## 4 Non-Vision-Based HAR  
Recent research in HAR has shifted towards the non-vision-based approach [3], and a variety of non-vision sensors such as movement sensors are now popular for use in HAR [35]. This is because movement sensor technology is more low cost and considered less invasive [36]. Ambient sensors are also used in smart homes for HAR. There are also instances of the integration of non-vision sensors to enhance vision-based HAR [37]. These instances are also covered in Section 5.1. This section will review the recent papers focusing on non-vision sensor technology for HAR. Long Short-Term Memory (LSTM) models are popular for non-vision HAR tasks because they are designed to capture temporal dependencies in sequential data such as accelerometer and gyroscope data. [PERSON] et al. classified two activities (walking and brisk walking) using an LSTM model [38]. They collected their data using an accelerometer, gyroscope, and magnetometer, which were then used to train and test their model. They used different sensor combinations to train and test the model. Then, they compared the performance of the different combinations and found that the combination of acceleration and angular velocity had the highest accuracy of 96.5%. The sensor input of only magnetic field gave the lowest accuracy of 59.2%. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**S/N** & **Paper** & **Dataset** & **Model** & **Accuracy** & **Contribution to Knowledge** \\ \hline \multirow{4}{*}{3} & Si-GCN: & & & & & & Constructed \\  & Structure-induced & & & & & inter-graph of \\  & Graph Convolution & 1. & NTU RGB+D & & 1. & 89.05\% & mini-graphs of \\  & Network for & 2. & HDM05 & & 2. & 85.45\% & specific parts of the \\  & skeleton-based action & & & & & & human skeleton to \\  & recognition [28] & & & & & & show the interactions \\ \hline \multirow{4}{*}{4} & Decoupling GCN & 1. & NTU-RGBD & & 1. & X-sub: 90.8\% & \\  & with DropGraph & 2. & NTU-RGBD- & & & and x-view: & \\  & Module for & 120 & & & 96.6\%. & \\  & skeleton-based action & 3. & Northwestern- & & & & DropGraph (ADG) to \\  & recognition [26] & & & & & & relieve the prevalent \\  & & & UCLA & & & 88.1\%. & \\  & & & & & & & in GCNs. \\ \hline \multirow{4}{*}{5} & Inception Spatial & & & & & & Improved GCN and \\  & Temporal Graph & & & & & & TCN based on the \\  & Convolutional & & & & & & Inception structure \\  & Networks for & & & & & x-sub: 89.9\%, & using the idea of \\  & skeleton-based action & & & & x-view: 96.2\% & multiscale \\  & recognition [29] & & & & & convolution to better \\  & & & & & & extract spatial and \\  & & & & & & temporal features. \\ \hline \multirow{4}{*}{6} & 3D skeleton-based & & & & Average Mean Angle & Proposed a \\  & human motion & & & & Errors (MAE): & multiscale approach \\  & prediction using & & & & 1. & 0.19 for 80 ms & to spatial and \\  & dynamic multiscale & & & & & and 1.20 for \\  & spatiotemporal graph & & & & & 1000 ms & graph convolution \\  & recurrent neural & & & & 2. & 0.25 for 80 ms & units (MGCUs) to \\  & networks [30] & & & & & and 1.43 for \\  & networks [30] & & & & & 1000 ms & describe the human \\  & & & & & & body’s semantic \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._[PERSON] et al. proposed the use of a Bidirectional LSTM (BiLSTM) model for inertial-based HAR [39]. They collected data from 30 volunteers wearing a smartphone (Samsung Galaxy S II) on the waist using the embedded inertial sensors. Their proposed BiLSTM model had the best accuracy of 100% in identifying the laying-down position and 85.8% as its worst accuracy in identifying standing. They compared their model with other existing models and it performed competitively. In 2023, [PERSON] and [PERSON] proposed a one-dimensional pyramidal residual network (1D-PyramidNet) [21] which they based on [PERSON]'s deep pyramidal residual networks (DPRNs). The model was trained on the Daily Human Activity (DHA) dataset, and the results were compared with other baseline deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU).