## 4 Non-Vision-Based HAR
### HAR Using Large Language Models  
With the boom of Artificial Intelligence, LLMs have been at the forefront of more recent research in business, cyber security, finance and healthcare [88]. The task of HAR is not left out of this trend. This subsection reviews some of these papers. While supervised learning might achieve high accuracy in HAR tasks, it requires a large amount of annotated data, which is expensive and time intensive [89]; with LLMs, this can be avoided or mitigated. [PERSON] et al. evaluated twelve LLMs on various health prediction tasks including HAR and presented a fine-tuned LLM model, HealthAlpaca [90]. They used four public datasets--PMData, LifeSnaps, GLOBEM, and AW_FB--for prompting and fine-tuning the LLMs. Their fine-tuned model achieved the highest accuracy in 8 out of the 10 tasks considered including HAR. They also performed an ablation study that showed the importance of context enhancement strategies (combining health knowledge, user context and temporal information). [PERSON] et al. proposed using a zero-shot human activity recogniser, HARGPT, which takes raw sensor data with a simple prompt and yields a recognition outcome [91]. They used well-known LLMs such as ChatGPT, Google Gemini, and LLaMA2-70b with a chain-of-thought (CoT) prompt design. They validated their models on the Capture24 and HHAR datasets, and they achieved an average accuracy of 80%. The authors compared the performance of GPT4 to various baseline models (Random Forest, SVM, DCNN, and LIMU-LSTM). The baseline models achieved over 90% accuracy on the seen datasets but on unseen data, GPT4-CoT surpassed them with F1 scores of 0.795 on Capture24 and 0.790 on the HHAR dataset. [PERSON] et al. proposed a model called LLMIE_UHAR that uses Iterative Evolution and LLM [89]. Their proposed model first selects valuable data from the large unlabelled dataset using a clustering algorithm. The selected data points are then changed into prompts which are then used as input into the LLM, which annotates them. The annotated data are then used to train a neural network. Their model integrates clustering algorithm, LLMs, and the neural network to enhance the HAR task. They validated their model on the ARAS dataset and it achieved an accuracy of 96%. In 2021, [PERSON] et al. developed LIMU-BERT (lite BERTlike self-supervised representation learning model for mobile IMU data), which makes use of unlabelled data [92]. Their model is based on BERT (Bidirectional Encoder Representations from Transformers), which is a language representation model. They validated their model on four datasets: HHAR, UCI, MotionSense and Shoaib datasets. The LIMU-BERT model achieved an average accuracy of 0.929 and F1 score of 0.921 on all datasets, which was higher than other baseline models (DCNN, DeepSense, R-GRU, and TPN). In 2024, [PERSON] et al. developed LLaSa (Large Language and Sensor Assistant), which combines LIMU-BERT and Llama [93]. In their paper, they also introduced SensorCaps, a movement sensors activity narration dataset and OpenSQA, a question-answer dataset. These datasets were created using movement sensor data from HHAR, UCI-HAR, MotionSense and Shoaib datasets. The model was evaluated first as a closed-ended zero-shot task on five datasets (HHAR, UCI-HAR, MotionSense, Shoaib, and SHL) and then as an open-ended task on PAMAP2 dataset. Its performance was compared to that of non-fine-tuned and fine-tuned GPT-3.5-Turbo and it performed much better. [PERSON] et al. developed PhysioLLM, which integrates contextual data to physiological data from a wearable to provide personalised understanding of health data and suggesting actions to achieve personal health goals [94]. They used two LLMs in their system, one to generate insights from the data and another for the conversation side of the system. Both LLMs are based on OpenAI's GPT-4-turbo model. They validated their system using a user study of the sleep pattern of 24 Fitbit watch users and their system outperformed the Fitbit App and a generic LLM.