## 1 Introduction
### Review of Related Works  
There have been survey papers on HAR with a focus on different areas like Wearable Sensor-based HAR [6], HAR in ambient smart living environments [2; 7] and deep learning-based HAR [4; 8; 9]. This section analyses some of those survey papers and highlights the gap this survey paper aims to bridge. [PERSON] et al. [6] discussed sensor modalities in HAR, classifying them as Wearable Sensor-based HAR (WSHAR), Ambient Sensor-based HAR (ASHAR) and Hybrid Sensor-based HAR (HSHAR). The survey focused on wearable sensor modality-centred HAR in healthcare and discussed the various steps involved in WSHAR, including sensor selection and placement, data collection and preprocessing, feature extraction and selection, and classification algorithms. [PERSON] and [PERSON] [7] reviewed the application of deep learning approaches to HAR and vital health sign monitoring in ambient assisted living environments. They highlighted the importance of ambient sensors in recognising complex human activities, especially in ambient assisted living environments. The paper also discussed different health vitals like respiration rate, body temperature, heart rate, etc., and the use of wearable and ambient sensors to monitor them. [PERSON] et al. [10] provided an analysis of data fusion techniques and classifier systems for HAR with a focus on wearable and mobile devices. The authors highlighted the importance of integrating data from multiple sensors to reduce uncertainty and enhance the accuracy of HAR systems. They discussed different data fusion techniques and highlighted the advantages of deep learning in automatic feature extraction. [PERSON] et al. [3] presented an overview of HAR research conducted between 2010 and 2018 with a focus on device-free solutions. In the survey paper, a new taxonomy is proposed to classify HAR into three main categories: action based, motion based, and interaction based [3]. The authors discussed the latest research in each category and highlighted key attributes and design approaches. They also highlighted some applications of HAR in various areas and discussed future research issues and challenges such as recognising complex activities, addressing environmental interference, security, and privacy issues, and so on. [PERSON] et al. [8] conducted a comprehensive overview of deep learning techniques in sensor-based HAR and proposed a new taxonomy of deep learning methods given the challenges of HAR. The paper discussed various deep learning architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and their applications in addressing challenges like feature extraction, annotation scarcity, and class imbalance. They emphasised the growing importance of HAR due to the increasing prevalence of smart devices and the Internet of Things (IoT) [8]. The paper also outlined the typical HAR process and identified challenges in HAR and compared different solutions for these challenges. The authors also suggested some directions for future research such as the use of deep unsupervised transfer learning and the need for a standardisation of the state of the art. [PERSON] et al. [9] reviewed deep learning-based human behaviour recognition methods. Their survey was mainly on human behaviour recognition based on two-stream, 3D convolutional and hybrid networks. They highlighted the importance of the availability of large-scale annotated datasets and reviewed some of the available datasets. They also pointed out some issues with the available datasets such as occlusion, which is when other people or objects obstruct the person being recognised, and the large amount of time and resources needed to properly label a large dataset. [PERSON] et al. [2] provided an overview of current HAR research in smart living and also provided a road-map for future advances in HAR. The authors delved into the multifaceted domain of HAR within the context of smart living environment.