---
title: "On-device Detection of Wearer-induced NLOS on Chest-worn UWB Tags: Machine Learning, Link Indicators, or Both?"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.41799v1"
permalink: "https://feed.astralor.com/items/itm_abfd12980e7c2a40c38d39a6"
published: "2026-10-30T14:28:13.000Z"
score: 57
channel: "ai"
tags: ["On-device Detection","Wearer-induced NLOS","Chest-worn UWB Tags","Machine Learning","Link Indicators","Both","Ultra-wideband","UWB"]
---

# On-device Detection of Wearer-induced NLOS on Chest-worn UWB Tags: Machine Learning, Link Indicators, or Both?

## At a glance

Ultra-wideband (UWB) enables high-accuracy distance estimation (ranging), but performance degrades in non-line-of-sight (NLOS) conditions. When UWB tags are worn, the human body inevitably causes a wearer-induced NLOS (…

## Key points

- Ultra-wideband (UWB) enables high-accuracy distance estimation (ranging), but performance degrades in non-line-of-sight (NLOS) conditions.
- When UWB tags are worn, the human body inevitably causes a wearer-induced NLOS (WNLOS), yielding significant errors; still, despite its relevance, WNLOS remains understudied.
- Moreover, WNLOS detection typically must occur on-device at the tag, requiring classification accuracy to be balanced against tight runtime and energy constraints often hard to meet with machine learning (ML) -- the dom…

## Full text

Ultra-wideband (UWB) enables high-accuracy distance estimation (ranging), but performance degrades in non-line-of-sight (NLOS) conditions. When UWB tags are worn, the human body inevitably causes a wearer-induced NLOS (WNLOS), yielding significant errors; still, despite its relevance, WNLOS remains understudied. Moreover, WNLOS detection typically must occur on-device at the tag, requiring classification accuracy to be balanced against tight runtime and energy constraints often hard to meet with machine learning (ML) -- the dominating NLOS classification approach. Meanwhile, UWB radio link indicators (LI) offer a faster and lightweight alternative that has rarely been explored. We study existing on-device ML and LI classifiers when applied to WNLOS, and propose novel hybrid ones exploiting their complementarity. We focus on chest-worn tags and analyze 21 classifiers on a dataset of 1.5 million samples across channels, PHY settings, environments, subjects, and orientations. Results show that while all of them achieve good classification accuracy, relative performance, including ranging errors due to misclassification, depends heavily on channel and PHY configuration. Runtime and energy efficiency also vary dramatically; some techniques offer marginal accuracy gains while consuming up to 39.6x more energy. Therefore, we also introduce a recommender tool to identify optimal classifiers under user-defined constraints, publicly released with our dataset.

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