Authors:
Yale Hartmann
;
Hui Liu
and
Tanja Schultz
Affiliation:
Cognitive Systems Lab, University of Bremen, Germany
Keyword(s):
Human Activity Recognition, Biosensors, Multi-channel Signal Processing, Feature Space Reduction, Stacking.
Abstract:
This work describes the implementation, optimization, and evaluation of a Human Activity Recognition (HAR) system using 21-channel biosignals. These biosignals capture multiple modalities, such as motion and muscle activity based on two 3D-inertial sensors, one 2D-goniometer, and four electromyographic sensors. We start with an early fusion, HMM-based recognition system which discriminates 18 human activities at 91% recognition accuracy. We then optimize preprocessing with a feature space reduction and feature vector stacking. For this purpose, a Linear Discriminant Analysis (LDA) was performed based on HMM state alignments. Our experimental results show that LDA feature space reduction improves recognition accuracy by four percentage points while stacking feature vectors currently does not show any positive effects. To the best of our knowledge, this is the first work on feature space reduction in a HAR system using various biosensors integrated into a knee bandage recognizing a div
erse set of activities.
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