Online continual learning for human activity recognition

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Martin Schiemer, Lei Fang, Simon Dobson, Juan Ye
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引用次数: 0

Abstract

Sensor-based human activity recognition (HAR), with the ability to recognise human activities from wearable or embedded sensors, has been playing an important role in many applications including personal health monitoring, smart home, and manufacturing. The real-world, long-term deployment of these HAR systems drives a critical research question: how to evolve the HAR model automatically over time to accommodate changes in an environment or activity patterns. This paper presents an online continual learning (OCL) scenario for HAR, where sensor data arrives in a streaming manner which contains unlabelled samples from already learnt activities or new activities. We propose a technique, OCL-HAR, making a real-time prediction on the streaming sensor data while at the same time discovering and learning new activities. We have empirically evaluated OCL-HAR on four third-party, publicly available HAR datasets. Our results have shown that this OCL scenario is challenging to state-of-the-art continual learning techniques that have significantly underperformed. Our technique OCL-HAR has consistently outperformed them in all experiment setups, leading up to 0.17 and 0.23 improvements in micro and macro F1 scores.

人类活动识别的在线持续学习
基于传感器的人类活动识别(HAR)能够从可穿戴或嵌入式传感器中识别人类活动,在个人健康监测、智能家居和制造等许多应用中发挥着重要作用。这些HAR系统在现实世界中的长期部署推动了一个关键的研究问题:如何随着时间的推移自动演化HAR模型,以适应环境或活动模式的变化。本文提出了一种用于HAR的在线连续学习(OCL)场景,其中传感器数据以流式方式到达,其中包含来自已经学习的活动或新活动的未标记样本。我们提出了一种技术,OCL-HAR,在发现和学习新活动的同时,对流式传感器数据进行实时预测。我们在四个第三方公开的HAR数据集上对OCL-HAR进行了实证评估。我们的研究结果表明,这种OCL场景对表现不佳的最先进的持续学习技术具有挑战性。我们的技术OCL-HAR在所有实验设置中始终优于他们,导致微观和宏观F1分数分别提高了0.17和0.23。
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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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