点击次数：

影响因子：5.4

DOI码：10.3390/bios12121182

所属单位：NOVA School of Science and Technology; University of Bremen

发表刊物：Biosensors

刊物所在地：Switzerland

关键字：automatic segmentation; unsupervised segmentation; novelty function; human activity recognition; biosignal processing; self-similarity matrix; clustering; information retrieval; data mining

摘要：Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.

全部作者：João Rodrigues*†, Hui Liu*†, Duarte Folgado†, David Belo†, Tanja Schultz, Hugo Gamboa*

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：12

期号：12

页面范围：1182

是否译文：否

发表时间：2022-12-19

收录刊物：SCI

发布刊物链接：https://www.mdpi.com/2079-6374/12/12/1182