Prediction of activity mode with global positioning system and accelerometer data
Prediction of activity mode with global positioning system and accelerometer data
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DOI：
10.1249/mss.0b013e318164c407
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发表时间：
2008-05-01
期刊：
影响因子：
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通讯作者：
Craig, Bruce A.
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文献类型：
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作者：
Troped, Philip J.;Oliveira, Marcelo S.;Craig, Bruce A.
目的：这项试点研究的主要目的是评估全球定位系统 (GPS) 和加速计数据的结合预测不同活动模式的效果。方法：十名成年人（七名男性，三名女性；23-51 岁）在步行、慢跑/跑步、骑自行车、直排轮滑或驾驶汽车时同时佩戴 GPS 装置和加速计。判别函数分析用于识别从加速度计计数和步数以及 GPS 速度导出的变量的简约组合，以实现最佳分类模式。总共使用 29 场比赛来制定此分类标准。该标准使用从所有回合的每分钟值的完整集合生成的两个数据集进行了验证。结果：使用“校准”数据进行的模型开发表明，仅两个加速度计变量（中位数计数和步数）就导致 29 场比赛中的 26 场 (90%) 被正确分类。使用每分钟“验证”数据集中的计数和步数 (N = 200) 预测活动模式的成功率为 86.5%。使用来自加速度计和 GPS 的三个变量（中位数、步数和速度），在“校准”数据中的 29 场活动中，有 27 场的分类正确（93%）。在包含 200 分钟的“验证”数据集中，加速度计计数和步数以及 GPS 速度的组合能够正确分类 91% 的观测结果。步行和骑自行车分钟被正确分类的频率最高（96%）。在另一个由活动回合组成的“验证”数据集中，这种变量组合在 43 场比赛中的 42 场中产生了正确的分类（98%）。结论：这项试点研究提供的证据表明，在加速度计监测中添加 GPS 可以在一定程度上改善身体活动模式分类。需要对自由生活的个体进行更大规模的研究，并扩大活动范围，以复制当前的研究结果，并进一步确定使用 GPS 和加速度计进行模式识别的优点。
Purpose: The primary aim of this pilot study was to assess how well the combination of global positioning system (GPS) and accelerometer data predicted different activity modes. Methods: Ten adults (seven male, three female; 23-51 yr) simultaneously wore a GPS unit and accelerometer during bouts of walking, jogging/running, bicycling, inline skating, or driving an automobile. Discriminant function analysis was used to identify a parsimonious combination of variables derived from accelerometer counts and steps and GPS speed that best classified mode. A total of 29 bouts were used to develop this classification criterion. This criterion was validated using two datasets generated from the complete collection of minute-by-minute values from all bouts. Results: Model development with "calibration" data showed that two accelerometer variables alone (median counts and steps) resulted in 26 of 29 bouts (90%) being correctly classified. Prediction of activity mode using counts and steps in a minute-by-minute "validation" dataset (N = 200) was 86.5%. Using three variables from the accelerometer and GPS (median counts, steps and speed) resulted in correct classification in 27 of 29 activity bouts in the "calibration" data (93%). In the "validation" dataset comprising 200 min, the combination of accelerometer counts and steps and GPS speed were able to correctly classify 91% of the observations. Walking and bicycling minutes were correctly classified most frequently (96%). In another "validation" dataset consisting of activity bouts, this combination of variables resulted in correct classification in 42 of 43 bouts (98%). Conclusion: This pilot study provides evidence that the addition of GPS to accelerometer monitoring improves physical activity mode classification to a small degree. Larger studies among free-living individuals and with an expanded range of activities are needed to replicate the current findings and further determine the merits of using GPS with accelerometers for mode identification.