[SSM Algorithm] Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation
Date:2025-05-17 Hits:
Impact Factor:5.4
DOI Number:10.3390/bios12121182
Affiliation of Author(s):NOVA School of Science and Technology; University of Bremen
Journal:Biosensors
Place of Publication:Switzerland
Key Words:automatic segmentation; unsupervised segmentation; novelty function; human activity recognition; biosignal processing; self-similarity matrix; clustering; information retrieval; data mining
Abstract: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.
All the Authors:João Rodrigues*†, Hui Liu*†, Duarte Folgado†, David Belo†, Tanja Schultz, Hugo Gamboa*
Indexed by:Journal paper
Discipline:Engineering
Document Type:J
Volume:12
Issue:12
Page Number:1182
Translation or Not:no
Date of Publication:2022-12-19
Included Journals:SCI
Publication links:https://www.mdpi.com/2079-6374/12/12/1182