Human activity learning for assistive robotics using a classifier ensemble

Abstract

Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers-support vector machines (SVMs), K -nearest neighbour and random forest-is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset-the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model

“…The accuracy of this method reached 77.3%. Recently, Cippitelli et al [28] in 2016 and David et al [12] in 2018 developed new approaches for activity classification using RGBD sensors and Support Vector Machine. The average activity accuracy levels obtained were, respectively, 93.5% and 92.3%.…”

“…Dynamic Bayesian Mixture Model [23] 2014 91.9% Support Vector Machine + Hidden Markov Model [26] 2015 77.3% Multiclass Support Vector Machine [25] 2016 93.5 Classifier Ensemble [12] 2018 92.3% Weighted 3D joints [41] 2019 94.4% Our System 2020 95.5%…”

“…An equally important component of human activity is the assistive robotic field. David et al, 2019 [12] used the robots to learn human actions by extracting descriptive information about their activity in order to classify them. In this process, activities are integrated into the context of daily living, and normal human activities in indoor positioning are assessed.Based on these methods, we applied three methods in this work: the traditional method for human tracking, skeleton joints, and a deep learning model for human presentation and activity classification.…”

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

“…The supervised learning model usually selects class label information from the same class, ignoring with-in class image variability and thereby degrading feature selection performance. Some contributions in the image classification field follow: Adama et al [23] proposed a methodology for human activity recognition (HAR) for assistive robotics. Eleven features are acquired from an RGB-depth sensor and redundant features are removed by an ensemble of three classifiers, namely, Support Vector Machine, K-nearest neighbor, and Random Forest.…”

A deep learning based wearable system for food and drink intake recognition

“…The rapid technological development in ubiquitous computing seen in recent years is translating into an increasing research attention towards Human Activity Recognition (HAR) (Lee et al 2011;Gayathri et al 2015;Adama et al 2018;Ortega-Anderez et al 2019;Anderez et al 2020;Casella et al 2020). Current portable or wearable devices such as smart-phones and smart-watches already integrate a broad array of sensors (i.e.…”