HyperHAR: Inter-sensing Device Bilateral Correlations and Hyper-correlations Learning Approach for Wearable Sensing Device Based Human Activity Recognition

Nafees Ahmad, Ho-fung Leung
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Abstract

Human activity recognition (HAR) has emerged as a prominent research field in recent years. Current HAR models are only able to model bilateral correlations between two sensing devices for feature extraction. However, for some activities, exploiting correlations among more than two sensing devices, which we call hyper-correlations in this paper, is essential for extracting discriminatory features. In this work, we propose a novel HyperHAR framework that automatically models both bilateral and hyper-correlations among sensing devices. The HyperHAR consists of three modules. The Intra-sensing Device Feature Extraction Module generates latent representation across the data of each sensing device, based on which the Inter-sensing Device Multi-order Correlations Learning Module simultaneously learns both bilateral correlations and hyper-correlations. Lastly, the Information Aggregation Module generates a representation for an individual sensing device by aggregating the bilateral correlations and hyper-correlations it involves in. It also generates the representation for a pair of sensing devices by aggregating the hyper-correlations between the pair and other different individual sensing devices. We also propose a computationally more efficient HyperHAR-Lite framework, a lightweight variant of the HyperHAR framework, at a small cost of accuracy. Both the HyperHAR and HyperHAR-Lite outperform SOTA models across three commonly used benchmark datasets with significant margins. We validate the efficiency and effectiveness of the proposed frameworks through an ablation study and quantitative and qualitative analysis.
HyperHAR:基于可穿戴传感设备的人类活动识别的传感设备间双边相关性和超相关性学习方法
近年来,人类活动识别(HAR)已成为一个突出的研究领域。目前的人类活动识别模型只能对两个传感设备之间的双边相关性进行建模,以提取特征。然而,对于某些活动,利用两个以上传感设备之间的相关性(本文称之为超相关性)对于提取识别特征至关重要。在这项工作中,我们提出了一个新颖的 HyperHAR 框架,它能自动为传感设备之间的双边和超相关性建模。HyperHAR 由三个模块组成。传感设备内部特征提取模块生成每个传感设备数据的潜在表征,在此基础上,传感设备间多阶相关性学习模块同时学习双边相关性和超相关性。最后,信息聚合模块通过聚合单个传感设备所涉及的双边相关性和超相关性,为其生成一个表征。信息聚合模块还通过聚合一对传感设备和其他不同传感设备之间的超相关性,生成这对传感设备的表征。我们还提出了一种计算效率更高的 HyperHAR-Lite 框架,它是 HyperHAR 框架的轻量级变体,只需付出较小的精度代价。在三个常用基准数据集上,HyperHAR 和 HyperHAR-Lite 的性能都明显优于 SOTA 模型。我们通过一项消融研究以及定量和定性分析验证了所建议框架的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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