In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna .v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately tracks and evaluates individualized rehabilitation exercises for patients suffering from low back pain using features such as exercise visualization and guidance, real-time feedback and monitoring, and personalized exercise plans. This dashboard was very effective for tracking rehabilitation progress. We recruited 32 individuals to participate in this pilot study. We monitored an individual’s overall performance for one week. Of the participants, 18.75% engaged in rehabilitative exercises less frequently than twice daily; 81.25% did so at least three times daily. The proposed Long Short-Term Memory (LSTM) architecture had a training accuracy score of 98.8% and a testing accuracy of 99.7%, with an average accuracy of 10-fold cross-validation of 98.54%. On the pre- and post-test assessments, there is a significant difference between pain levels, with a p < 0.05 and a t-stat value of 12.175. The proposed system’s usability score is 79.375, indicating that it provides a user-friendly environment for the user to use the PoAna .v1 web application. So far, our research suggests that the Streamlit 1.38.0-based dashboard improves patients’ engagement, adherence, and success with exercise. Future research aims to add more characteristics that can improve the complete care of low back pain (LBP) and validate the effectiveness of this intervention in larger patient cohorts.

The revolutionary merging of digital twin technology with deep learning methods brings a new age of creativity and efficiency in smart manufacturing. Improving production processes has been much easier with the advent of digital twins (DTs), which simulate real-world procedures in a computer simulation. Affordable and systematic DT implementation has been made possible by the increasing use of cyber-physical systems (CPS), the Internet of Things (IoT), big data analytics, and cloud computing in the manufacturing sector. This can improve manufacturing, track the health of machines and equipment, and offer product support and services. By incorporating deep learning algorithms, digital twins can acquire information, adapt, and make smart predictions using complicated information sources. These digital twins can optimize schedules for manufacturing, anticipate equipment faults, and independently analyze massive datasets with the help of dominant neural networks. With an eye on deep learning’s revolutionary effect on production efficiency, this chapter delves into how insights produced by deep learning help with controlling and monitoring in real-time, guaranteeing flexible and agile production processes, quality assurance, supply chain management, and asset performance. <!----> <!----> <!----> <!---->

In recent years, advancements in optimized Deep Learning (DL) models have strived to speed up self-recuperation training effectively. Video-based acceleration techniques enhance the self-recuperation process concerning fast recovery, increase patient engagement, individual progress tracking, optimized exercise plans, remote monitoring, effective feedback generation for correction of exercise poses, and reduced healthcare costs. This article explored the state-of-the-art comparison of various acceleration techniques for hardware and software in non-invasive self-recuperation. Also, we have deliberated some optimized DL methods to be deployed in the edge computers of users and developed accelerated hardware to achieve high precision outcomes with low computation and memory consumption on a home-based self-recuperation training system. Finally, this article addresses challenges and suggestions for future research directions on home-based self-recuperation acceleration techniques.

Advancements in human-machine interaction are enabling prosthetic hand control for people with physical disabilities, achieved through surface electromyography (sEMG). The sEMG is an emerging, non-invasive, and versatile technique for recuperation and prosthetic applications. For classifying both discrete and continuous muscle movements on the human body, carried through advanced non-invasive sensors, high-density sEMG (HD-sEMG), and Artificial Intelligence (AI) learning models. The combination of sensors with AI models enables the recognition of precise hand gestures and body postures by extracting key signal features for muscle motor activities through the placement of electrodes on the skin surface. This systematic review outlines English-language articles from 2020 to 2025, focusing on the process of signal acquisition and processing for deep learning or machine learning models, in line with time and frequency methods for decomposing motor units. Surveyed challenges in the real-world applications, such as healthcare, artificial replacement of body parts, and sports medicine. To highlight the need for reproducibility and analyze the performance of various AI models, we discussed the open-source datasets and toolboxes used for software development with standardized benchmark datasets. Experimental challenges related to sEMG hardware, including electrode displacement on the skin surface, inter-individual variability, and cross-talk, were discussed. Ultimately, the pathway for future directions in the development of AI-based sEMG models, with a focus on explainable AI, multimodal systems, and diverse federated data, aims to bridge the gap between laboratory research and clinical impact