发表机构
Bangladesh University of Engineering and Technology (BUET)（孟加拉国工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文利用针织压阻袖套，通过可解释特征与XGBoost实现姿势筛查，在泛化、可解释性和鲁棒性上验证了系统可部署性。
AI 中文摘要
压力感应智能纺织品将体表接触转换为与姿势和运动密切相关的密集、图像状信号，使其成为可穿戴姿势筛查的一种有前景的低成本途径。然而，实现这一前景需要的不仅仅是分类准确性：一个可部署的系统必须能够泛化到训练中未见过的佩戴者，揭示其决策背后的物理证据，并容忍每次服装脱下和重新穿戴时发生的小型穿戴偏移。本文以佩戴在前臂上的针织压阻袖套作为测试平台，共同解决这三个要求。我们将细粒度的日常活动重新分组为三个较粗的筛查类别（中性、潜在不良和功能性或过渡性），设计了29个可解释的压力分布特征，涵盖全局强度、空间压力中心、象限不对称性、分布复杂性和短时域时间变化，并在严格的受试者间划分下进行评估。经过调优的XGBoost分类器在未见过的测试受试者上达到0.818的准确率、0.788的平衡准确率和0.801的宏F1分数，帧级自举95%置信区间约为正负0.01，在留一受试者交叉验证下受试者间标准差接近0.06。一个基于原始帧训练的简单2D-CNN基线实现了大致相似的性能，表明手工设计的特征在此任务上并未被学习的空间表示所超越。基于SHAP的解释、特征组消融、合并不良类内的逐活动错误分析、类映射敏感性以及模拟穿戴旋转压力测试共同定位了模型依赖的内容、性能下降的位置及其原因，直接针对决定此类系统是否可部署的泛化性、可解释性和鲁棒性差距。
英文摘要
Pressure-sensing smart textiles convert body-surface contact into a dense, image-like signal closely tied to posture and movement, making them a promising low-cost route to wearable posture screening. Realizing that promise, however, requires more than classification accuracy: a deployable system must generalize to wearers unseen during training, expose the physical evidence behind its decisions, and tolerate the small donning offsets that occur whenever a garment is removed and re-worn. This paper addresses these three requirements jointly using a knitted piezoresistive sleeve worn on the forearm as a testbed. We regroup fine-grained everyday activities into three coarser screening categories (neutral, potentially undesirable, and functional or transitional), engineer 29 interpretable pressure-distribution features spanning global intensity, spatial center of pressure, quadrant asymmetry, distribution complexity, and short-horizon temporal change, and evaluate under a strict subject-wise split. A tuned XGBoost classifier reaches 0.818 accuracy, 0.788 balanced accuracy, and 0.801 macro F1 on unseen test subjects, with tight frame-level bootstrap 95% intervals of about plus-minus 0.01 and a subject-to-subject standard deviation near 0.06 under leave-one-subject-out cross-validation. A simple 2D-CNN baseline trained on raw frames achieves broadly similar performance, showing that hand-engineered features are not left behind by a learned spatial representation on this task. SHAP-based explanation, a feature-group ablation, per-activity error analysis inside the pooled undesirable class, class-mapping sensitivity, and a simulated donning-rotation stress test together locate what the model relies on, where it degrades, and why, directly targeting the generalization, interpretability, and robustness gaps that determine whether such a system is deployable.
