Temporal Alignment Improves Feature Quality: An Experiment on Activity Recognition with Accelerometer Data
Temporal Alignment Improves Feature Quality: An Experiment on Activity Recognition with Accelerometer Data
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DOI：
10.1109/cvprw.2018.00075
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发表时间：
2018-06
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影响因子：
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通讯作者：
Hongjun Choi;Qiao Wang;M. Toledo;P. Turaga;M. Buman;Anuj Srivastava
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作者：
Hongjun Choi;Qiao Wang;M. Toledo;P. Turaga;M. Buman;Anuj Srivastava
活动识别已经受到了许多研究领域的关注，如人类表现增强、健康促进和人机交互。然而，从加速度计数据中识别活动仍然是一个具有挑战性的问题，因为对采样率的敏感性、数据的不对齐以及临床相关人群中活动的变异性增加。为了解决这些问题，我们采用了功能分析的方法，考虑了运动中的非弹性速率变化。在为给定的最终用途利用健壮的机器学习管道之前，整体框架将活动类中的时间可变性排除在外。该方法已在7个班级50个科目的日常活动中进行了评估。结果表明，所提出的方法在分离时间速率不同的相似类方面取得了改进的性能，并且对窗口长度的变化具有更高的鲁棒性。这些结果表明，时间对齐应该被认为是活动识别管道的核心部分。
Activity recognition has been receiving significant attention from a variety of research areas such as human performance enhancement, health promotion, and human computer interaction. However, recognizing activities from accelerometer data still remains a challenging problem due to sensitivity to sampling rates, misalignment of data, and increased variability in activities among clinically relevant populations. In order to solve these issues, we adopt methods from functional analysis, which consider non-elastic rate variations in movement. The overall framework factors out temporal variability within activity classes, before leveraging robust machine learning pipelines for a given end-use. The proposed approach has been evaluated on 7 classes of everyday activities with 50 subjects. The results indicate that proposed approach achieves improved performance with the improvements observed in separating similar classes that differ in temporal rates, and also demonstrate higher robustness to change in window lengths. These results suggest that temporal alignment should be considered a core part of activity recognition pipelines.