## 4 Non-Vision-Based HAR
The authors used the Bayesian optimisation approach to fine-tune the hyper-parameters of the models being compared. The 1D-PyramidNet achieved an accuracy of 96.64% and an F1-score of 95.48%, which was higher than the other baseline models. Following the successes in using CNN models in vision-based HAR, researchers have proposed adjusting CNN models or combining them with other models to accommodate the nature of non-vision/time series data. In 2022, [PERSON] et al. proposed the use of a one-dimensional Convolutional Neural Network (1D-CNN) for non-vision based HAR [37]. They collected and processed the Wireless Sensor Data Mining (WISDM) dataset using a wearable sensor. They trained and validated their model on the dataset and it showed high accuracy, precision, recall, and F1-score. In 2020, [PERSON] and [PERSON] proposed using a CNN-LSTM model which eliminates the need for the manual extraction of features [40]. They also used Bayesian optimisation to tune the hyper-parameters and trained their model on the WISDM dataset. They compared the performance of their model with that of baseline CNN and LSTM models, and it performed better with an accuracy of 96.2%. In 2023, [PERSON] and [PERSON] proposed a 1D Convolution-based CNN-LSTM model [41]. They used their own calibrated dataset and the MotionSense and mHealth datasets to train and test the model. They also compared their model with baseline ANN and LSTM models, and their model performed better with an accuracy of 98% on their calibrated dataset. On the MotionSense dataset, it achieved 99% accuracy and 99.2% on the mHealth dataset. [PERSON] et al., in 2024, modified the 1D CNN-LSTM by making it a two-stream network [42], and their hybrid model achieved 99.74% accuracy on the WISDM dataset. In 2024, [PERSON] et al. proposed a hybrid HAR system that combines Gramian Angular Field (GAF) with DenseNet [43], specifically the DenseNet169 model. The GAF algorithm turns the time series data into two-dimensional images. Then, DenseNet is used to classify these data. They used the mHealth dataset to train and test their model. Then, they compared its performance with other models and obtained an accuracy of 97.83% and F1-score of 97.83%. [PERSON] and [PERSON] proposed a model to recognise complex activities using electromyography (EMG) sensors [44]. They proposed a lightweight CNN-LSTM model, which they trained and tested on the EMG Physical Action Dataset [45]. They compared the performance of their model against Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) models. Their proposed lightweight CNN-LSTM performed better with the highest accuracy of 84.12% and average accuracy of 83%. They also performed an ablation study and showed the importance of integrating CNN, LSTM, and the dropout layers, as the model performed better with these layers than without. With the increase in the popularity of smart homes, HAR within these homes becomes an important task, especially for health monitoring [46]. Ambient non-vision sensors are generally considered more acceptable due to comfortability and privacy [47]. In 2019, [PERSON] et al. used variations of Recurrent Neural Networks (RNNs) to analyse ambient data from two smart homes with two residents each [46]. They applied Gated Recurrent Unit (GRU) and LSTM models to the Activity Recognition with Ambient Sensing (ARAS)dataset and used a Generative Adversarial Network (GAN) to generate more data. They compared the results of the GRU and LSTM models on 10, 30 days, and 50 days of data and found that the GRU generally performed better than the LSTM models. In 2021, [PERSON] and [PERSON] compared the performance of three models (Multilayer Perceptron (MLP), RNN and LSTM) on HAR task in an ambient home environment [48]. They evaluated their models on the ARAS dataset and found that they performed well on more frequent activities but were not efficient on rare activities. Also, their MLP model had the best performance on the dataset.