Special Issue on Deep Learning for Human Activity Recognition
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
Human activity recognition (HAR) can benefit various applications, such as health-care services and smart home applications. Many sensors have been utilized for human activity recognition, such as wearable sensors, smartphones, radio frequency (RF) sensors (WiFi, RFID), LED light sensors, cameras, etc. Owing to the rapid development of wireless sensor network, a large amount of data has been collected for the recognition of human activities with different kind of sensors. Conventional shallow learning algorithms, such as support vector machine and random forest, require to manually extract some representative features from large and noisy sensory data. However, manual feature engineering requires export knowledge and will inevitably miss implicit features.
Recently, deep learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn representative features from massive data. This technology can be a good candidate for human activity recognition. Some initial attempts can be found in the literature. However, many challenging research problems in terms of accuracy, device heterogeneous, environment changes, etc. remain unsolved.
This special collection intends to prompt state-of-the-art approaches on deep learning for human activity recognition. All submitted papers will be peer-reviewed and selected based on both their quality and relevance.
Potential topics include but are not limited to:
Device-based HAR using deep learning
Device-free HAR using deep learning
Image based HAR using deep learning
Light sensor based HAR using deep learning
Sensor fusion for HAR using deep learning
Fusion of shallow models with deep networks for HAR