A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

Abstract

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Keywords: artificial hydrocarbon networks; artificial organic networks; flexibility; flexible human activity recognition; supervised machine learning; wearable sensors.

Figure 1

Structure of an artificial hydrocarbon network using saturated and linear chains of molecules [26]. Throughout this work, the topology of the proposed classifier considers one hydrocarbon compound. Reprinted from Publication Expert Systems with Applications, 42 (22), Hiram Ponce, Pedro Ponce, Héctor Bastida, Arturo Molina, A novel robust liquid level controller for coupled tanks systems using artificial hydrocarbon networks, 8858–8867, Copyright (2015), with permission from Elsevier.

Figure 2

Diagram of the proposed artificial hydrocarbon network based classifier (AHN-classifier). First, reduced feature set is used to train the AHN-model, then it is used as AHN-classifier in the testing step.

Figure 3

Methodology implemented in the case study for HAR systems.

Figure 4

Location of the five wearable IMUs used in the dataset.

Figure 5

A subset of the first one-hundred components calculated by the PCA method: Variance values shown in straight line, and cumulative variance shown in dashed line.

Figure 6

Results of case 1: Confusion matrix of the AHN-classifier in the performance for all subjects using cross-validation. Numbers represent window counts.

Figure 7

Results of case 2: Confusion matrix of the AHN-classifier in the leave-one subject-out performance for user-independent. Numbers represent the average of window counts in the eight models.

Figure 8

Results of case 3: Confusion matrix of the AHN-classifier in the cross-validation within a subject performance for user-dependent. Numbers represent the average of window counts in the eight models.