Human Activity Recognition (HAR) with artificial intelligence fosters the development of innovative solutions. However, building AI models often requires a substantial amount of data and can be time-consuming. In this context, our work adopted the TimeGAN technique for data augmentation, facilitating the construction of a more efficient model. We developed a classifier that integrates both synthetic and real data. This strategy significantly reduces the time required for data collection and may accelerate the development of new wearable technologies. This approach represents a promising step in optimizing development processes in AI applications for HAR, enhancing the speed and effectiveness of technological innovation.

Simulating real-world activities in virtual environments with high fidelity is crucial for advancing training, simulation, and data analysis. This capability is particularly advantageous for complex, hazardous, or impractical tasks in real settings, as it enables extensive investigation without compromising safety or feasibility. This paper introduces a system for capturing and virtually simulating walking and running motions by utilizing gyroscope data to monitor the rotational dynamics of the lower limbs. Our approach provides an accurate recording of these movements, supporting a detailed biomechanical analysis. Our findings demonstrate that specialized sensors, as opposed to general-purpose devices like smartwatches, yield more precise, targeted data, thereby enhancing the quality of movement analysis.We also consider the potential of integrating multimodal data, such as combining motion sensors with image-based analysis for enhanced human activity recognition. Although not yet implemented, this integration offers a promising direction for future system improvements, aiming to increase the scope and precision of activity recognition in virtual environments. Our system lays foundational work for advanced human data capture devices, creating a link between the physical and virtual realms. This enables comprehensive studies of biomechanics with potential applications in fields like sports science and orthopedics.

In this century, smart devices are increasingly present in our lives, such as at work, sports, or household chores. In this context, we have wearable devices that can help people with health monitoring or physical performance in sports activities. With the integration of artificial intelligence (AI), these wearable devices can identify injuries in athletes or care for the elderly in rehabilitation from human activity recognition (HAR). AI techniques are commonly applied for pattern recognition, such as image classification or HAR. In this context, we seek to develop a smart wearable device to recognize walking activities. In order to improve the identification of these tasks through AI algorithms, we propose the fusion of data between four sensors called SPUs. Each SPU has NodeMCU ESP-32 and BNO080 IMU hardware in its architecture. The data from these hardware provides information in high precision. A zero W raspberry pi collected this information. After extracting and manipulating this data, we trained a deep learning model. The model accuracy was higher than 92% reaching an overall accuracy of 97%. Therefore, the smart wearable device showed a new tool for recognizing walking activity, which could be applied in the future to recognize more complex tasks.