What is it about?
The rapid development of wearable technology provides new opportunities for action data processing and classification techniques. Wearable sensors can monitor the physiological and motion signals of the human body in real-time, providing rich data sources for health monitoring, sports analysis, and human-computer interaction. This paper provides a comprehensive review of motion data processing and classification techniques based on wearable sensors, mainly including feature extraction techniques, classification techniques, and future development and challenges. First, this paper introduces the research background of wearable sensors, emphasizing their important applications in health monitoring, sports analysis, and human-computer interaction. Then, it elaborates on the work content of action data processing and classification techniques, including feature extraction, model construction, and activity recognition. In feature extraction techniques, this paper focuses on the content of shallow feature extraction and deep feature extraction; in classification techniques, it mainly studies traditional machine learning models and deep learning models. Finally, this paper points out the current challenges and prospects for future research directions. Through in-depth discussions of feature extraction techniques and classification techniques for sensor time series data in wearable technology, this paper helps promote the application and development of wearable technology in health monitoring, sports analysis, and human-computer interaction.
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Why is it important?
The rapid development of technology and the continuous progress of society have made wearable technology one of the most concerned fields in recent years. Wearable devices such as smart watches, smart glasses, health trackers, and smart clothing have gradually integrated into our lives, not only providing us with many conveniences but also giving us more opportunities to obtain data about ourselves and our surrounding environment. Sensors in these devices collect various types of data, including images, sounds, motions, physiological parameters, time series data, etc.. Among them, time series data is a particularly challenging type of data because they usually contain a large amount of time series information and require complex processing and analysis techniques to extract useful information.
Perspectives
The rise of wearable sensor technology has greatly benefited from the continuous progress of sensors and the improvement of computing power and storage capacity. Modern wearable sensors are capable of capturing multiple data types, including acceleration, gyroscope, heart rate, skin conductance, temperature, and so on. These sensors monitor various physiological and environmental parameters of the human body in a non-invasive manner, providing strong support for applications such as health monitoring, sports analysis, and the improvement of quality of life. However, the data generated by these sensors is typically multi-channel, high-dimensional time series data with large data volume and complexity. Therefore, the classification techniques for wearable sensor time series data have aroused widespread research interest and received extensive attention in both academia and industry. The efficient processing and analysis of these data to achieve accurate Human Activity Recognition (HAR) has become a challenge.
Richard (Ricky) Smith Jr.
Read the Original
This page is a summary of: A Survey of Motion Data Processing and Classification Techniques Based on Wearable Sensors, December 2023, IgMin Publications Inc.,
DOI: 10.61927/igmin123.
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