## 3 Vision-Based HAR  
Vision-based HAR is one of the earlier approaches to HAR [3]. It involves applying computer vision techniques to the task of HAR. It usually requires the use of images acquired by visual sensing technologies. It focuses on recognising appearance-based behaviour [22]. With the advancement in computer vision, research on vision-based HAR has also become popular. This section will review recent HAR papers pertaining to computer vision. The use of the Convolutional Neural Network (CNN) is popular in computer vision problems, and this is no different in HAR. Researchers have proposed different versions of CNN models and also in combination with other models. In 2020, [PERSON] and [PERSON] proposed a feature extraction approach that combines Scale-Invariant Feature Transform (SIFT) and optical flow computation, and then used a CNN-based classification approach [23]. They analysed the model's performance on the Weizmann and KTH datasets, and it had an accuracy of 98.43% and 94.96% respectively. [PERSON] et al. proposed using a Temporal Convolutional Neural Network (TCNN) which leverages spatiotemporal features for vision-based HAR using only short videos [24]. Their proposed model is based on a feature extraction module that contains a three-dimensional convolutional layer and a two-dimensional LSTM layer and then two fully connected layers, which are used for classification. They used the Kinect Activity Recognition Dataset (KARD), Cornell Activity Dataset (CARD-60), and the MSR Daily Activity 3D Dataset to evaluate the performance of their model. They used only the RGB images from these datasets. The model achieved 100% accuracy for the KARD and CAD-60 datasets and 95.6% accuracy for the MSR Daily Activity 3D dataset. They also compared their results with those of other papers and showed that their model performed better. In 2023, [PERSON] et al. proposed the use of a hybrid CNN with LSTM model for vision-based HAR [25]. Their proposed model uses CNN for filtering features and LSTM for sequential classification, and then fully connected layers are used for feature mapping. They trained and validated their model on the UCF50 dataset, and it had an accuracy of 90.93%, which was better than the CNN and LSTM models. Some researchers have proposed using skeleton data for HAR due to its robustness to circumstance and illumination changes [26]. Graphical Convolutional Networks (GCNs) are favoured by researchers for this task [27]. In 2019, [PERSON] et al. proposed the structure-induced GCN [28] which carries out spectral graph convolution on a constructed inter-graph of mini-graphs of specific parts of the human skeleton. They evaluated their model on the NTU RGB+D and HDM05 datasets, and it had an accuracy of 89.05% and 85.45%, respectively. In 2020, [PERSON] et al. proposed a Decoupling GCN (DC-GCN), which uses a decoupling aggregation mechanism and DropGraph instead of dropout for regularisation [26]. They validated their model on the NTU-RGBD, NTU-RGBD-120, and Northwestern-UCLA datasets and carried out ablation studies to show the efficacy of the DC-GCN. The result of their experiments showed that the DC-GCN exceeds the performance of other graph models with less computational cost. In 2022, [PERSON] et al. proposed a novel Inception Spatial Temporal GCN (IST-GCN), which they trained and validated on the NTU RGB+D [29]. The model uses multiscale convolution to improve the GCN based on the inception structure. The model had an accuracy of 89.9% using x-subject validation and 96.2% using x-view, which is better than other skeleton HAR models. More research has also been performed with the combination of skeleton data with other modalities of data. Most recently in 2024, [PERSON] et al. proposed a Dynamic Multiscale Spatiotemporal Graph Recurrent Neural Network (DMST-GRNN), which uses multiscale graph convolution units (MGCUs) to represent the interconnections of the human body [30].