## 3 Vision-Based HAR
Their model is an encoder-decoder framework with the MGCUs as encoders and Graph-Gated Recurrent Unit Lite (GGRU-L) used as the decoder. They trained and validated their model on the Human3.6M and CMU Mocap datasets. Average mean angle errors were used to measure the accuracy of the model, and it outperformed baseline models for both datasets. Vision-based HAR has experienced better accuracy in recent multimodal research, which uses RGB-D data rather than just RGB data [31]. RGB-D images, which provide more data such as depth information, other data such as the skeleton image, and additional context information, have also been explored by researchers. The prevalence of depth cameras like the Microsoft Kinect has also increased the availability of multimodal vision data [32]. These multimodal approaches are reviewed in Section 5.1. Vision-based HAR has also been known to face some issues such as occlusion, privacy issues and the effect of lighting conditions, which is one of the reasons for the growth in popularity of non-vision HAR approaches. Table 2 summarises the recent literature concerning vision-based HAR. There are some commonly used performance metrics for evaluating classification algorithms. They include confusion matrix, accuracy, recall, precision, F1-score, and specificity. Most of the papers reviewed used accuracy to measure the performance of their model. Hence, for the purpose of comparison, accuracy is used to compare the different models reviewed. The accuracy of a model measures the ratio of correctly predicted outputs to the total number of outputs. The formula for accuracy is given as  
\[(Accuracy=(TP+TN)/(TP+TN+FP+FN)) \tag{1}\]  
where TP is True Positives, TN is True Negatives, FP is False Positives and FN is False Negatives. Ref. [24] proposed a Temporal Convolutional Neural Network and achieved 100% accuracy on the KARD dataset, while [25] proposed a hybrid CNN with LSTM model and achieved an accuracy of 90.93%. There has also been some research on HAR using skeleton data, and one of the common datasets for evaluating these models is the NTU RGB+D. It has two validation views: x-subject and x-view. In x-subject, the validation data are collected from different subjects from those of the training data, while in x-view, the training and validation data are obtained from various camera views [33]. The DCGCN model [26] achieved an accuracy of 90.8% on the x-subject validation, while IST-GCN model [29] obtained an accuracy of 89.9% on the same. On the x-view validation of NTU RGB+D, the DC-GCN model had an accuracy of 96.6%, while IST-GCN had an accuracy of 96.2%. In real-world environments, vision HAR faces challenges like changing light conditions, activities that look similar (like walking vs. jogging), and actions happening at different speeds. Factors such as illumination and lighting environment can affect image quality, which is the foundation of vision-based HAR [31]. HAR can be difficult when there are distractions or movement in the background. These factors can make it hard for the models to work as well as they would in controlled settings [34]. There is also the issue of privacy and the invasive nature of vision sensors. Due to these issues, non-vision sensors have become popular for HAR tasks. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \begin{tabular}{} \end{tabular}  \\ \cline{1-1} \cline{3-6}  & A novel approach for