## 4 Non-Vision-Based HAR
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**S/N**} & \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Sensors**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & **Contribution to** \\  & & & & & & **Knowledge** \\ \hline \multirow{8}{*}{3} & Efficient Recognition & & & & & & Introduced \\  & of Complex Human & & & & & the 1D-PyramidNet \\  & Activities Based on & & & & & model which uses an \\  & Smartwatch Sensors & DHA & Accelerometer & PyramidNet & & & incremental strategy \\  & Using Deep & & & & & for feature \\  & Pyramidal Residual & & & & & map expansion. \\  & Network [21] & & & & & \\ \hline \multirow{8}{*}{4} & An Intelligent Robust & & & & & & \\  & One Dimensional & & & & & \\  & HAR-CNN Model for & & & & & \\  & Human Activity & & & & & & \\  & Recognition using & & & & & \\  & Wearable Sensor & & & & & \\  & Data [37] & & & & & \\ \hline \multirow{8}{*}{5} & Smartwatch-based & & & & & & Proposed a 2-layer \\  & Human Activity & & & & & CNN-LSTM \\  & Recognition Using & & & and gyroscope & model & 96.2\% & and tuned the \\  & Hybrid LSTM & & & & & & \\  & Network [40] & & & & & \\ \hline \multirow{8}{*}{6} & Enhanced Complex & & & & & \\  & Human Activity & & & & & \\  & Recognition System: & & & & & \\  & A Proficient Deep & New HAR & & & & \\  & Learning Framework & dataset using & & & & \\  & Exploiting & EMG sensors & 8-channel & 1D & 83\% & \\  & Physiological Sensors & & & & & \\  & and Feature & & & & & \\  & Learning [44] & & & & & \\ \hline \multirow{8}{*}{7} & An Efficient and & & & & & Proposed a Conv1D \\  & Lightweight Deep & New & & & & & based CNN-LSTM \\  & Learning Model for & Calibrated & & & & model and developed \\  & Human Activity & Dataset, & & & & \\  & Recognition on Raw & MotionSense & & & & \\  & Sensor Data in & & & & & \\  & Uncontrolled & datasets. & & & & \\  & Environment [41] & & & & & \\ \hline \multirow{8}{*}{8} & Compressed Deep & & & & & Introduced a \\  & Learning Model For & & & & & multi-input \\  & Human Activity & & & & & CNN-LSTM model \\  & Recognition [42] & & & & & with dual \\ \hline \multirow{8}{*}{9} & Wireless body area & & & & & \\  & sensor networks & & & & & \\  & based Human & & & & & \\  & Activity Recognition using deep & & & & & \\  & learning [43] & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
include K-Nearest Neighbour, Random Forest, Support Vector Machine, Hidden Markov and Gaussian Mixture [55]. The 2010s saw the start of research on deep learning for HAR. Researchers turned to deep learning algorithms which automate feature extraction to reduce the time for preprocessing and improve the accuracy of HAR models. Deep learning architectures, particularly Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks, have gained significant traction in the field of HAR [53]. These deep learning algorithms can automatically learn abstract patterns from raw sensor data during training, eliminating the need for manual feature extraction. They are also able to handle more complex activities effectively. However, they require a large amount of data for training. Over time, different deep learning algorithms have been proposed for the task of HAR, such as DenseNet [35], TCNN [24], CNN-LSTM models [25; 42; 44], DC-GCN [26], IST-GCN [29], DMST-GRNN [30], and DEBONAIR [11]. In recent years, in addition to DL algorithms, there has been some research using multimodal techniques, DRL and LLMs. This section will focus on these three recent algorithms.