Abstract
Human Activity Recognition is focused on the use of sensing technology to classify human activities and to infer human behavior. While traditional machine learning approaches use hand-crafted features to train their models, recent advancements in neural networks allow for automatic feature extraction. Auto-encoders are a type of neural network that can learn complex representations of the data and are commonly used for anomaly detection. In this work we propose a novel multi-class algorithm which consists of an ensemble of auto-encoders where each auto-encoder is associated with a unique class. We compared the proposed approach with other state-of-the-art approaches in the context of human activity recognition. Experimental results show that ensembles of auto-encoders can be efficient, robust and competitive. Moreover, this modular classifier structure allows for more flexible models. For example, the extension of the number of classes, by the inclusion of new auto-encoders, without the necessity to retrain the whole model.
Highlights
Human Activity Recognition (HAR) is a research field focused on the use of sensing technology to classify human activities and to infer human behavior [1]
We note that in the experiments that includes the deep learning models, we present the results of our method with incremental learning, called Ensemble of Auto-Encoders (EAE), and the model without incremental learning, called EAE_Off
As for the other models, the median accuracy is around 87%, their variance is larger than the variance of EAE
Summary
Human Activity Recognition (HAR) is a research field focused on the use of sensing technology to classify human activities and to infer human behavior [1]. In our previous work [7] we studied a semi-supervised ensemble, EkVN, which combined 3 different algorithms (kNearest Neighbour, Very Fast Decision Tree and Naive Bayes). This method relies on heuristic hand-crafted feature extraction for HAR. We found that the feature extraction process has a relatively high energy and time costs This can have implications, for example in mobile applications, where the use of resources must be carefully managed in order to keep the application efficiently working for long periods of time
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