IEEE ACCESSに1件採択

@article{ieee-access-Zheng2025,

title = {A Review of Wearables-Based Activities of Daily Living Recognition with LLMs: Overview, Progress and Trends},

author = {Wenqi Zheng, Yutaka Arakawa},

url = {https://ieeexplore.ieee.org/document/11123829},

doi = {10.1109/ACCESS.2025.3598088},

year  = {2025},

date = {2025-08-11},

journal = {IEEE ACCESS},

abstract = {Over the past few years, driven by advancements in wearable devices and intelligent algorithms, numerous works have emerged in the field of wearables-based activities of daily living recognition. However, most existing methods still face challenges in addressing activity recognition in complex scenarios and generalizing to different devices. Many recent methods use Large Language Models to address these challenges and have made significant progress. LLMs enhance complex scenario perception by capturing rich contextual knowledge and improve generalization across tasks, enabling better adaptability to diverse conditions. In this paper, we present a comprehensive review of wearables-based ADL recognition with LLMs, covering 204 related works. We firstly introduce the overall pipeline of adapting pretrained LLMs to the field of wearables-based ADL recognition. Then the progress on data processing, LLMs finetuning and inference, and ADL applications enabled by LLMs are reported systematically. In the end, we summarize current research trends and discuss the future directions towards effective and generalized activity recognition with different wearable devices.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}