71,870 research outputs found
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.Comment: 39th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC
Bio-inspired broad-class phonetic labelling
Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed
Early Recognition of Human Activities from First-Person Videos Using Onset Representations
In this paper, we propose a methodology for early recognition of human
activities from videos taken with a first-person viewpoint. Early recognition,
which is also known as activity prediction, is an ability to infer an ongoing
activity at its early stage. We present an algorithm to perform recognition of
activities targeted at the camera from streaming videos, making the system to
predict intended activities of the interacting person and avoid harmful events
before they actually happen. We introduce the novel concept of 'onset' that
efficiently summarizes pre-activity observations, and design an approach to
consider event history in addition to ongoing video observation for early
first-person recognition of activities. We propose to represent onset using
cascade histograms of time series gradients, and we describe a novel
algorithmic setup to take advantage of onset for early recognition of
activities. The experimental results clearly illustrate that the proposed
concept of onset enables better/earlier recognition of human activities from
first-person videos
- âŠ