Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study
This paper introduces a new unsupervised method for the clustering of physiological data into health states based on their similarity. We propose an iterative hierarchical clustering approach that combines health states according to a similarity constraint to new arbitrary health states. We applied method to experimental data in which the physical strain of subjects was systematically varied. We derived health states based on parameters extracted from ECG data. The occurrence of health states shows a high temporal correlation to the experimental phases of the physical exercise. We compared our method to other clustering algorithms and found a significantly higher accuracy with respect to the identification of health states.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSimilar Papers 제목 키워드 기반
Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification
The task of multiple people tracking in monocular videos is challenging because of the numerous difficulties involved: occlusions, varying environments, crowded scenes, camera parameters and motion. In the tracking-by-de…
ClusteringMultiple People TrackingPerson Re-IdentificationHierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification
For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate on the target dataset, which often focuse…
ClusteringDomain AdaptationPerson Re-IdentificationTriplet+2Energy Clustering for Unsupervised Person Re-identification
Due to the high cost of data annotation in supervised learning for person re-identification (Re-ID) methods, unsupervised learning becomes more attractive in the real world. The Bottom-up Clustering (BUC) approach based …
ClusteringDiversityPerson Re-IdentificationTransfer Learning+1A Novel Data Segmentation Method for Data-driven Phase Identification
This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exc…
ClusteringSKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning
Self-supervised learning (SSL) has achieved remarkable success across various speech-processing tasks. To enhance its efficiency, previous works often leverage the use of compression techniques. A notable recent attempt …
Knowledge DistillationSelf-Supervised Learning