# SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning
## 2 Literature Review  
The most straightforward and practical application of human activity recognition (HAR) involves using wearable devices to track individuals while they perform their daily activities. Humans perform many exercises every day, such as walking, running, football games, dozing, and eating, and are constantly expanding these exercises. Therefore, these data have an enormous significance in educating us about different parts of human existence and what they mean in people's lives. HAR has many applications and can be used in various domains, for example, smart cities [24], elderly and child care [11], physical rehabilitation [25], identifying criminal behavior and violence [26], real-time content delivery [13], surveillance [14], etc. The two types of sensors that are usually used for human activity recognition include visual sensors, such as cameras, which produce video as data, and non-visual sensors, such as accelerometers and gyroscope sensors, which generate numerical data. Machine learning strategies are on the leading edge and play a key role in HAR. Over time, these AI-based HAR techniques have improved the accuracy in prediction and working on complex information. RF, Support vector machine (SVM), decision tree, and DL are the most well-known decision-making strategies for HAR. [PERSON] et al. [27] focused on recognizing strange human practices. Unusual exercises are sudden occasions that occur arbitrarily. Human practices can be perceived using a variety of methods. The most commonly used method is multi-class SVM. [PERSON] et al. [27] proposed a new plan to address human practices in the form of a state progress table. The changing table helps keep the classifier away from the states that are inaccessible from the current state. By staying away from unreachable states, the computational time for grouping is radically reduced compared to that using conventional methods. Many classifiers face the constraints of a long training time and large feature vector size. [PERSON] and [PERSON] [28] proposed a method based on the SVM classifier, addressing the problems in human activity recognition using an existing spatio-temporal feature descriptor. A comparison of the system proposed in [28] with existing classifiers using two standard datasets shows that the system in [28] is much better in terms of computational time and either exceeds the existing recognition rates or is equal to it. Several other SVM-based methodologies have been proposed in the literature [29; 30; 31; 32], referred to for further information. Computing feature importance is a critical task in HAR problems, as it helps to decrease the features that do not hold any relevance, and removable features can increase classification performance. [PERSON] and [PERSON] [33] proposed a random forest-based feature importance method for HAR problems. The first step is to train a conventional RF algorithm on the HAR dataset to compute the feature importance. In the second step, the feature importance values are transferred to the directed RF algorithm. The authors used the directed RF algorithm because trees are not dependent on each other, and parallel computation decreases the training time and minimizes the prediction time. The algorithm developed only two ensembles and showed a high selectivity with a small sample. Further, this helped to maintain high prediction accuracy. In [33], five widely used HAR datasets were used, and the authors noted that the directed RF can find smaller feature sets while maintaining a high HAR accuracy. Different functions exist for further improving the HAR prediction, including like, display, and circle [34; 35; 36]. In recent years, deep learning has rapidly grown in all application domains (natural science, computer science, multimedia, networking, security, finance, etc.) due to its ability to efficiently understand complex and non-linear data [30]. Human actions need to be accepted as a specific activity that helps to identify different types of human development and behavior.