# Large Language Models are Zero-Shot Recognizers for Activities of Daily Living

arXiv: https://arxiv.org/abs/2407.01238 · HTML: https://mlsanity.com/paper/2407.01238/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 68 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Large Language Models are Few-Shot Health Learners](https://mlsanity.com/paper/2305.15525/) (2023)
- [Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data](https://mlsanity.com/paper/2206.02909/) (2022)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?](https://mlsanity.com/paper/2403.02727/) (2024)
- [Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks](https://mlsanity.com/paper/1903.12489/) (2019)
- [Large Language Models Are Neurosymbolic Reasoners](https://mlsanity.com/paper/2401.09334/) (2024)
- [TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition](https://mlsanity.com/paper/2311.08245/) (2023)
- [ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition](https://mlsanity.com/paper/2202.00758/) (2022)
- [IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2402.01049/) (2024)
- [Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data](https://mlsanity.com/paper/2403.01133/) (2024)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning](https://mlsanity.com/paper/2310.20034/) (2023)
- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [Unsupervised Human Activity Recognition through Two-stage Prompting with ChatGPT](https://mlsanity.com/paper/2306.02140/) (2023)
- [Context-driven Active and Incremental Activity Recognition](https://mlsanity.com/paper/1906.03033/) (2019)

## Built on by (most cited first, 13 of 13)

- [Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework](https://mlsanity.com/paper/2508.03989/) (2025)
- [RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition](https://mlsanity.com/paper/2512.08984/) (2025)
- [ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data](https://mlsanity.com/paper/2505.14038/) (2025)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (2025)
- [MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living](https://mlsanity.com/paper/2504.20505/) (2025)
- [Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey](https://mlsanity.com/paper/2412.14708/) (2024)
- [Knowledge Distillation for LLM-Based Human Activity Recognition in Homes](https://mlsanity.com/paper/2601.07469/) (2026)
- [Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence](https://mlsanity.com/paper/2601.08241/) (2026)
- [The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline](https://mlsanity.com/paper/2504.08877/) (2025)
- [Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Models](https://mlsanity.com/paper/2511.02894/) (2025)
- [LLM-powered reasoning in agent-based modeling](https://mlsanity.com/paper/2607.06757/) (2026)
- [A Plug-and-Play 2D Motion Interface for Real-World Motion Language Models](https://mlsanity.com/paper/2608.15984/) (2026)
- [More Features Are Not More Evidence: Limits of Training-Free Human Activity Recognition with Jev](https://mlsanity.com/paper/2609.36154/) (2026)

## Abstract (arXiv)

The sensor-based recognition of Activities of Daily Living (ADLs) in smart home environments enables several applications in the areas of energy management, safety, well-being, and healthcare. ADLs recognition is typically based on deep learning methods requiring large datasets to be trained. Recently, several studies proved that Large Language Models (LLMs) effectively capture common-sense knowledge about human activities. However, the effectiveness of LLMs for ADLs recognition in smart home environments still deserves to be investigated. In this work, we propose ADL-LLM, a novel LLM-based ADLs recognition system. ADLLLM transforms raw sensor data into textual representations, that are processed by an LLM to perform zero-shot ADLs recognition. Moreover, in the scenario where a small labeled dataset is available, ADL-LLM can also be empowered with few-shot prompting. We evaluated ADL-LLM on two public datasets, showing its effectiveness in this domain.