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首页> 外文期刊>ACM transactions on sensor networks >SonicDoor: A Person Identification System Based on Modeling of Shape, Behavior, and Walking Patterns
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SonicDoor: A Person Identification System Based on Modeling of Shape, Behavior, and Walking Patterns

机译：SonicDoor：基于形状，行为和行走模式建模的人员识别系统

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摘要

Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current techniques do not scale beyond 20 people, whereas commercial buildings have 100 or more people. This article proposes a new method to identify occupants by sensing their body shape, movement, and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information, such as paths and historical walks through different doors of the building. Each SonicDoor is instrumented with ultrasonic ping sensors, one on top sensing height and two on the sides of the door sensing width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9,000 walking events spanning over 170 people. The proposed method identifies 100 occupants with an accuracy of 90.2%, which makes it suitable for commercial buildings.
机译：非侵入式乘员身份识别可在智能建筑中实现多种应用，例如气候和照明的个性化。当前的技术规模不能超过20人，而商业建筑却可以容纳100人或更多。本文提出了一种新的方法，通过在乘员穿过装有三个超声波传感器的门SonicDoor时感应其身体形状，运动和行走方式来识别乘员。所提出的方法可以推断上下文信息，例如穿过建筑物不同门的路径和历史走道。每个SonicDoor都装有超声波ping传感器，一个在顶部感应高度，一个在侧面感应走过门的人的宽度。 SonicDoor会检测到步行事件并对其进行分析，以推断Walker是使用电话，手持手提包还是背包。它从步行事件中提取了一组特征，并使用一组转换函数来校正它们以减轻偏差。我们在一个真实的建筑物中部署了五个SonicDoors，历时两个月，并收集了数据，其中包括9000多次步行事件，涉及170多人。所提出的方法可识别100名居住者，其准确度为90.2％，这使其适用于商业建筑。

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