Abstract:
Human activity recognition (HAR) is encountered in a plethora of applications, such as pervasive health care systems and smart homes. The majority of existing HAR techniq...Show MoreMetadata
Abstract:
Human activity recognition (HAR) is encountered in a plethora of applications, such as pervasive health care systems and smart homes. The majority of existing HAR techniques employs features extracted from symbolic or frequency-domain representations of the associated data, whilst ignoring completely the behavior of the underlying data generating dynamical system. To address this problem, this work proposes a novel self-tuned architecture for feature extraction and activity recognition by modeling directly the inherent dynamics of wearable sensor data in higher-dimensional phase spaces, which encode state recurrences for each individual activity. Experimental evaluation on real data of leisure activities demonstrates an improved recognition accuracy of our method when compared against a state-of-the-art motif-based approach using symbolic representations.
Date of Conference: 02-06 September 2019
Date Added to IEEE Xplore: 18 November 2019
ISBN Information: