: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. : Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. : The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. : This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.
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http://dx.doi.org/10.1142/S0129065722500472 | DOI Listing |
Front Surg
January 2025
Rehabilitation Center, The First Rehabilitation Hospital in Shanghai, Shanghai, China.
Background: Telerehabilitation is gaining popularity in European and American countries, but whether it can be successfully implemented in China still lacks support from clinical studies.
Objective: This trial aimed to determine if a home-based telerehabilitation method is clinically noninferior to standard in-hospital face-to-face rehabilitation for elderly patients with total hip arthroplasty (THA) in China.
Methods: This multicenter randomized controlled trial was conducted from January 2021 to June 2022 at The First Rehabilitation Hospital in Shanghai, Shanghai Jiao Tong University affiliated Sixth People's Hospital and Shanghai Tongji University affiliated Tenth People's Hospital.
The rising prevalence of obesity and diabetes underscores the need for innovative approaches to promote healthier lifestyles and improve clinical outcomes. Emerging evidence suggests that integrating mobile health (mHealth) technologies, such as smartphone applications and wearable devices, may provide a promising solution. mHealth interventions have the potential to enhance the delivery and accessibility of nutritional therapy and lifestyle modification programs for people with obesity and diabetes.
View Article and Find Full Text PDFThe explosive growth of mobile data traffic and the demands of 6 G networks for ultra-high data rates and low latency necessitate advanced infrastructure solutions. One promising approach is the implementation of radio-over-fiber (RoF)-based distributed antenna systems (DAS), which can efficiently transmit radio frequency signals over optical fiber, especially in dense indoor environments. However, analog RoF systems face challenges, including noise, nonlinearities, and power fading caused by chromatic dispersion.
View Article and Find Full Text PDFSingle-pixel imaging (SPI) using deep learning networks, e.g., convolutional neural networks (CNNs) and vision transformers (ViTs), has made significant progress.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Background And Objective: Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions.
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