A comparison of activity classification in younger and older cohorts using a smartphone
A comparison of activity classification in younger and older cohorts using a smartphone
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DOI：
10.1088/0967-3334/35/11/2269
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发表时间：
2014-11-01
影响因子：
3.2
通讯作者：
Redmond, Stephen J.
中科院分区：
文献类型：
--
作者：
Del Rosario, Michael B.;Wang, Kejia;Redmond, Stephen J.
人类活动的自动识别作为估计能量消耗的手段是有用的，并且具有用于跌倒检测和预测的潜力。智能手机作为一种无处不在的设备的出现，为利用其嵌入式传感器、计算能力和数据连接作为持续健康监测平台提供了机会。在本文描述的研究中，37名老年人（83.9 +/- 3.4岁）进行了一系列日常生活活动（ADL），同时将智能手机（包含三轴加速度计、三轴陀螺仪和气压传感器）放在裤子的前口袋里。这些结果与之前进行的一项类似试验进行了比较，其中20名年轻人（21.9 +/- 1.65岁）被要求使用相同的智能手机进行相同的ADL（同样在裤子的前口袋里）。在每次试验中，参与者被要求进行几项活动（站，坐，躺，在平地上行走，上下楼梯，乘坐电梯上下）。在每个采集会话期间，记录内部传感器信号，随后用于基于决策树算法开发活动分类器，该决策树算法将ADL分类为类似于1.25 s的时期。当使用留一交叉验证程序对年轻队列进行训练和测试时，获得了80.9% +/- 9.57%（kappa = 0.75 +/- 0.12）的总分类灵敏度。再次使用交叉验证对较老的队列进行重新训练和测试，得到了82.0% +/- 8.88%的可比总类敏感性（kappa = 0.74 +/- 0.12）。当与年轻组一起训练并对老年组进行测试时，总类别敏感度为69.2% +/- 24.8%。（95%置信区间[69.6%，70.6%]）和kappa = 0.60 +/- 0.27（95%置信区间[0.58，0.59]）。当对老年组进行训练并对年轻组进行测试时，总类别敏感度为80.5% +/- 6.80%。（95%置信区间[79.0%，80.6%]）和kappa = 0.74 +/- 0.08（95%置信区间[0.73，0.75]）。所开发的决策树分类器的实例作为软件应用程序在智能手机上实现。它能够在单次电池充电的情况下执行17小时的实时活动分类，说明智能手机技术提供了一个可行的平台，可以在其上执行长期活动监测。
Automatic recognition of human activity is useful as a means of estimating energy expenditure and has potential for use in fall detection and prediction. The emergence of the smartphone as a ubiquitous device presents an opportunity to utilize its embedded sensors, computational power and data connectivity as a platform for continuous health monitoring. In the study described herein, 37 older people (83.9 +/- 3.4 years) performed a series of activities of daily living (ADLs) while a smartphone (containing a triaxial accelerometer, triaxial gyroscope and barometric pressure sensor) was placed in the front pocket of their trousers. These results are compared to a similar trial conducted previously in which 20 young people (21.9 +/- 1.65 years) were asked to perform the same ADLs using the same smartphone (again in the front pocket of their trousers).In each trial, the participants were asked to perform several activities (standing, sitting, lying, walking on level ground, up and down staircases, and riding an elevator up and down) in a free-living environment. During each acquisition session, the internal sensor signals were recorded and subsequently used to develop activity classifiers based on a decision tree algorithm that classified ADL in epochs of similar to 1.25 s. When training and testing with the younger cohort, using a leave-one-out cross validation procedure, a total classification sensitivity of 80.9% +/- 9.57% (kappa = 0.75 +/- 0.12) was obtained. Retraining and testing on the older cohort, again using cross validation, gives a comparable total class sensitivity of 82.0% +/- 8.88% (kappa = 0.74 +/- 0.12).When trained with the younger group and tested on the older group, a total class sensitivity of 69.2% +/- 24.8% (95% confidence interval [69.6%, 70.6%]) and kappa = 0.60 +/- 0.27 (95% confidence interval [0.58, 0.59]) was obtained. When trained on the older group and tested on the younger group, a total class sensitivity of 80.5% +/- 6.80% (95% confidence interval [79.0%, 80.6%]) and kappa = 0.74 +/- 0.08 (95% confidence interval [0.73, 0.75]) was obtained.An instance of the decision tree classifier developed was implemented on the smartphone as a software application. It was capable of performing real-time activity classification for a period of 17 h on a single battery charge, illustrating that smartphone technology provides a viable platform on which to perform long-term activity monitoring.