# SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Avg. Accuracy (\%)** & **Disadvantages** & **Advantages** \\ \hline [PERSON] et al. [8] & 93 & Accuracy can be higher & Lightweight with low computational cost. \\ \hline [PERSON] et al. [9] & 91 & Accuracy can be higher, and smartphone position may vary. & Addresses two different problems in a single solution which are HAR and smartphone position recognition. \\ \hline [PERSON] et al. [11] & 93.5 & Smartphones need to be carried which is not practical always & Have life-saving healthcare application \\ \hline [PERSON] et al. [12] & 95.85 & Prediction accuracy must be higher, particularly for healthcare applications & Can recognize HAR and activities transitions \\ \hline [PERSON] et al. [13] & 87 & Tested on the small dataset & HAR concept used for adaptive content delivery \\ \hline [PERSON] et al. [24] & 97.1 & Validation needs to be performed on more extensive and diverse IoT datasets. & Better accuracy, memory efficiency, and relatively higher processing speed \\ \hline [PERSON] [25] & 82 & The proposed prototype is not suitable for the final confirmation of a performed activity. Additionally, further study is needed to investigate other features that will allow for improved activity differentiation. & A prototype of a battery-operated wearable health-tracking device that tracks body temperature and body motions \\ \hline [PERSON] et al. [26] & 74.1 & A personal reidentification approach to discriminate the owner from the thief is needed for enhancing the accuracy level, and work needs to be carried out to recognize complex activities. & System proposal for motorbike theft detection in video surveillance systems \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A Comparison of the Related Works. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Avg. Accuracy (\%)** & **Disadvantages** & **Advantages** \\ \hline \multirow{4}{*}{[PERSON] et al. [27]} & \multirow{4}{*}{94.4} & Data from environmental and physiological sensors are not considered. A varied form of the sensor can be used to understand the context information and the patient’s health condition to provide better assistance. & The computational time for classification is reduced significantly when compared to conventional approaches. The precision and sensitivity of the proposed system are better. \\ \hline \multirow{4}{*}{[PERSON] et al. [28]} & \multirow{4}{*}{100} & When the number of training examples is few due to an imbalance, the proposed system performs marginally inferior to the existing established system & The proposed system is superior in terms of computational time in terms of human activity recognition. \\ \hline \multirow{4}{*}{[PERSON] et al. [29]} & \multirow{4}{*}{90.6} & Accuracy level decreases with distance. & The proposed system can be used as home automation input for the home security system. \\ \hline \multirow{4}{*}{[PERSON] [30]} & \multirow{4}{*}{95.6} & Placement of sensors for correct detection is an issue, and there is no involvement of an unsupervised approach for automatic activity recognition. & The proposed system recognized a number of human activities like walking considerably, running, jumping, standing, sitting, and sleeping using only a single triaxial accelerometer. \\ \hline \multirow{4}{*}{[PERSON] et al. [31]} & \multirow{4}{*}{95.3} & Improvement in the action recognition score is needed, and decreasing the detection time. & The proposed approach maintains a good accuracy score even using limited frame numbers. \\ \hline \multirow{4}{*}{[PERSON] et al. [32]} & \multirow{4}{*}{99.9} & More considerable dataset validation is required, decrease the use of a number of sensors. The use of a more robust algorithm is needed. \\ \hline \multirow{4}{*}{[PERSON] et al. [33]} & \multirow{4}{*}{95} & More benchmark activity recognition data sets are needed for further validation of the study. & The proposed study allows parallel computing and offers low computational costs with high recognition accuracy.