Search Results (648)

Pain and knee stiffness are common problems following total knee replacement surgery, with 10–20% of patients reporting dissatisfaction following their procedure. A remote assessment of knee stiffness could improve outcomes through continuous monitoring, facilitating timely intervention. Using machine learning algorithms, computer vision can extract joint angles from video footage, offering a method to monitor knee range of motion in patients’ homes. This study outlines a protocol to provide proof of concept and validate a computer vision-based approach for measuring knee range of motion in individuals who have undergone total knee replacement. The study also explores the feasibility of integrating this technology into clinical practice, enhancing post-operative care. The study is divided into three components: carrying out focus groups, validating the computer vision-based software, and home testing. The focus groups will involve five people who underwent total knee replacement and ten healthcare professionals or carers who will discuss the deployment of the software in clinical settings. For the validation phase, 60 participants, including 30 patients who underwent total knee replacement surgery five to nine weeks prior and 30 healthy controls, will be recruited. The participants will perform five tasks, including the sit-to-stand test, where knee range of motion will be measured using computer vision-based markerless motion capture software, marker-based motion capture, and physiotherapy assessments. The accuracy and reliability of the software will be evaluated against these established methods. Participants will perform the sit-to-stand task at home. This will allow for a comparison between home-recorded and lab-based data. The findings from this study have the potential to significantly enhance the monitoring of knee stiffness following total knee replacement. By providing accurate, remote measurements and enabling the early detection of issues, this technology could facilitate timely referrals to non-surgical treatments, ultimately reducing the need for costly and invasive procedures to improve knee range of motion. Full article

Chronic respiratory diseases (CRDs), including asthma and chronic obstructive pulmonary disease (COPD), represent significant global health challenges, contributing to substantial morbidity and mortality. As the prevalence of CRDs continues to rise, particularly in low-income countries, there is a pressing need for more efficient and personalized approaches to diagnosis and treatment. This article explores the impact of emerging technologies, particularly artificial intelligence (AI), on the management of CRDs. AI applications, including machine learning (ML), deep learning (DL), and large language models (LLMs), are transforming the landscape of CRD care, enabling earlier diagnosis, personalized treatment, and enhanced remote patient monitoring. The integration of AI with telehealth and wearable technologies further supports proactive interventions and improved patient outcomes. However, challenges remain, including issues related to data quality, algorithmic bias, and ethical concerns such as patient privacy and AI transparency. This paper evaluates the effectiveness, accessibility, and ethical implications of AI-driven tools in CRD management, offering insights into their potential to shape the future of respiratory healthcare. The integration of AI and advanced technologies in managing CRDs like COPD and asthma holds substantial potential for enhancing early diagnosis, personalized treatment, and remote monitoring, though challenges remain regarding data quality, ethical considerations, and regulatory oversight. Full article

Background: Heart failure (HF) is a chronic disease with a steadily increasing prevalence, high mortality, and social and economic costs. Furthermore, every hospitalization for acute HF is associated with worsening prognosis and reduced life expectancy. In order to prevent hospitalizations, it would be useful to have instruments that can predict them well in advance. Methods: We performed a review on remote monitoring of heart failure through implantable devices. Results: Precise multi-parameter algorithms, available for ICD and CRT-D patients, have been created, which also use artificial intelligence and are able to predict a new heart failure event more than 30 days in advance. There are also implantable pulmonary artery devices that can predict hospitalizations and reduce the impact of heart failure. The proper organization of transmission and alert management is crucial for clinical success in using these tools. Conclusions: The full implementation of remote monitoring of implantable devices, and in particular, the use of new algorithms for the prediction of acute heart failure episodes, represents a huge challenge but also a huge opportunity. Full article

Medical ultrasound is a widely used diagnostic imaging modality that provides real-time imaging at a relatively low cost. However, its widespread application is hindered by the need for expert operation, particularly in remote regional areas where trained sonographers are scarce. This paper presents the development of HaptiScan, a state-of-the-art telerobotic ultrasound system equipped with haptic feedback. The system utilizes a commercially available robotic manipulator, the UR5 robot from Universal Robots, integrated with a force/torque sensor and the Phantom Omni haptic device. This configuration enables skilled sonographers to remotely conduct ultrasound procedures via an internet connection, addressing both the geographic and ergonomic limitations faced in traditional sonography. Key innovative features of the system include real-time force feedback, ensuring that sonographers can precisely control the ultrasound probe from a remote location. The system is further enhanced by safety measures such as over-force sensing, patient discomfort monitoring, and emergency stop mechanisms. Quantitative indicators of the system’s performance include successful teleoperation over long distances with time delays, as demonstrated in simulations. These simulations validate the system’s control methodologies, showing stable performance with force feedback under varying time delays and distances. Additionally, the UR5 manipulator’s precision, kinematic, and dynamic models are mathematically formulated to optimize teleoperation. The results highlight the effectiveness of the proposed system in overcoming the technical challenges of remote ultrasound procedures, offering a viable solution for real-world telemedicine applications. Full article

In recent years, skin-mounted devices have gained prominence in personal wellness and remote patient care. However, the rigid components of many wearables often cause discomfort due to their mechanical mismatch with the skin. To address this, we extend the use of the solderable stretchable sensing system (S4) to develop a wireless skin temperature-sensing smart adhesive. This work introduces two novel types of progress in wearables: the first demonstration of Bluetooth-integration and development of a thin-film-based stretchable inverted-F antenna (SIFA). Characterized through RF simulations, vector network analysis under deformation, and anechoic chamber tests, SIFA demonstrated potential as a low-profile, on-body Bluetooth antenna with a resonant frequency of 2.45 GHz that helps S4 retain its thin overall profile. The final S4 system achieved high correlation (R = 0.95, p < 0.001, mean standard error = 0.04 °C) with commercial sensors during daily activities. These findings suggest that S4-based smart adhesives integrated with SIFAs could offer a promising platform for comfortable, efficient, and functional skin-integrated wearables, supporting a range of health monitoring applications. Full article

Medical professionals primarily utilize medical images to detect anomalies within the interior structures and essential organs concealed by the skeletal and dermal layers. The primary purpose of medical imaging is to extract image features for the diagnosis of medical conditions. The processing of these images is indispensable for evaluating a patient’s health. However, when monitoring patients over extended periods using specific medical imaging technologies, a substantial volume of data accumulates daily. Consequently, there arises a necessity to compress these data in order to remove duplicates and speed up the process of acquiring data, making it appropriate for effective analysis and transmission. Compressed Sensing (CS) has recently gained widespread acceptance for rapidly compressing images with a reduced number of samples. Ensuring high-quality image reconstruction using conventional CS and block-based CS (BCS) poses a significant challenge since they rely on randomly selected samples. This challenge can be surmounted by adopting a variable BCS approach that selectively samples from diverse regions within an image. In this context, this paper introduces a novel CS method that uses an energy matrix, namely coefficient shuffling variable BCS (CSEM-VBCS), tailored for compressing a variety of medical images with balanced sparsity, thereby achieving a substantial compression ratio and good reconstruction quality. The results of experimental evaluations underscore a remarkable enhancement in the performance metrics of the proposed method when compared to contemporary state-of-the-art techniques. Unlike other approaches, CSEM-VBCS uses coefficient shuffling to prioritize regions of interest, allowing for more effective compression without compromising image quality. This strategy is especially useful in telemedicine, where bandwidth constraints often limit the transmission of high-resolution medical images. By ensuring faster data acquisition and reduced redundancy, CSEM-VBCS significantly enhances the efficiency of remote patient monitoring and diagnosis. Full article

Background/Objectives: Remote monitoring (RM) cardiac implantable electronic devices for adults delivers improved patient outcomes. However, previously used bedside transmitters are not optimal due to deficient patient adherence. The goal of this study was to evaluate the efficacy of RM regarding the connectivity of smartphone app-based solutions, adherence to scheduled automatic follow-ups, and prevalence of alert-based events. Methods: We evaluated the adult heart failure (HF) population with an implanted cardiac resynchronization therapy defibrillator (CRT-D) divided into two arms: with app-based RM (abRM) and without app-based RM (control). Results: A total of 81 patients (median age of 69.0) were included in our study. Sixty-five patients received a CRT-D with abRM functionality, and sixteen did not. Twelve patients had no smartphone, and two provided no consent, resulting in their transfer to the control group. Finally, the abRM arm consisted of 51 patients, while 30 patients were in the control group. The median period of follow-up lasted 12 months. Among abRM patients, 98.0% successfully transmitted their first scheduled follow-up, and 80.4% were continuously monitored. Alert-based events were mainly related to arrhythmic events and device functionality with significantly shorter median times to notification (1 day vs. 101 days; p < 0.0001) in the abRM group. Conclusions: Our study showed a high level of compliance with timely initial transmission and adherence to scheduled remote follow-ups. Patient enrollment eligibility was a major challenge due to the limited accessibility of smartphones in the population. App-based RM demonstrated an accurate notification of events and patient-initiated transmissions in emergencies, regardless of location. Full article

The healthcare sector is undergoing a significant transformation due to the rapid expansion of data and advancements in digital technologies. The increasing complexity of healthcare data, including electronic health records (EHRs), medical imaging, and patient monitoring, underscores the necessity of big data technologies. These technologies are essential for enhancing decision-making, personalizing treatments, and optimizing operations. Digitalization further revolutionizes healthcare by improving accessibility and convenience through technologies such as EHRs, telemedicine, and wearable health devices. Cloud computing, with its scalable resources and cost efficiency, plays a crucial role in managing large-scale healthcare data and supporting remote treatment. However, integrating cloud computing in healthcare, especially in remote areas with limited network infrastructure, presents challenges. These include difficulties in accessing cloud services and concerns over data security. This article proposes a smart healthcare system utilizing the edge-cloud continuum to address these issues. The proposed system aims to enhance data accessibility and security while maintaining high prediction accuracy for disease management. The study includes foundational knowledge of relevant technologies, a detailed system architecture, experimental design, and discussions on conclusions and future research directions. Full article

The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations. Full article

The demand for remote rehabilitation is increasing, opening up convenient and effective home-based therapy for the sick and elderly. In this study, we use AnyBody simulations to analyze muscle activity and determine key parameters for designing a rehabilitation exoskeleton, as well as selecting the appropriate motor torque to assist patients during rehabilitation sessions. The exoskeleton was designed with a PID control mechanism for the precise management of motor positions and joint torques, and it operates in both automated and teleoperation modes. Hip and knee movements are monitored via smartphone-based IMU sensors, enabling real-time feedback. Bluetooth communication ensures seamless control during various training scenarios. Our study demonstrates that remotely controlled rehabilitation systems can be implemented effectively, offering vital support not only during global health crises such as pandemics but also in improving the accessibility of rehabilitation services in remote or underserved areas. This approach has the potential to transform the way physical therapy can be delivered, making it more accessible and adaptable to the needs of a larger patient population. Full article

Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson’s disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care. Full article

Early diagnosis and treatment of surgical wound infection can be challenging. This is especially relevant in the management of periprosthetic joint infection: early detection is key to success and reducing morbidity, mortality and resource use. ‘Smart’ dressings have been developed to detect parameters suggestive of infection. This scoping review investigates the current status of the field, limited to devices tested in animal models and/or humans, with a focus on their application to arthroplasty. The literature was searched using MEDLINE/PubMed and Embase databases from 2000 to 2023. Original articles assessing external sensing methods for the detection of wound infection in animal models or human participants were included. Sixteen articles were eligible. The results were broadly divided by sensing method: colorimetric, electrochemical and fluorescence/photothermal responses. Six of the devices detected more than one parameter (multimodal), while the rest were unimodal. The most common parameters examined were temperature and pH. Most ‘smart’ dressings focused on diagnosing infection in chronic wounds, and none were tested in humans with wound infections. There is limited late-stage research into using dressing sensors to diagnose wound infection in post-surgical patients. Future research should explore this to enable inpatient and remote outpatient monitoring of post-operative wounds to detect wound infection. Full article

Background: It has recently been shown that excessive fluctuation in blood pressure readings for an individual over time is closely associated with poor outcomes, including increased risk of cardiovascular mortality, coronary heart disease and stroke. Fluctuations may be associated with inconsistent adherence to medical recommendations. This new marker of risk has not yet been incorporated into a monitoring and intervention strategy that seeks to reduce cardiovascular risk by identifying patients through an algorithm tied to their electronic health record (EHR). Methods: We describe the methods used in an innovative “proof of concept” trial using CP&R (Cardiovascular Precision Medicine and Remote Intervention). A blood pressure variability index is calculated for clinic patients via an EHR review. Consenting patients with excessive variability are offered a remote intervention aimed at improving adherence to medical recommendations. The outcomes include the ability to identify and engage the identified patients and the effects of the intervention on blood pressure variability using a pre–post comparison design without parallel controls. Conclusions: Our innovative approach uses a recently identified marker based on reviewing and manipulating EHR data tied to a remote intervention. This design reduces patient burden and supports equitable and targeted resource allocation, utilizing an objective criterion for behavioral risk. This study is registered under ClinicalTrials.gov Identifier: NCT05814562. Full article

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography. Full article

Objective: This qualitative study sought to explore patient experiences with technologies used in the Community Health in a Virtual Environment (Co-HIVE) pilot trial. Technology is becoming increasingly prevalent in mental healthcare, and user acceptance is critical for successful adoption and therefore clinical impact. The Co-HIVE pilot trialled a model of care whereby community-dwelling patients with symptoms of depression utilised virtual appointments and remote monitoring for the assessment and management of their condition, as an adjunct to routine care. Methods: Using a qualitative descriptive design, participants for this study were patients with symptoms of moderate to severe depression (based on the 9-item Patient Health Questionnaire, PHQ-9), who had completed the Co-HIVE pilot. Data was collected via semi-structured interviews that were audio-recorded, transcribed clean-verbatim, and thematically analysed using the Framework Method. Results: Ten participants completed the semi-structured interviews. Participants reported experiencing more personalised care, improved health knowledge and understanding, and greater self-care, enabled by the remote monitoring technology. Additionally, participants reported virtual appointments supported the clinician–patient relationship and improved access to mental health services. Conclusions: This experience of participants with the Co-HIVE pilot indicates there is a degree of acceptance of health technologies for use with community mental healthcare. This acceptance demonstrates opportunities to innovate existing mental health services by leveraging technology. Full article