# SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning
## 5 Conclusions  
With the exponentially growing number of smart devices today, we can sense and record data that we could not even imagine a decade ago. We can benefit from wearable devices and smartphones through sensing, storing, and processing the stored data. One of the important and popular uses of this type of data is in HAR. This is a problem of great interest due to its wide array of applications, which include but are not limited to healthcare, surveillance, adaptive content delivery, and tracking. The use of wearable technology is rapidly increasing, and its effects have been positively observed by users in relation to follow-up healthcare appointments.  
In this paper, we identified crucial research gaps in the area of HAR and investigated them. Firstly, the individual classifiers belong to a particular family of algorithms, such as neural networks, decision trees, bagging, and boosting. However, each of them can perform efficiently for certain types of data patterns, with associated weaknesses. Secondly, data and the environment in which they are sensed are dynamic, and there is a need to frequently update the models according to the changing sensed data patterns.  
In this paper, we addressed the problem of HAR. Machine-learning algorithms play a significant role in developing our understanding of HAR data and applying the acquired knowledge to various application domains. We proposed a Soft-Voting and Self-Learning (SVSL)-based HAR method, which uses a soft-voting and self-learning mechanism to classify human activities. The SVSL method produced better results for dataset-I than the four other state-of-the-art classifiers: DL, GLM, RF, and AdaBoost. SVSL outperformed GLM and AdaBoost by almost 9%, and 3% and had a prediction accuracy that was more than 1% higher than that of DL and RF, respectively. Similar prediction accuracy patterns have been seen for dataset-II. We also noticed that the SVSL method had an improved prediction  
Figure 11: Prediction accuracy (%) of the SVSL methods, RF, GLM, DL, and AdaBoost.  
Figure 12: Training and testing times for all the methods in seconds.  
accuracy for the classes where the other three state-of-the-art classifiers produced a higher misclassification. Further, the average accuracies for both datasets increased by 0.11% and 0.44% after five executions of the self-training mechanisms for the proposed method.  
In the future, we plan to work toward enhancing the performance of the proposed method using additional diverse datasets. Additionally, we planned to develop an automated system to collect HAR data. This area has an enormous real-world application scope and must be exploited for the sake of better understanding human behaviors and our surroundings, which will enhance decision making.  
This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, under Grant No. RG-1439-311-10. The authors, therefore, acknowledge with thanks DSR for technical and financial support.  
Not applicable.  
The dataset used in this work is publicly available and is provided by the UCI data repository.  
The authors declare no conflict of interest.