Authors:
Yale Hartmann
;
Hui Liu
and
Tanja Schultz
Affiliation:
Cognitive Systems Lab, University of Bremen, Germany
Keyword(s):
Human Activity Recognition, Biosignals, Multi-channel Signal Processing, Feature Space Reduction, Stacking.
Abstract:
In this paper, we study the effect of Feature Space Reduction for the task of Human Activity Recognition
(HAR). For this purpose, we investigate a Linear Discriminant Analysis (LDA) trained with Hidden Markov
Models (HMMs) force-aligned targets. HAR is a typical application of machine learning, which includes finding a lower-dimensional representation of sequential data to address the curse of dimensionality. This paper
uses three datasets (CSL19, UniMiB, and CSL18), which contain data recordings from humans performing
more than 16 everyday activities. Data were recorded with wearable sensors integrated into two devices, a
knee bandage and a smartphone. First, early-fusion baselines are trained, utilizing an HMM-based approach
with Gaussian Mixture Models to model the emission probabilities. Then, recognizers with feature space
reduction based on stacking combined with an LDA are evaluated and compared against the baseline. Experimental results show that feature space reductio
n improves balanced accuracy by ten percentage points on
the UniMiB and seven points on the CSL18 datasets while remaining the same on the CSL19 dataset. The
best recognizers achieve 93.7 ± 1.4% (CSL19), 69.5 ± 8.1% (UniMiB), and 70.6 ± 6.0% (CSL18) balanced
accuracy in a leave-one-person-out cross-validation.
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