Muniasamy Anandhavalli
Technol Health Care. 2025;33(1):395-409. doi: 10.3233/THC-241064.
A daily activity routine is vital for overall health and well-being, supporting physical and mental fitness. Consistent physical activity is linked to a multitude of benefits for the body, mind, and emotions, playing a key role in raising a healthy lifestyle. The use of wearable devices has become essential in the realm of health and fitness, facilitating the monitoring of daily activities. While convolutional neural networks (CNN) have proven effective, challenges remain in quickly adapting to a variety of activities.
This study aimed to develop a model for precise recognition of human activities to revolutionize health monitoring by integrating transformer models with multi-head attention for precise human activity recognition using wearable devices.
The Human Activity Recognition (HAR) algorithm uses deep learning to classify human activities using spectrogram data. It uses a pretrained convolution neural network (CNN) with a MobileNetV2 model to extract features, a dense residual transformer network (DRTN), and a multi-head multi-level attention architecture (MH-MLA) to capture time-related patterns. The model then blends information from both layers through an adaptive attention mechanism and uses a SoftMax function to provide classification probabilities for various human activities.
The integrated approach, combining pretrained CNN with transformer models to create a thorough and effective system for recognizing human activities from spectrogram data, outperformed these methods in various datasets - HARTH, KU-HAR, and HuGaDB produced accuracies of 92.81%, 97.98%, and 95.32%, respectively. This suggests that the integration of diverse methodologies yields good results in capturing nuanced human activities across different activities. The comparison analysis showed that the integrated system consistently performs better for dynamic human activity recognition datasets.
In conclusion, maintaining a routine of daily activities is crucial for overall health and well-being. Regular physical activity contributes substantially to a healthy lifestyle, benefiting both the body and the mind. The integration of wearable devices has simplified the monitoring of daily routines. This research introduces an innovative approach to human activity recognition, combining the CNN model with a dense residual transformer network (DRTN) with multi-head multi-level attention (MH-MLA) within the transformer architecture to enhance its capability.
日常活动规律对整体健康和幸福至关重要，有助于身心健康。持续的体育活动对身体、心理和情绪有诸多益处，在培养健康生活方式中发挥关键作用。可穿戴设备的使用在健康和健身领域已变得至关重要，便于监测日常活动。虽然卷积神经网络（CNN）已被证明有效，但在快速适应各种活动方面仍存在挑战。
本研究旨在开发一种用于精确识别人类活动的模型，通过将变压器模型与多头注意力相结合，利用可穿戴设备实现精确的人类活动识别，从而彻底改变健康监测。
人类活动识别（HAR）算法使用深度学习，通过频谱图数据对人类活动进行分类。它使用带有MobileNetV2模型的预训练卷积神经网络（CNN）来提取特征，一个密集残差变压器网络（DRTN）和一个多头多级注意力架构（MH-MLA）来捕捉与时间相关的模式。然后，该模型通过自适应注意力机制融合来自两层的信息，并使用SoftMax函数为各种人类活动提供分类概率。
将预训练的CNN与变压器模型相结合的集成方法，创建了一个用于从频谱图数据中识别人类活动的全面且有效的系统，在各种数据集上优于这些方法——在HARTH、KU-HAR和HuGaDB数据集上的准确率分别为92.81%、97.98%和95.32%。这表明不同方法的整合在捕捉不同活动中细微的人类活动方面产生了良好效果。比较分析表明，集成系统在动态人类活动识别数据集上始终表现更好。
总之，保持日常活动规律对整体健康和幸福至关重要。定期体育活动对健康生活方式有很大贡献，对身体和心理都有益。可穿戴设备的集成简化了日常活动的监测。本研究引入了一种创新的人类活动识别方法，将CNN模型与变压器架构内的密集残差变压器网络（DRTN）和多头多级注意力（MH-MLA）相结合，以增强其能力。
