## 4 Non-Vision-Based HAR
One of the problems faced by HAR in smart home environments is the labelling of the data. To solve this problem, [PERSON] et al. propose the use of multisource transfer learning to transfer a HAR model from labelled source homes to an unlabelled target home [47]. They first generated transferable representations (TRs) of the sensors in the labelled homes. Then, an LSTM HAR model was built based on the TR and then deployed to the unlabelled home. They conducted experiments on four homes in the CASAS dataset (HH101, HH103, HH105, and HH109) to validate their proposed method. Their experiments showed that their method outperformed HAR models which are based on only common sensors or single-source homes. HAR becomes even more complicated when there is more than one resident in the house and the activities of residents overlap. To address this issue, [PERSON] et al. proposed the classifier chain method of multilabel classification [49], which considers underlying label dependencies. They used K-Nearest Neighbour as the base classifier and evaluated the proposed method on the ARAS dataset using 10-fold cross-validation. Their method was able to recognise the 27 activities in the dataset and the resident performing it. Table 3 shows a summary of the papers reviewed in this section. On the WISDM dataset, a movement sensor dataset, ref. [42] in 2024 achieved the highest model accuracy of 99.74% using a multi-input CNN-LSTM model showing the efficiency of multimodality. More multimodal methods are discussed in Section 5.1. For ambient sensors and the smart home environment, using the ARAS dataset, ref. [48] achieved an accuracy of between 0.91 and 0.92 using MLP, RNN, and LSTM models for both houses in the dataset, while ref. [49] achieved a higher accuracy of 0.931 in house B using the classifier chain method of the Multilabel the Classification (MLC) technique with KNN as the base classifier. Some work has also been conducted with datasets that have physiological data. On the mHealth dataset, which has movement sensors and physiological sensors (ECG), ref. [43] used GAF-DenseNet and achieved 97.83% accuracy, and ref. [41] achieved an accuracy of 99.2%. In 2023, ref. [44] collected a new EMG dataset and obtained an accuracy of 83% on the HAR task using a 1D CNN-LSTM model. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**S/N** & **Paper** & **Dataset** & **Sensors** & **Model** & **Accuracy** & **Contribution to Knowledge** \\ \hline \multirow{4}{*}{1} & Classification of & & & & & Investigated the best combination of sensor data (found acceleration and angular velocity to give the highest accuracy). \\  & Human Motion Activities using & New dataset of walking and & Accelerometer, workspace and & DNN model (LSTM) & 96.5\% & \\  & Mobile Phone & & & & & \\  & Sensors and Deep Learning Model [38] & & & & & \\ \hline \multirow{4}{*}{2} & Human Activity & & & & & Used a grid search to identify the best architecture. \\  & Recognition on & & & & & \\ \cline{1-1}  & Smartphones Using a Bidirectional LSTM & & Accelerometer and gyroscope & BiLSTM & 92.67\% & \\ \cline{1-1}  & Network [39] & & & & & best architecture. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Non-vision HAR reviewed papers.