## 1 Introduction  
Human Activity Recognition (HAR) involves using different techniques to determine what a person is doing from observations of sensory information of the person's body and conditions of the surrounding environment [1] and also inferring the person's goal and mental status [2]. It is the automatic detection and recognition of human activities using data collected from various sensors. These activities can range from simple actions like standing or sitting to more complex activities like eating, cooking, or commuting to work [3].  
HAR is a fast-evolving field that can change how we monitor and understand human behaviour. It is used in multiple domains such as sports, gaming, health monitoring, robotics, human-computer interaction, security, surveillance, and entertainment [4]. The monitoring of vital health parameters like blood pressure, respiration rate, electrocardiogram (ECG) patterns, and pulse rate is crucial to personal health systems and telemedicine techniques [5]. These data are important in the application of HAR in health-based use cases.  
This survey aims to provide an overview of the state-of-the-art in HAR, focusing on new techniques such as multimodality, Deep Reinforcement Learning (DRL) and the use of large language models (LLMs) in HAR. It explores the wide range of human activities and the various sensor technologies used for data collection. It examines the various recent algorithms for activity recognition and available datasets with physiological data. Also, the applications of HAR in healthcare are discussed as well as the challenges and future directions.  
This survey paper uses a systematic literature review methodology to identify and analyse recent and relevant research on HAR with a focus on multimodality, DRL, and LLMs. For this survey, three primary data sources were used: IEEE Xplore, SCOPUS, and Google Scholar. The search was limited to articles published in English between 2019 and 2024, which captured novel and recent advancements in the topic area. The decision to limit the survey to publications between 2019 and 2024 was to capture the most recent algorithms in HAR, especially ones that have not been captured by already existing surveys. Publications not accessible either due to the website being unavailable or being part of paid content were excluded from this study. Redundant publications (for instance conference proceedings already extended in a journal article) were also excluded.  
Publications with novel algorithms were selected for this survey with emphasis on multimodality, DRL, and LLMs. Multimodal HAR algorithms are important for capturing the complex nature of human activities, and there are various research works that have attempted to harness its potential. Traditionally, HAR models use supervised learning and require large and annotated datasets which are computationally expensive to acquire. While some of the sensors can acquire a ton of data, researchers are limited to only the data they can afford to label. With DRL and LLM, such large data can be labelled with less computational cost and even unlabelled and semi-labelled datasets can be used in building HAR models. This is important because HAR models are limited to their training datasets and the more robust their dataset, the more robust the model would be. For future research in the area of HAR, these three methodologies will aid in the development of robust, user-centred, and more accurate HAR systems which can be personalised especially for use in healthcare applications. The publications reviewed are grouped firstly based on the type of sensors used and then based on the algorithm used.