## 4 Non-Vision-Based HAR
### HAR Using Deep Reinforcement Learning  
Deep Reinforcement Learning (DRL) has recently emerged as a promising alternative or addition to traditional supervised learning approaches to HAR. DRL is a combination of deep learning and reinforcement learning. Reinforcement learning is a reward-oriented learning technique that is based on trial and error. [PERSON] et al. proposed using DRL for data fusion in a multimodal system and performed an ablation study to show the effectiveness of this DRL-based data fusion [69]. [PERSON] et al. proposed using DRL in attention-aware sampling for HAR. Their proposed method involves training an attention model using DRL to identify the key frames in a video and discard the irrelevant ones [82]. They evaluated the performance of their method on the UCF101 and HMDB51 datasets and it performed competitively. In the same vein, [PERSON] et al. proposed a Multiagent Reinforcement Learning (MARL) framework that uses DRL to identify relevant frames for the task of HAR [83]. In their paper, they proposed using multiple agents, where each agent picks a series of frames encoded into a vector which are then fed into a policy network. When a frame is selected as relevant, a classification model then classifies the activity being carried out on that frame. They examined MARL with various architectures (BN-Inception, Inception-V3, ResNet-101, ResNet-152 and C3D) and it improved the overall accuracy of all the models. They also compared its performance on the ActivityNet, YouTube Birds and YouTube Cars datasets in comparison to other state-of-the-art models. They found that the method with ResNet-152 outperformed other models. In 2021, [PERSON] and [PERSON] proposed a Joint Selection Deep Reinforcement Learning (JSDRL) framework that selects the key joints in the skeleton data of a video and uses that for Human Activity Recognition [32]. It formulates the problem as a joint selection problem and uses DRL to find the best solution. It selects and filters out the relevant joints, thus enhancing the classifier's performance and reducing training time. They compared the performance of three models (BiLSTM, CNN, and decoupling graph Convolutional Neural Networks with dropGraph module (DCGCN)) with and without the JSDRL framework on the NTU-RGBD and UT-Kinect Datasets, and the models performed better with the framework than without. The framework achieved the highest accuracy when paired with DCGCN. [PERSON] et al. also proposed a DRL framework called the Dynamic Key Feature Selection Network (DKFSN) for feature extraction on time-series sequential data [84]. The DKFSN uses a reinforcement agent to select the best deep features to optimise recognition and \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}} \hline \hline
**S/N** & **Paper** & **Dataset** & **Model** & **Accuracy** & **Contribution to Knowledge** \\ \hline \multirow{8}{*}{8} & Deep learning-based multimodal complex & & & Proposed using different sub-networks to process fast-changing and simple, fast-changing complex and slow-changing data. \\  & Human Activity Recognition using wearable devices [11] & & & & Proposed using different sub-networks to process fast-changing and simple, fast-changing and complex and slow-changing data. \\ \hline \multirow{8}{*}{9} & Human Activity & & & & Proposed using different sub-networks to process fast-changing and simple, fast-changing and complex and slow-changing data. \\  & Recognition From multimodal Wearable Sensor Data Using Deep Multistage LSTM Architecture Based on Temporal Feature Aggregation [80] & & & Proposed individual LSTM streams for temporal extraction of each data type. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._disregard the ones that negatively affect recognition. The baseline classification model used for the framework is the BiLSTM with fully connected layers, and it was validated on the Opportunity and UCI HAR datasets.