数据驱动的上下文检测利用被动感知的近距物来识别日常生活中的复杂活动

A. Akbari, Reese Grimsley, R. Jafari
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引用次数: 1

摘要

可穿戴系统在人体活动识别等各种应用中开启了新的传感范式,可以提高移动医疗应用的有效性。目前使用可穿戴设备的系统无法理解周围环境,这限制了它们的传感能力。例如,仅仅使用可穿戴运动传感器,就很难区分具有相似运动模式但发生在不同环境中的某些活动,例如参加会议或上课。本文的重点是了解用户的周围环境,即环境上下文,以增强可穿戴设备的能力,重点是检测复杂的日常生活活动(ADL)。我们开发了一种方法来自动检测上下文使用被动可观察信息广播的设备在用户的地区。该系统不需要特定的基础设施或额外的硬件。我们开发了一种模式提取算法和上下文与活动之间的概率映射,以减少可能结果的集合。该系统包含一个与运动传感器一起工作的通用ADL分类器,学习个性化上下文,并使用该分类器将活动的搜索空间减少到在特定上下文中发生的活动。我们收集了复杂adl的真实世界数据,通过缩小搜索空间和上下文,我们将平均f1分数从0.72提高到0.80。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living
Wearable systems have unlocked new sensing paradigms in various applications such as human activity recognition, which can enhance effectiveness of mobile health applications. Current systems using wearables are not capable of understanding their surroundings, which limits their sensing capabilities. For instance, distinguishing certain activities such as attending a meeting or class, which have similar motion patterns but happen in different contexts, is challenging by merely using wearable motion sensors. This article focuses on understanding user's surroundings, i.e., environmental context, to enhance capability of wearables, with focus on detecting complex activities of daily living (ADL). We develop a methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale. This system does not require specific infrastructure or additional hardware. We develop a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. The proposed system contains a general ADL classifier working with motion sensors, learns personalized context, and uses that to reduce the search space of activities to those that occur within a certain context. We collected real-world data of complex ADLs and by narrowing the search space with context, we improve average F1-score from 0.72 to 0.80.
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CiteScore
10.30
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