A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation.
A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation.
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DOI：
10.2196/mhealth.8516
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发表时间：
2018-04-20
影响因子：
5
通讯作者：
Esliger DW
中科院分区：
文献类型：
--
作者：
Magistro D;Sessa S;Kingsnorth AP;Loveday A;Simeone A;Zecca M;Esliger DW
不幸的是，鉴于难以实现持续的生活方式行为的困难，全球促进“多少”体育活动的努力在很大程度上是不成功的。生活方式的行为（即，在哪里，何时和与谁），以确定承诺干预目标。
这项研究的目的是开发和实施一种创新的算法，以确定使用邻近传感器与广泛使用的体育活动监视器相结合的“何处”体育活动。
总共将19个蓝牙信标放置在多级混合使用的建筑物中。使用基于图形生成和贝叶斯过滤器的原始和创新算法分析数据的不同步行速度和居住时间。
线性回归模型揭示了信标衍生的位置和地面真相跟踪时间之间的显着相关性，并且具有内部相关性表明拟合度高（R2 = .9780）。较长的住宅时间（> 100 s；误差<10％，R2 = .9775）。制造商限制为0.1 Hz。
这项研究表明，我们的算法可以准确预测个人在室内环境中的位置。护理计划（例如，患者 - 临床医生流，患者 - 临床医生的互动）。
Unfortunately, global efforts to promote “how much” physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets. The aim of this study was to develop and implement an innovative algorithm to determine “where” physical activity occurs using proximity sensors coupled with a widely used physical activity monitor. A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters. Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R2=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R2=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer. This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of “context sensing” will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction).
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影响因子：
4.4
作者：
Kirchner, Thomas R.;Shiffman, Saul
通讯作者：
Shiffman, Saul
DOI：
10.3390/s17061318
发表时间：
2017-06-07
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Castillo-Cara M;Lovón-Melgarejo J;Bravo-Rocca G;Orozco-Barbosa L;García-Varea I
通讯作者：
García-Varea I
DOI：
10.1186/1479-5868-9-89
发表时间：
2012-07-30
影响因子：
8.7
作者：
Kerr, Jacqueline;Sallis, James F.;King, Abby C.
通讯作者：
King, Abby C.
DOI：
10.3390/s17081749
发表时间：
2017-07-31
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
García Ó;Prieto J;Alonso RS;Corchado JM
通讯作者：
Corchado JM
影响因子：
5.5
作者：
Krenn, Patricia J.;Titze, Sylvia;Oja, Pekka;Jones, Andrew;Ogilvie, David
通讯作者：
Ogilvie, David