AI 中文总结
提出一个基于UWB数据的HAR框架，通过两种降维技术（CPE-IPCA和PPSA）结合模式发现与分类，实现高精度活动识别，适用于实时系统。
AI 中文摘要
传感器技术的最新进展使得人体活动识别（HAR）更加有效，特别是在计算资源有限的实时系统中。然而，超宽带（UWB）雷达数据由于高维性、噪声、复杂性和非线性特征而仍然具有挑战性。本研究提出一个框架，以高效地减小数据规模、发现重要模式，并以高精度从UWB信号中分类六种活动类型（Standff、Liedown、Noactivity、Sit、Stand和Walk）。本文引入了两种新颖的降维技术。第一种是聚类多项式扩展与增量PCA（CPE-IPCA），它将聚类和多项式特征扩展与增量PCA相结合，仅用50个分量保留了100%的方差。第二种是PCA后标准化方法（PPSA），它在PCA后对数据进行标准化，并在80个分量中保留了99.1%的方差，与传统非线性方法相比，实现了更优的压缩和计算效率。使用Apriori和FP-Growth识别频繁模式，然后使用随机森林和向量空间模型（VSM）进行分类。该框架在CPE-IPCA上使用随机森林达到100%的准确率，在PPSA上达到99%，而VSM在PPSA上达到100%的精确率、召回率和F1分数，在CPE-IPCA上达到接近完美的性能（精确率1.00，召回率0.98-1.00，F1 0.99-1.00），展示了一个快速、可解释且稳健的HAR系统，适用于医疗保健、辅助生活和智能环境。
英文摘要
Recent advances in sensor technology have enabled more effective human activity recognition (HAR), particularly in real-time systems with limited computational resources. However, Ultra-Wideband (UWB) radar data remain challenging due to high dimensionality, noise, complexity, and nonlinear characteristics. This research proposes a framework to efficiently reduce data size, uncover significant patterns, and classify six activity types (Standff, Liedown, Noactivity, Sit, Stand, and Walk) from UWB signals with high accuracy. Two novel dimensionality reduction techniques are introduced in this paper. The first, Clustered Polynomial Expansion with Incremental PCA (CPE-IPCA), combines clustering and polynomial feature expansion with Incremental PCA, preserving 100% of the variance in only 50 components. The second, Post-PCA Standardization Approach (PPSA), standardizes data after PCA and retains 99.1% of the variance in 80 components, achieving superior compression and computational efficiency compared to conventional nonlinear methods. Frequent patterns are identified using Apriori and FP-Growth, which are then classified with Random Forest and a Vector Space Model (VSM). The framework achieves 100% accuracy with Random Forest on CPE-IPCA and 99% on PPSA, while VSM attains 100% precision, recall, and F1 on PPSA and near-perfect performance on CPE-IPCA (precision 1.00, recall 0.98-1.00, F1 0.99-1.00), demonstrating a fast, interpretable, and robust HAR system suitable for healthcare, assisted living, and smart environments.
Comments14 pages, 9 figures. Manuscript prepared for submission to an IEEE journal in machine learning
