Background: A nanomaterial-based electronic-skin (E-Skin) wearable sensor has been successfully used for detecting and measuring body movements such as finger movement and foot pressure. The ultrathin and highly sensitive characteristics of E-Skin sensor make it a suitable alternative for continuously out-of-hospital lumbar–pelvic movement (LPM) monitoring. Monitoring these movements can help medical experts better understand individuals’ low back pain experience. However, there is a lack of prior studies in this research area. Therefore, this paper explores the potential of E-Skin sensors to detect and measure the anatomical angles of lumbar–pelvic movements by building a linear relationship model to compare its performance to clinically validated inertial measurement unit (IMU)-based sensing system (ViMove). Methods: The paper first presents a review and classification of existing wireless sensing technologies for monitoring of body movements, and then it describes a series of experiments performed with E-Skin sensors for detecting five standard LPMs including flexion, extension, pelvic tilt, lateral flexion, and rotation, and measure their anatomical angles. The outputs of both E-Skin and ViMove sensors were recorded during each experiment and further analysed to build the comparative models to evaluate the performance of detecting and measuring LPMs. Results: E-Skin sensor outputs showed a persistently repeating pattern for each movement. Due to the ability to sense minor skin deformation by E-skin sensor, its reaction time in detecting lumbar–pelvic movement is quicker than ViMove by ~1 s. Conclusions: E-Skin sensors offer new capabilities for detecting and measuring lumbar–pelvic movements. They have lower cost compared to commercially available IMU-based systems and their non-invasive highly stretchable characteristic makes them more comfortable for long-term use. These features make them a suitable sensing technology for developing continuous, out-of-hospital real-time monitoring and management systems for individuals with low back pain.

Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body’s needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community. However, many digital solutions have not involved consumers and health care professionals in their design, leading to poor adoption. The SmartHeart project aimed to codevelop a smart health ecosystem to support the early detection of HF deterioration and encourage self-care, potentially preventing hospitalizations. Objective: This study aims to provide an overview of the SmartHeart project by describing our approach to designing the SmartHeart system, outlining its features, and describing the planned pilot study to determine the feasibility of the system. Methods: We used the Integrate, Design, Assess, and Share (IDEAS) framework to guide the development of the SmartHeart system, involving users (people with HF and their caregivers) and stakeholders (health care providers involved in the management of HF) in its design. SmartHeart is a complete remote heart health monitoring and automated feedback delivery system. It includes 2 user interfaces for patients: an Amazon Alexa conversational agent and a smartphone app. The system collects physiological, symptom, and behavioral data through wireless sensors and self-reports from users. These data are processed and analyzed to provide personalized health insights, self-care support, and alerts in case of health deterioration. The system also includes a web-based user interface for health care professionals, allowing them to access data, send messages to users, and receive notifications about potential health deterioration. A single-arm, multicenter pilot trial (N=20) is planned to determine the feasibility and acceptability of SmartHeart before evaluation through a randomized controlled trial. The primary outcome will be a description of the study's feasibility (recruitment, attrition, engagement, and changes in self-care). Results: The SmartHeart study started in January 2021 on procurement of funding. Recruitment for the pilot trial started in August 2024 and will be completed by March 2025. We have currently enrolled 12 participants. Follow-up of all participants will be completed by the end of May 2025. Conclusions: We have co-designed and developed a complete remote heart health monitoring and automated feedback delivery system for the early detection of HF deterioration and prevention of HF-related hospitalizations. The next step is a pilot study, which will provide valuable information on feasibility and preliminary effects to inform a larger evaluation trial. SmartHeart has the potential to augment existing health services and help people with HF stay well while living in the community. International Registered Report Identifier (IRRID): DERR1-10.2196/62964

Resistance to Schistosoma mansoni infection in the mouse has been induced either specifically by a primary infection with this parasite or nonspecifically by a variety of immunostimulants such as BCG. In the present study we developed an in vitro system to examine the effector mechanism of nonspecifically induced resistance. Activated macrophage monolayers obtained from BCG- or Corynebacterium parvum-treated mice killed a respective mean of 32 ± 6% and 48 ± 5% of schistosomula after 24 hr incubation. The killing of the parasites was verified by their inability to mature to adult worms upon injection into normal mice. The activated macrophage-mediated killing was related to cell:parasite ratio, and was partially lost if the macrophage monolayers were kept in cultures for 24 hr before incubation with the organism. Supernatants of macrophages cultured in the presence of schistosomula killed a mean of 51 ± 3% of the organisms whereas those from cells cultured alone resulted in a mean killing of 25 ± 3%. Furthermore, toxic supernatants could be generated equally well on incubation with S. mansoni schistosomula or Trichinella spiralis larvae. Our data show that activated macrophage monolayers through soluble mediators destroy a significant proportion of the multicellular parasite S. mansoni schistosomula in vitro.

Continuous fetal movement monitoring in late pregnancy may improve fetal wellbeing and pregnancy outcomes. While fetal movements can be visualized with ultrasound, it is intermittent and limited to clinical settings. Inertial measurement units may enable at-home fetal monitoring but usually require a large-footprint, multisensor design. Here, we report smart, compact wearable pressure-strain combo sensors continuously monitoring fetal movements through maternal abdominal skin motions. In the 2D and 3D artificial abdomen systems, our octagonal-shaped gold nanowire–based strain sensor served as an isotropic sensor, enabling omnidirectional simulated “kicking load” detection within an area of ~77 (2D) and ~217 cm ² (3D), while an interdigitated electrode–based pressure sensor showed highly sensitive localized load detection. Building upon these findings, we designed compact pressure- and strain-sensing integrated Band-Aids and tested on 59 pregnant women. We developed machine learning models to distinguish fetal from nonfetal movements with >90% accuracy in ultrasound-based validation studies. This AI-powered, Band-Aid–like sensing system offers potential as a compact, comfortable, and accurate continuous out-of-hospital fetal movement monitoring technology.

Lumbar-pelvic movements (LPMs) are generally performed in the clinical setting to identify limitations in a range of movements. Continuous monitoring of these movements can provide real-time feedback to both patients and medical experts with the potential of identifying activities that may precipitate symptoms of low back pain (LBP) as well as improving therapy by providing a personalised approach. Recent advances in mobile computing technology and wearable sensors have paved the way for developing mobile physical activity monitoring applications with more advanced and complex algorithms, such as deep neural network (DNN) based models. However, there is a lack of prior studies that focus on real-time LPMs detection with multimodal sensory data. Meanwhile, most research in the area of body movement detection do not consider the potential transition logic of the constituent low-level body movements (e.g., LPMs) within their corresponding high-level physical activity. This information could significantly increase accuracy of detection results. Further, current studies mainly perform deep learning-based movement detection in the cloud (or a backend server) that could increase network bandwidth and response time. To address these limitations, this paper proposes a novel LPMs detection approach using an enhanced and adapted hybrid DNN model, which includes a convolutional neural network followed by a long-short term memory recurrent network (CNN-LSTM), and performs detection locally on the mobile device. The results of a comparative evaluation of the proposed model and baseline models are described. We also introduce a set of domain-specific pre-defined rules, based on the transition logic information, to reconstruct the detection outputs to further improve the detection accuracy.