# Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition

arXiv: https://arxiv.org/abs/2407.09529 · HTML: https://mlsanity.com/paper/2407.09529/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes](https://mlsanity.com/paper/2012.02300/) (2020)
- [Explainable Activity Recognition for Smart Home Systems](https://mlsanity.com/paper/2105.09787/) (2021)
- [Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes](https://mlsanity.com/paper/2111.12158/) (2021)
- [Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition](https://mlsanity.com/paper/2404.07245/) (2024)

## Built on by (most cited first, 5 of 5)

- [RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition](https://mlsanity.com/paper/2512.08984/) (2025)
- [MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living](https://mlsanity.com/paper/2504.20505/) (2025)
- [A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control](https://mlsanity.com/paper/2511.00136/) (2025)
- [Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence](https://mlsanity.com/paper/2601.08241/) (2026)
- [RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment](https://mlsanity.com/paper/2607.26631/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR) is one of the central problems in fields such as healthcare, elderly care, and security at home. However, traditional HAR approaches face challenges including data scarcity, difficulties in model generalization, and the complexity of recognizing activities in multi-person scenarios. This paper proposes a system framework called LAHAR, based on large language models. Utilizing prompt engineering techniques, LAHAR addresses HAR in multi-person scenarios by enabling subject separation and action-level descriptions of events occurring in the environment. We validated our approach on the ARAS dataset, and the results demonstrate that LAHAR achieves comparable accuracy to the state-of-the-art method at higher resolutions and maintains robustness in multi-person scenarios.