利用脚步振动实现持续识别，无需预存数据且隐私友好。
Continual Person Identification using Footstep-Induced Floor Vibrations on Heterogeneous Floor Structures
- 通过分解振动源差异，设计特征变换降低个体间波动。
- 实测20人，识别准确率达90%，变异减少70%。
- 适合无接触、隐私敏感的公共建筑身份识别场景。
人员识别对智能建筑提供健康监测、活动追踪和人员管理等个性化服务至关重要。然而，以往方法依赖于预先采集每个人的样本数据，在访客频繁的建筑和公共场所中难以实现。因此亟需一种能实时渐进学习人员身份的持续识别系统。现有研究多采用摄像头，但需视线通透且引发隐私担忧；可穿戴设备或压力垫则受限于携带要求或密集部署。为此，有研究提出利用脚步引起的结构振动进行识别，该方式非侵入且更易被接受隐私保护。但其面临重大挑战：因建筑结构异质性与步态差异导致振动数据高度变异性，使在线识别算法性能下降。本文系统分析了脚步振动数据的变异性来源，量化并分解不同变异成分，进而设计特征变换函数，有效降低个体内部数据波动，提升不同个体之间的可区分性。通过20人的实地实验验证，结果表明变异减少70%，在线识别准确率达到90%。
原文摘要 · Abstract (English)
Person identification is important for smart buildings to provide personalized services such as health monitoring, activity tracking, and personnel management. However, previous person identification relies on pre-collected data from everyone, which is impractical in many buildings and public facilities in which visitors are typically expected. This calls for a continual person identification system that gradually learns people's identities on the fly. Existing studies use cameras to achieve this goal, but they require direct line-of-sight and also have raised privacy concerns in public. Other modalities such as wearables and pressure mats are limited by the requirement of device-carrying or dense deployment. Thus, prior studies introduced footstep-induced structural vibration sensing, which is non-intrusive and perceived as more privacy-friendly. However, this approach has a significant challenge: the high variability of vibration data due to structural heterogeneity and human gait variations, which makes online person identification algorithms perform poorly. In this paper, we characterize the variability in footstep-induced structural vibration data for accurate online person identification. To achieve this, we quantify and decompose different sources of variability and then design a feature transformation function to reduce the variability within each person's data to make different people's data more separable. We evaluate our approach through field experiments with 20 people. The results show a 70% variability reduction and a 90% accuracy for online person identification.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。