Cloud computing has recently gained widespread attention owing to its use in applications involving the Internet of Things (IoT). However, the transmission of massive volumes of data to a cloud server often results in overhead. Fog computing has emerged as a viable solution to address this issue. This study implements an Artificial Intelligence of Things (AIoT) system based on fog computing on a smart farm. Three experiments are conducted to evaluate the performance of the AIoT system. First, network traffic volumes between systems employing and not employing fog computing are compared. Second, the performance of the communication protocols—hypertext transport protocol (HTTP), message queuing telemetry transport protocol (MQTT), and constrained application protocol (CoAP)—commonly used in IoT applications is assessed. Finally, a convolutional neural network-based algorithm is introduced to determine the maturity level of coffee tree images. Experimental data are collected over ten days from a coffee tree farm in the Republic of Korea. Notably, the fog computing system demonstrates a 26% reduction in the cumulative data volume compared with a non-fog system. MQTT exhibits stable results in terms of the data volume and loss rate. Additionally, the maturity level determination algorithm performed on coffee fruits provides reliable results.

The rehabilitation of amputees has gained research attention due to digital rehabilitation systems. Traditional methods rely on intensive hospital training and prosthetics, which have limitations in accessibility and sustainability. This study proposes a mixed reality (MR) system combining real and virtual environments to enhance effectiveness and provide immediate patient feedback. The system enables amputees to perform exercises outside the hospital, ensuring continuous rehabilitation. Surface electromyography (sEMG) analysis monitors muscle activity and synchronizes intended movements with virtual prosthetics in real-time. Various algorithms filter noise from sEMG signals and reproduce amputees’ intended movements. Pre-processing includes amplification, mean filtering, and Savitzky-Golay filtering for signal quality improvement. A gated recurrent unit model with four hidden layers and dropout techniques is used for motion classification. The sEMG data was collected over 12 weeks from non-amputees and amputees, achieving 92.81% accuracy and 0.231 loss rate. Clinical trials involved quadriceps strengthening exercises, resulting in 231.61% muscle activity improvement. The MR-based system promotes muscle activation, enhances training effectiveness, and improves quality of life. This study integrates MR and sEMG technologies to provide a patient-centric, effective, and sustainable solution, overcoming limitations of existing methods. The system facilitates the convergence of technology and medical services, contributing to personalized rehabilitation frameworks.

There has been a surge in research focused on the analysis of vital signs using remote photoplethysmography (rPPG) sensors, as opposed to traditional photoplethysmography (PPG) methods. Unlike PPG, rPPG imposes no spatial constraints and employs a straightforward measurement technique, making it increasingly prevalent. Its integration into image processing, harnesses the remarkable advances in artificial-intelligence technology, achieving accuracy that is comparable to that of traditional PPG sensors. In prior studies, obtaining vital signs often necessitated an unnecessary and procedural fixation of facial positions within frames to enhance predictive accuracy. Despite such fixation, achieving notably high accuracy remained elusive. Here, we introduce a simple yet robust approach utilizing videos captured by an rPPG sensor, ensuring both high accuracy and resilience to noise. We propose a convolutional neural network model meticulously designed to resist interference from noise data that may arise in the initial stages, coupled with effective preprocessing techniques to attain superior predictive accuracy. Data extracted by a facial extractor undergoes preprocessing via normalization. Leveraging the Temporal Shift Module (TSM), this normalization efficiently captures temporal relationships without incurring additional computational overhead. Mitigating noise signal interference from non-facial data through the use of multiple attention masks and augmenting prediction accuracy via skip connections. Moreover, we compile a specially tailored dataset for pulse rate and breath rate data, catering specifically to the East Asian population. The proposed process demonstrates outstanding performance in predicting both pulse rate and breath rate.

Pressure injuries pose significant health risks, especially for the elderly, immobile individuals, and those with sensory impairments. These injuries can rapidly become chronic, making initial diagnosis important. Due to the difficulty of transporting patients from local health facilities to higher-level general hospitals for treatment, it is essential to utilize telemedicine tools, such as chatbots, to ensure rapid initial diagnosis. Recent advances in artificial intelligence have demonstrated potential for medical imaging and disease classification. Ongoing research in the field of dermatological diseases focuses on disease classification. However, the assessment accuracy of artificial intelligence is often limited by unequal class distributions and insufficient dataset quantities. In this study, we aim to enhance the accuracy of artificial intelligence models by generating synthetic datasets. Specifically, we focused on training models for Pressure Injury assessment using both real and synthetic datasets. We used PI data at a domestic medical university. As part of our supplementary research, we established a chatbot system to facilitate the assessment of pressure injuries. Using both constructed and synthetic data, we achieved a top-1 accuracy of 92.03%. The experimental results demonstrate that combining real and synthetic data significantly improves model accuracy. These findings suggest that synthetic datasets can be effectively utilized to address the limitations of small-scale datasets in medical applications. Future research should explore the use of diverse synthetic data generation methods and validate model performance on a variety of datasets to enhance the generalization and robustness of AI models for Pressure Injury assessment.