ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval
  Driven LLM Agents
Authors: name: Zechen Li, name: Baiyu Chen, name: Hao Xue, name: Flora D. Salim

Motion sensor time-series are central to human activity recognition (HAR),
with applications in health, sports, and smart devices. However, existing
methods are trained for fixed activity sets and require costly retraining when
new behaviours or sensor setups appear. Recent attempts to use large language
models (LLMs) for HAR, typically by converting signals into text or images,
suffer from limited accuracy and lack verifiable interpretability. We propose
ZARA, the first agent-based framework for zero-shot, explainable HAR directly
from raw motion time-series. ZARA integrates an automatically derived pair-wise
feature knowledge base that captures discriminative statistics for every
activity pair, a multi-sensor retrieval module that surfaces relevant evidence,
and a hierarchical agent pipeline that guides the LLM to iteratively select
features, draw on this evidence, and produce both activity predictions and
natural-language explanations. ZARA enables flexible and interpretable HAR
without any fine-tuning or task-specific classifiers. Extensive experiments on
8 HAR benchmarks show that ZARA achieves SOTA zero-shot performance, delivering
clear reasoning while exceeding the strongest baselines by 2.53x in macro F1.
Ablation studies further confirm the necessity of each module, marking ZARA as
a promising step toward trustworthy, plug-and-play motion time-series analysis.
Our codes are available at https://github.com/zechenli03/ZARA.