# Unsupervised Human Activity Recognition through Two-stage Prompting with ChatGPT

arXiv: https://arxiv.org/abs/2306.02140 · HTML: https://mlsanity.com/paper/2306.02140/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 3 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Generated Knowledge Prompting for Commonsense Reasoning](https://mlsanity.com/paper/2110.08387/) (2021)

## Built on by (most cited first, 11 of 11)

- [SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition](https://mlsanity.com/paper/2410.10624/) (2024)
- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [Large Language Models are Zero-Shot Recognizers for Activities of Daily Living](https://mlsanity.com/paper/2407.01238/) (2024)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (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)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)
- [OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models](https://mlsanity.com/paper/2501.00432/) (2024)
- [Thou Shalt Not Prompt: Zero-Shot Human Activity Recognition in Smart Homes via Language Modeling of Sensor Data & Activities](https://mlsanity.com/paper/2507.21964/) (2025)

## Abstract (arXiv)

Wearable sensor devices, which offer the advantage of recording daily objects used by a person while performing an activity, enable the feasibility of unsupervised Human Activity Recognition (HAR). Unfortunately, previous unsupervised approaches using the usage sequence of objects usually require a proper description of activities manually prepared by humans. Instead, we leverage the knowledge embedded in a Large Language Model (LLM) of ChatGPT. Because the sequence of objects robustly characterizes the activity identity, it is possible that ChatGPT already learned the association between activities and objects from existing contexts. However, previous prompt engineering for ChatGPT exhibits limited generalization ability when dealing with a list of words (i.e., sequence of objects) due to the similar weighting assigned to each word in the list. In this study, we propose a two-stage prompt engineering, which first guides ChatGPT to generate activity descriptions associated with objects while emphasizing important objects for distinguishing similar activities; then outputs activity classes and explanations for enhancing the contexts that are helpful for HAR. To the best of our knowledge, this is the first study that utilizes ChatGPT to recognize activities using objects in an unsupervised manner. We conducted our approach on three datasets and demonstrated the state-of-the-art performance.