Mixed Reality and Interaction Lab, Department of Software, Sejong University, Seoul 05006, Korea.
Sensors (Basel). 2022 Jan 1;22(1):323. doi: 10.3390/s22010323.
In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.
近年来，人体活动识别（HAR）已成为健康和人机交互领域中最重要的研究课题之一。许多基于人工智能的模型被开发用于活动识别；然而，由于这些算法无法提取空间和时间特征，因此在实际的长期 HAR 中表现不佳。此外，在文献中，可用于物理活动识别的公开数据集数量有限，其中包含的活动数量较少。考虑到这些限制，我们通过结合卷积神经网络（CNN）和长短时记忆网络（LSTM）来开发一种混合模型，用于活动识别，其中 CNN 用于提取空间特征，LSTM 网络用于学习时间信息。此外，还生成了一个新的具有挑战性的数据集，该数据集是使用 Kinect V2 传感器从 20 名参与者那里收集的，包含 12 种不同类别的人体物理活动。对不同的传统机器学习和深度学习模型进行了广泛的消融研究，以获得 HAR 的最佳解决方案。通过 CNN-LSTM 技术实现了 90.89%的准确率，表明所提出的模型适用于 HAR 应用。
