论文标题
在数据中发现行为倾向，以改善人类活动识别
Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition
论文作者
论文摘要
基于传感器的自动评估痴呆症患者的挑战行为是支持选择干预措施的重要任务。但是，由于患者间和患者内的变异性较大，预测诸如冷漠和躁动之类的行为具有挑战性。本文的目的是通过利用患者在一天或一周中的某些时间表现出特定行为的观察来提高识别性能。我们建议通过聚类时间段的注释分布来识别类似行为的此类段。群集中的所有时间段然后由相似的行为组成，因此表明行为倾向（BPD）。我们通过为每个BPD培训分类器来利用BPD。从经验上讲，我们证明，当知道每个时间段的BPD时，活动识别性能可以大大提高。
The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved.
