One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes
One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes
复制标题
DOI：
10.1109/jstsp.2016.2535972
复制
发表时间：
2016-08-01
影响因子：
7.5
通讯作者：
Schmitter-Edgecombe, Maureen
中科院分区：
文献类型：
--
作者：
Das, Barnan;Cook, Diane J.;Schmitter-Edgecombe, Maureen
照顾痴呆症患者通常会带来极度的身体和情绪压力，这往往会导致抑郁症。智能家居技术和机器学习技术的进步可以提供创新的解决方案来减轻护理人员的负担。护理人员提供的一项关键服务是提示记忆力有限的人开始并完成日常活动。我们假设传感器技术与机器学习技术相结合可以自动化提供基于提醒的干预的过程。自动化干预的第一步是检测个人何时面临活动困难。我们提出基于一类分类的机器学习方法，用于学习正常活动模式。当我们将这些分类器应用于以前没有见过的活动模式时，分类器能够检测到活动错误，这些错误代表了潜在的提示情况。我们利用从老年参与者那里获得的智能家居传感器数据来验证我们的方法，其中一些参与者在执行日常活动时遇到困难，从而犯了错误。
Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step toward automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.