Unobtrusive analysis of human behavior in task-based group interactions
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Authors
Bhattacharya, Indrani
Issue Date
2019-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Electrical engineering
Alternative Title
Abstract
Thirdly, we present a method of estimating the seated body posture and armpose of the participants from the higher resolution ceiling mounted ToF sensors. For body posture estimation, we extract features from the elevation profile of each individual and train a Support Vector Machine (SVM) classifier for classifying leaning forward vs. leaning backward classes. For armpose classification, we use transfer learning on the Mask R-CNN architecture followed by a rule-based algorithm to classify if the arms are together, crossed, on table, or touching the face. We use the recorded audio information to extract non-verbal speech patterns, acoustic features, and discussion content.
Description
August 2019
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY