| 摘要(英) |
Physiological Signal: are defined as multichannel readings from the central and autonomic nervous system that carry meaningful information in terms of actions, responses, feelings, and behavior. (Taken from Computer Science Review, 2021). They are collected through various monitoring devices using time series sampling methods to provide data sources for the exploration and observation of physiological states required for activities such as medical care, caregiving, and medical research.
In recent years, various innovative monitoring methods, different from traditional ECG/PPG/sphygmomanometers, have emerged. Particularly, the development of non-contact monitoring applications has garnered significant attention. These technological solutions are not intended to replace traditional medical devices but rather emphasize their application in daily life and long-term care scenario. The goal is to achieve "continuous" and "zero-restriction" collection of physiological signals, highlighting their value for "potential risk, early detection," and "anytime, anywhere, just in case" use. However, these monitoring methods are susceptible to interference from the external environment and the autonomous activities of the elder people or patients. To successfully implement these methods in practical use, an appropriate signal algorithm is crucial. Different hardware sensing solutions have different signal characteristics, often requiring the development of specialized algorithms. This research aims to explore the possibility of developing a more generic algorithm framework that is both "simple" and "compatible" with various detection schemes, reducing the development barrier for users. As the performance and cost of hardware sensing solutions in the market continue to optimize, applying an easy-to-use and meaningful analytical algorithm can expedite the commercialization process and diversity in the industry, benefiting more end users.
The monitoring and analysis of physiological signals fundamentally relies on the "periodicity" and "amplitude" of the signal expression. The simple data transformation adopted in this study faithfully represents these two characteristics while pursuing greater compatibility. The selected test scheme involves a low-frequency 2.4GHz physiological radar product, detecting respiratory rate and heart rate. Although its frequency band is highly susceptible to interference, it allows for a greater installation distance compared to high-frequency products, reaching up to 2 meters. Despite some signal fundamental frequency offset situations, experimental results demonstrate that the average error rate for respiratory calculations can be within 2 rpm, and the average error rate for heart rate can be within 8 bpm. Additionally, using a simple conversion formula with the same data, it is possible to successfully identify three states: "disturbance," "presence of physiological signal," and "absence of physiological signal," providing crucial risk indicators for clinical care.
Finally, using the a forementioned data transformation combined with a basic neural network MLP (Multi-Layer Perceptron) architecture, modeling for two signal types, including "presence of physiological signal" and "absence of physiological signal," can be successfully completed. The test set achieved an optimal classifier performance of 98%, indicating potential future applications in establishing models for specific physiological features and risk warnings for vulnerable groups. This proves that the research results have the expected advantages of being a versatile, simple, and highly compatible method for multiple uses. |
| 參考文獻 |
參考文獻
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