Appl. Syst. Innov., Volume 7, Issue 4 (August 2024) – 6 articles

The accurate and efficient recognition of gym workout activities using wearable sensors holds significant implications for assessing fitness levels, tailoring personalized training regimens, and overseeing rehabilitation progress. This study introduces CNN-ResBiGRU, a novel deep learning architecture that amalgamates residual and hybrid methodologies, aiming to precisely categorize gym exercises based on multimodal sensor data. The primary goal of this model is to effectively identify various gym workouts by integrating convolutional neural networks, residual connections, and bidirectional gated recurrent units. Raw electromyography and inertial measurement unit data collected from wearable sensors worn by individuals during strength training and gym sessions serve as inputs for the CNN-ResBiGRU model. Initially, convolutional neural network layers are employed to extract unique features in both temporal and spatial dimensions, capturing localized patterns within the sensor outputs. Subsequently, the extracted features are fed into the ResBiGRU component, leveraging residual connections and bidirectional processing to capture the exercise activities’ long-term temporal dependencies and contextual information. The performance of the proposed model is evaluated using the Myogym dataset, comprising data from 10 participants engaged in 30 distinct gym activities. The model achieves a classification accuracy of 97.29% and an F1-score of 92.68%. Ablation studies confirm the effectiveness of the convolutional neural network and ResBiGRU components. The proposed hybrid model uses wearable multimodal sensor data to accurately and efficiently recognize gym exercise activity. Full article

Based on the theory of user needs and the product development mode and framework of mobile Internet interactive innovation, a new “reality → virtual → reality” interactive innovation product development mode is constructed. It draws on the unique characteristics, systematic technical system, and comprehensive scientific and technological layout of the Metaverse. On this basis, a framework for product development and design based on interactive innovation from the Metaverse perspective is innovatively proposed. In the Metaverse scenario, interactive innovation knowledge can be easily and effectively transformed into design knowledge, and all groups of users truly participate in the whole process of product design. Moreover, the development of interactive innovative products in the Metaverse scenario can be combined with artificial intelligence (AI) technology to further automate the statistical analysis of user needs and preferences so as to meet the dynamic needs of users and accurately develop products that fit user needs and enterprise standards. In addition, users, designers, and enterprises can make joint decisions on product design solutions and development forms, and the Metaverse technology can also optimize the products with continuous iteration and obtain the optimal solutions. An automotive case study illustrates the feasibility and effectiveness of the model for product development innovation and enterprise digital transformation. Full article

This paper proposes a new approach to enhance the realism and interactivity of shooting simulation systems by integrating a three-degree–of–freedom (3-DoF) platform with sensory and interactive elements, as well as digital content. The system employs visual effects computers and servo controls, utilizing network packet messages for communication based on different scene definitions. When the control handle sends commands, the visual effects computer transmits control parameters to the image generator. Additionally, AI-controlled aircrafts act as enemy planes, autonomously determining flight paths, tracking targets, and engaging in combat, thereby enhancing realism in interactive mechanisms. An iterative learning control (ILC) is designed to provide the platform with good dynamic response, load capacity, and tracking ability when operated by a manual control handle. The core control uses a TMS320F28377D digital signal processor from Texas Instruments, integrated with visual effects computers for three-axis control, controller computation, finite state machines, and network communication operations. Experimental results demonstrate the feasibility and effectiveness of the developed three-axis shooting platform, achieving immersion and coordination with AI enemy aircrafts. Full article

In recent years, model compression techniques have gained significant attention as a means to reduce the computational and memory requirements of deep neural networks. Knowledge distillation and pruning are two prominent approaches in this domain, each offering unique advantages in achieving model efficiency. This paper investigates the combined effects of knowledge distillation and two pruning strategies, weight pruning and channel pruning, on enhancing compression efficiency and model performance. The study introduces a metric called “Performance Efficiency” to evaluate the impact of these pruning strategies on model compression and performance. Our research is conducted on the popular datasets CIFAR-10 and CIFAR-100. We compared diverse model architectures, including ResNet, DenseNet, EfficientNet, and MobileNet. The results emphasize the efficacy of both weight and channel pruning in achieving model compression. However, a significant distinction emerges, with weight pruning showing superior performance across all four architecture types. We realized that the weight pruning method better adapts to knowledge distillation than channel pruning. Pruned models show a significant reduction in parameters without a significant reduction in accuracy. Full article

The urgent need for research and study with nondestructive and noninvasive methods and the preservation of cultural heritage led to the development and application of methodologies for the multi-level digitization of cultural elements. Photogrammetry and three-dimensional scanning offer photorealistic and accurate digital representations, while X-rays and computed tomography reveal properties and characteristics of the internal and invisible structure of objects. However, the investigation of and access to these datasets are, in several cases, limited due to the increased computing resources and the special knowledge required for their processing and analysis. The evolution of immersive technologies and the creative industry of video games offers unique user experiences. Game engines are the ideal platform to host the development of easy-to-use applications that combine heterogeneous data while simultaneously integrating immersive and emerging technologies. This article seeks to shed light on how heterogeneous digital representations of 3D imaging and tomography can be harmoniously combined in a virtual space and, through simple interactions, provide holistic knowledge and enhanced experience to end users. This research builds on previous experience concerning the virtual museum for the Antikythera Mechanism and describes a conceptual framework for the design and development of an affordable and easy-to-use display tool for combined representations of heterogeneous datasets in the virtual space. Our solution was validated by 62 users who participated in tests and evaluations. The results show that the proposed methodology met its objectives. Apart from cultural heritage, the specific methodology could be easily extended and adapted for training purposes in a wide field of application, such as in education, health, engineering, industry, and more. Full article

Data-centric artificial intelligence (DC-AI) is a modern paradigm that gives more priority to data quality enhancement, rather than only optimizing the complex codes of AI models. The DC-AI paradigm is expected to substantially advance the status of AI research and developments, which has been solely based on model-centric AI (MC-AI) over the past 30 years. Until present, there exists very little knowledge about DC-AI, and its significance in terms of solving real-world problems remains unexplored in the recent literature. In this technical note, we present the core aspects of DC-AI and MC-AI and discuss their interplay when used to solve some real-world problems. We discuss the potential scenarios/situations that require the integration of DC-AI with MC-AI to solve challenging problems in AI. We performed a case study on a real-world dataset to corroborate the potential of DC-AI in realistic scenarios and to prove its significance over MC-AI when either data are limited or their quality is poor. Afterward, we comprehensively discuss the challenges that currently hinder the realization of DC-AI, and we list promising avenues for future research and development concerning DC-AI. Lastly, we discuss the next-generation computing for DC-AI that can foster DC-AI-related developments and can help transition DC-AI from theory to practice. Our detailed analysis can guide AI practitioners toward exploring the undisclosed potential of DC-AI in the current AI-driven era. Full article