ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval
  Driven LLM Agents
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.
6899e842996e0a645a52a801
Zechen Li
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2025-08-13T07:22:02.583Z
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Zechen Li
user
zechenli03
6899e842996e0a645a52a802
Baiyu Chen
claimed_verified
2025-08-20T08:53:31.770Z
67b5890b0d878eff1a36ce8d
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Breeze Chen
user
Breezelled
6899e842996e0a645a52a803
Hao Xue
6899e842996e0a645a52a804
Flora D. Salim
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval
  Driven LLM Agents
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.
ZARA is an agent-based framework for zero-shot, explainable human activity recognition from raw motion time-series, achieving state-of-the-art performance without fine-tuning.
agent-based framework zero-shot explainable HAR raw motion time-series feature knowledge base multi-sensor retrieval module hierarchical agent pipeline LLM macro F1 plug-and-play motion time-series analysis