Abstract
The recognition of abnormal signal of wearable sensor is of great significance to the application value of the device. In order to improve the accuracy of abnormal signal recognition of wearable sensors and indirectly ensure the safety of wearable sensor devices, a method of abnormal signal recognition of wearable sensors based on machine learning was proposed. According to the different abnormal types and principles of wearable sensors, the signal abnormal judgment criteria are set. The wearable sensor signal is collected, and the initial signal is preprocessed by Kalman filtering, normalization and weighted fusion. The machine learning algorithm is used to extract the features of sensor signals, and the recognition results of the abnormal type, abnormal semaphore and abnormal location of sensor signals are obtained through feature matching. Through the identification performance test experiment, it is obtained that the average abnormal type error detection rate of the optimization design identification method is 0.86%, and the average statistical error of abnormal semaphore is 0.22 db, lower than the preset value.
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2021 Anhui Provincial Natural Science Research Key Project (KJ2021A1384).
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, C., Zhang, X. (2023). Abnormal Signal Recognition Method of Wearable Sensor Based on Machine Learning. In: Wang, S. (eds) IoT and Big Data Technologies for Health Care. IoTCare 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-031-33545-7_23
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DOI: https://doi.org/10.1007/978-3-031-33545-7_23
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