  There is an ongoing debate regarding the potential of Large Language Models
(LLMs) as foundational models seamlessly integrated with Cyber-Physical Systems
(CPS) for interpreting the physical world. In this paper, we carry out a case
study to answer the following question: Are LLMs capable of zero-shot human
activity recognition (HAR). Our study, HARGPT, presents an affirmative answer
by demonstrating that LLMs can comprehend raw IMU data and perform HAR tasks in
a zero-shot manner, with only appropriate prompts. HARGPT inputs raw IMU data
into LLMs and utilizes the role-play and think step-by-step strategies for
prompting. We benchmark HARGPT on GPT4 using two public datasets of different
inter-class similarities and compare various baselines both based on
traditional machine learning and state-of-the-art deep classification models.
Remarkably, LLMs successfully recognize human activities from raw IMU data and
consistently outperform all the baselines on both datasets. Our findings
indicate that by effective prompting, LLMs can interpret raw IMU data based on
their knowledge base, possessing a promising potential to analyze raw sensor
data of the physical world effectively.
