# SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning
###### Abstract
The prediction accuracy of HAR algorithms has been significantly improved over the last decade due to the manufacturing of highly sophisticated, reliable, and accurate sensors and the development of specialized HAR methods that leverage machine learning and deep learning through the use of high-performance computing and big data technologies [17]. However, several challenges are still hindering the progress in improving HAR performance, mainly due to the emergence of fog and edge computing, which bring many new opportunities (low latency, dynamic and real-time decision making, etc.) but come with their challenges [18; 19; 20]. In this paper, we focus on addressing two important research gaps in HAR research. Firstly, most of the work on HAR is based on the use of a single classifier, such as neural networks, decision trees, bagging, and boosting. These classifiers can, individually, provide good performance for certain types of data patterns; however, they may fall short for specific other data patterns. Combining these classifiers' prediction probabilities together in ensembles is known to have improved classification performance [21; 22]. However, the selection of classifiers in ensembles requires a careful design process and is challenging. Secondly, data and the environment in which they are sensed change rapidly in real-world applications, and in these cases, frequent updates of the models are required based on the changing patterns in the data being sensed. Accordingly, to address the above-mentioned challenges, in this paper, we propose a human activity recognition method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies to improve the overall performance of the HAR method. First, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), and Random Forest (RF) classifiers using soft-voting. Secondly, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. Soft-voting helps to enhance accuracy, while self-training helps to classify the most challenging data instances. We investigate the performance of our proposed SVSL method using a dataset that is publicly available on the UCI data repository, provided by [PERSON] et al. [23] The data contain six human activities related to lying, sitting, and walking. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two the datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost, showing that SVSL produces a comparatively better classification for some activity classes, as DL, GLM, RF, and AdaBoost produce a higher number of misclassification instances (an observation that we plan to investigate further in the future, with the expectation of further success). We also show that the self-training mechanism of the proposed SVSL method increases the average prediction accuracy from 99.26% to 99.37% for dataset I, and 97.15%, to 97.59% for dataset II. The rest of the paper is organized as follows. A review of the relevant literature is given in Section 2. The proposed SVSL method is described in Section 3. In Section 4, the proposed SVSL method is evaluated using the provided results. Finally, in Section 5, we conclude the paper and provide a direction for future work.