is one of the most common spinal deformities, which affects millions of people worldwide. Bracing and physiotherapy exercises represent the first-line, non-invasive approaches for managing scoliosis. In recent years, the use of wearable devices has spread as a novel approach to the treatment of scoliosis. However, their effectiveness in treatment planning and outcomes has not been thoroughly evaluated. This manuscript provides a scoping review of the classification and application of wearable devices and the role of artificial intelligence (AI) in interpreting the data collected by wearable devices and guiding the treatment. A systematic search was carried out on Scopus, Web of Science, PubMed, and EMBASE for studies published between January 2020 and February 2025. A total of 269 studies were screened, and 88 articles were reviewed in depth. Inclusion criteria encompassed articles focusing on wearable devices integrated into smart braces, rehabilitation systems for scoliosis management, AI and machine-learning (ML) applications in scoliosis treatment, virtual reality (VR), and telemedicine for scoliosis care. The literature shows that the use of wearable devices can enhance scoliosis treatment by improving the efficiency of braces and enabling remote monitoring in rehabilitation programs. However, more research is needed to evaluate user compliance, long-term effectiveness, and the need for personalized interventions. Future advancements in artificial intelligence, microsensor technology, and data analytics may enhance the efficacy of these devices, which can lead to more personalized and accessible scoliosis treatment. Full article

The current design of community smart health spaces lacks a systematic theoretical framework. This study innovatively proposes a hybrid model combining a SWOT analysis, the “four orders of design”, AHP, and TOPSIS to optimize the design of community smart health spaces systematically. First, a SWOT analysis is employed to assess the current state of community smart health spaces, and strategies are proposed based on this study. Subsequently, the “four orders of design” framework is integrated to clarify the design priorities for symbols, tangible objects, action events, and system environments. The AHP hierarchical analysis method is then used to quantify the weights of 16 design indicators, ensuring the objectivity and scientific rigor of decision-making. Finally, the TOPSIS method is introduced to validate the feasibility of the proposed solutions. The study found that (1) among the four categories of needs—behavioral experience, perceptual experience, hardware facilities, and software facilities—behavioral experience (weight 0.470) is the core indicator, with telemedicine (0.197) and autonomous driving (0.121) being the key functions. (2) The overall alignment of this design scheme is 0.844, with user satisfaction significantly superior to traditional schemes, proving the feasibility of the hybrid model. The research findings support decision-making in constructing smart health spaces in communities, thereby helping to upgrade smart health space services in communities. Full article

Smart telemedicine represents an innovative application of information and communication technology within the healthcare sector, encompassing healthcare delivery, disease management, public health surveillance, education, and research. The commercialization of 5G and the extensive adoption of the Internet of Things (IoT) enable smart telemedicine devices to mitigate geographical and transmission delays, hence enhancing the quality of treatment provided to individuals. Although intelligent medicine is significant, previous studies emphasize the implementation and adoption of systems or technologies with few studies conducted on the platform of smart telemedicine equipment. This study aims to address the research gap by forecasting future developments and delineating smart telemedicine device designs utilizing platform-based design. We introduce a hybrid multi-criteria model that delineates the components of the intelligent medical platform. A portable 12-lead electrocardiogram (ECG) system is used by a global telemedicine technology company to assess the viability of the suggested framework. The portable 12-lead ECG device integrates artificial intelligence (AI), cloud computing, and 6G technology. The results of this study provide a basis for product creation by other smart telemedicine companies, while the platform-based analytical methodology can be employed for future product design. Full article

Telemedicine diagnosis has become a more flexible and convenient way to receive diagnoses, which is of great significance in enhancing diagnosis, cutting costs, and serving remote users. However, telemedicine faces many security problems, such as the complexity of user authentication, the balance of the existing biometric factor authentication scheme, the unpredictability of user behavior, and the difficulty of unified authentication due to the differences in the security standards and authentication mechanisms of different trust domains, which affect the sustainable development of telemedicine. To address the above issues, this paper presents an identity management scheme based on multi-factor authentication and dynamic trust evaluation for telemedicine. Its authentication combines iris recognition for secure biometric verification, smart cards for encrypted credential storage, and static passwords for supplementary verification, addressing scenarios like facial coverage in medical settings. The scheme dynamically adjusts authentication based on attack rates, login anomalies, and service durations. By integrating ShangMi cryptographic algorithms and blockchain, it optimizes performance, achieving 35% lower communication overhead than previous protocols. A security analysis shows it resists impersonation, man-in-the-middle, and password modification attacks while preserving user anonymity. System evaluation meets authoritative standards, validating its practicality. This scheme balances security and efficiency, providing a strong basis for telemedicine’s long-term viability. Full article