Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement.
Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement.
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DOI：
10.3390/s150818901
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发表时间：
2015-07-31
期刊：
影响因子：
--
通讯作者：
Lovell NH
中科院分区：
文献类型：
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作者：
del Rosario MB;Redmond SJ;Lovell NH
移动技术的进步导致了“智能手机”的出现，这是一种具有更先进连接功能的新型设备，迅速成为我们生活中不可或缺的一部分。智能手机配备了相对先进的计算能力、全球定位系统（GPS）接收器和传感能力（即惯性测量单元（IMU）以及最近的磁力计和气压计），这些都可以在可穿戴式动态监视器（wam）中找到。因此，最初为wam开发的算法“计数”步数（即计步器）；衡量身体活动水平；间接估计能量消耗和监测人体运动可以在智能手机上使用。这些算法可能使临床医生能够“闭环”，通过开出及时的干预措施，改善或维持有跌倒风险的人群的健康，或者患有慢性疾病的人群的健康，这些疾病的进展与运动和流动性减少有关。智能手机技术无处不在的特性使其成为远程监控人类活动的理想平台，无需购买专用的WAM，也不会带来使用不便。在本文中，概述了可以在智能手机中找到的传感器，然后总结了该领域的发展，重点是用于对人类运动进行分类的算法的进化。本文将讨论在文献中发现的局限性，以及对未来研究方向的建议。
Advances in mobile technology have led to the emergence of the “smartphone”, a new class of device with more advanced connectivity features that have quickly made it a constant presence in our lives. Smartphones are equipped with comparatively advanced computing capabilities, a global positioning system (GPS) receivers, and sensing capabilities (i.e., an inertial measurement unit (IMU) and more recently magnetometer and barometer) which can be found in wearable ambulatory monitors (WAMs). As a result, algorithms initially developed for WAMs that “count” steps (i.e., pedometers); gauge physical activity levels; indirectly estimate energy expenditure and monitor human movement can be utilised on the smartphone. These algorithms may enable clinicians to “close the loop” by prescribing timely interventions to improve or maintain wellbeing in populations who are at risk of falling or suffer from a chronic disease whose progression is linked to a reduction in movement and mobility. The ubiquitous nature of smartphone technology makes it the ideal platform from which human movement can be remotely monitored without the expense of purchasing, and inconvenience of using, a dedicated WAM. In this paper, an overview of the sensors that can be found in the smartphone are presented, followed by a summary of the developments in this field with an emphasis on the evolution of algorithms used to classify human movement. The limitations identified in the literature will be discussed, as well as suggestions about future research directions.