Abstract

Fitness and activity tracking devices acquire, process and store rich behavioural data that are consumed by the end-user to learn health insights. This rich data source also enables a secondary use of being part of a biometric authentication system. However, there are many open research challenges with the use of data generated by fitness and activity trackers as a biometric source. In this article, the challenge of using data acquired from low-cost devices is tackled. This includes investigating how to best partition the data to deduce repeatable behavioural traits, while maximizing the uniqueness between participant datasets. In this exploratory research, 3 months’ worth of data (heart rate, step count and sleep) for five participants is acquired and utilized in its raw form from low-cost devices. It is established that dividing the data into 14-h segments is deemed the most suitable based on measuring coefficients of variance. Several supervised machine learning algorithms are then applied where the performance is evaluated by six metrics to demonstrate the potential of employing this data source in biometric-based security systems.

中文翻译：

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用于生物识别的低成本健身和活动跟踪器

摘要

健身和活动跟踪设备可获取，处理和存储丰富的行为数据，这些数据将被最终用户用来学习健康见解。这种丰富的数据源还可以作为生物识别系统的一部分进行二次使用。但是，使用健身和活动追踪器生成的数据作为生物统计数据来源存在许多开放的研究挑战。在本文中，解决了使用从低成本设备获取的数据的挑战。这包括研究如何最大程度地对数据进行分区以推断出可重复的行为特征，同时最大化参与者数据集之间的唯一性。在这项探索性研究中，从低成本设备中以原始形式获取并利用了五个参与者的3个月的数据（心率，步数和睡眠）。已经确定，基于测量方差系数，将数据划分为14-h段被认为是最合适的。然后应用了几种有监督的机器学习算法，其中通过六个度量对性能进行评估，以证明在基于生物特征的安全系统中使用此数据源的潜力。