Ieee Journal of Selected Topics in Signal Processing 1 One-class Classification-based Real-time Activity Error Detection in Smart Homes
Ieee Journal of Selected Topics in Signal Processing 1 One-class Classification-based Real-time Activity Error Detection in Smart Homes
复制标题
DOI：
--
复制
发表时间：
--
期刊：
影响因子：
--
通讯作者：
Barnan Das;D. Cook;N. C. Krishnan;M. Schmitter-Edgecombe
中科院分区：
文献类型：
--
作者：
Barnan Das;D. Cook;N. C. Krishnan;M. Schmitter-Edgecombe
照顾痴呆症患者往往伴随着极度的身体和情绪压力，这往往会导致抑郁。智能家居技术和机器学习技术的进步可以为减轻护理人员的负担提供创新的解决方案。护理人员提供的一项关键服务是促使有记忆限制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提供基于提醒的干预过程。自动化干预的第一步是检测个人何时面临活动困难。我们提出了基于一类分类学习正常活动模式的机器学习方法。当我们将这些分类器应用于以前未见过的活动模式时，分类器能够检测活动错误，这些错误表示潜在的提示情况。我们通过从老年参与者获得的智能家居传感器数据验证了我们的方法，其中一些人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 towards 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.