# SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning
## 2 Literature Review
HAR uses information gathered from different types of sensors. [PERSON] et al. [12] proposed a deep learning-based method that can perceive two different exercises and transitions between them. This has a very important practical use, particularly for medical care applications. In [12], the authors first designed a deep convolutional neural (CNN) model to extract distinguishing parameters from the sensed data. Then, to capture the conditions of two different exercises, a long transient memory network was used. This step improved the HAR accuracy for two exercise activities. With the fusion of CNN and LSTM, a model was introduced for wearable devices that can precisely deduce exercises and switching one exercise to another. With the fusion of CNN and a long transient memory network, a model was introduced for wearable devices that can precisely deduce one exercise and then switching to another successfully. The test results showed that the proposed method is highly accurate, with a correct classification rate of up to 95.87% and a correct classification rate for changes of over 80%, which is superior to comparable HAR models. Another deep LSTM neural network method for HAR is introduced in [37], where IMU sensor data are used. DL techniques are used for the classification problem but perform very well for time-series problems too. [PERSON] et al. [38] examined the pros and cons of time series data augmentation to enhance the accuracy of DL models for HAR from smartphone-based accelerometer data. [PERSON] et al. critically analyzed Gated Recurrent Units, Long-Short Term Memory, and Vanilla and tested them using three publicly available HAR datasets. They used double cross arrangement information augmentation procedures and studied their effect on the accuracy of the objective model. The analysis proved that using gated intermittent units produces the best accuracy and preparation time results and enables long-transient memory processing. Furthermore, the results showed that the use of information extension essentially improves the quality of the acknowledgment. Similar to [8], [PERSON] et al. [39] focused on the importance of the low computational power of mobile devices while performing HAR using DL models. A very interesting framework of feature fusion is proposed by [PERSON] et al. [40], where handcrafted features are fused with automatically generated features through DL for HAR. HAR is of great importance when managing and controlling pandemics like that of COVID-19 today and in the future. Applications for contact tracing, social distancing, and information dissemination have grown significantly during COVID-19 to effectively manage and control the pandemic. Contact tracing applications help to determine the near history of an infected person, such as where the infected person went and whom the infected person met in the last week. On the other hand, social distancing applications assist in determining whether people follow social distancing guidelines. Location coordinates, proximity data, and HAR data play an important role in contact tracing and social distancing applications. Countries around the world have successfully used these applications. [PERSON] and [PERSON] [41] introduced a human movement classifier based on convolutional deep neural networks to improve the exposure of COVID-19 to the above-stated applications. Specifically, the raw information from a cell phone's accelerometer sensor was arranged to frame a picture, including some channels (HAR-images) used as fingerprints for progress action, which can be extrapolated to the following applications, constituting one of the contributions of the present study. The experimental results from examining anecdotal information revealed that HAR images are potent attractants for human action acceptance. The novel coronavirus devastated the world and forced researchers to find solutions for controlling and irradiating this infection.