Authors:
Yale Hartmann
;
Hui Liu
;
Steffen Lahrberg
and
Tanja Schultz
Affiliation:
Cognitive Systems Lab, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany
Keyword(s):
High-level Feature, Human Activity Recognition, Hidden Markov Model, Motion Unit, Few-shot Learning, Wearable Sensors.
Abstract:
This paper introduces and evaluates a novel way of processing human activities based on unique combinations
of interpretable categorical high-level features with applications to classification, few-shot learning, as well
as cross-dataset and cross-sensor comparison, combination, and analysis. Feature extraction is considered as
a classification problem and solved with Hidden Markov Models making the feature space easily extensible.
The feature extraction is person-independently evaluated on the CSL-SHARE and UniMiB SHAR datasets
and achieves balanced accuracies up from 96.1% on CSL-SHARE and up to 91.1% on UniMiB SHAR. Furthermore, classification experiments on the separate and combined datasets achieve 85% (CSL-SHARE), 65%
(UniMiB SHAR), and 74% (combined) accuracy. The few-shot learning experiments show potential with low
errors in feature extraction but require further work for good activity classification. Remarkable is the possibility to attribute errors and indicate opti
mization areas easily. These experiments demonstrate the potential
and possibilities of the proposed method and the high-level, extensible, and interpretable feature space.
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