Multi-sensor physical activity recognition in free-living.
Multi-sensor physical activity recognition in free-living.
复制标题
自由生活中的多传感器身体活动识别。
DOI：
10.1145/2638728.2641673
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Lanckriet G
中科院分区：
文献类型：
--
作者：
Ellis K;Godbole S;Kerr J;Lanckriet G
自由生活人群的身体活动监测在公共卫生研究、减肥干预、情境感知推荐系统和辅助技术方面有许多应用。我们提出了一个身体活动识别系统，该系统是从40名佩戴多个传感器7天的女性的自由生活数据集中学习的。多级分类系统首先学习每个传感器的低级码本表示，并使用随机森林分类器来产生每个活动类的分钟级概率。然后，更高级别的HMM层随着时间的推移学习转换模式和活动持续时间，以平滑分钟级别的预测。
Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions.