A Self-Tuned Architecture for Human ActivityRecognition Based on a Dynamical RecurrenceAnalysis of Wearable Sensor Data

Human activity recognition (HAR) is encountered ina plethora of applications, such as pervasive health care systemsand smart homes. The majority of existing HAR techniquesemploys features extracted from symbolic or frequency-domainrepresentations of the associated data, whilst ignoring completelythe behavior of the underlying data generating dynamical system.To address this problem, this work proposes a novel self-tunedarchitecture for feature extraction and activity recognition bymodeling directly the inherent dynamics of wearable sensordata in higher-dimensional phase spaces, which encode staterecurrences for each individual activity. Experimental evaluationon real data of leisure activities demonstrates an improved recog-nition accuracy of our method when compared against a state-of-the-art motif-based approach using symbolic representations.