Facing the gradual ageing society, elderly people living independently are in need of serious attention. In order to assist them to live in a safer environment, the increasing cost of nursing care and the shortage of health-care workers urges the demand of home-based assisted living in recent times. Therefore, home-based health-care has become an active research domain, particularly the abnormal activities detection involving information and communications technologies. This survey paper highlights this kind of technologies that exist for human anomalous behavior detection. It also reviews and discusses the current research trends, their outcomes and effects in elderly care. Our study is mainly focused on dense sensing network based activities and anomaly detection, which are robust to environment change, non-intrusive, user-friendly in the sense that do not require the occupant to wear any devices. From our study, we observe that employing sensor fusion techniques could significantly increases the efficiency of dense sensing network. In addition, sensor fusion models ensure a high level of robustness and effectiveness compared to the traditional methods.

中文翻译：

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基于密集感知网络的养老异常行为检测调查

面对逐渐老龄化的社会，独立生活的老年人需要得到重视。为了帮助他们生活在更安全的环境中，护理费用的增加和医护人员的短缺催生了近来居家辅助生活的需求。因此，居家医疗成为一个活跃的研究领域，尤其是涉及信息和通信技术的异常活动检测。本调查论文重点介绍了用于人类异常行为检测的此类技术。它还回顾和讨论了当前的研究趋势、结果和对老年护理的影响。我们的研究主要集中在基于密集感知网络的活动和异常检测，它们对环境变化具有鲁棒性、非侵入性、用户友好，不需要占用者佩戴任何设备。从我们的研究中，我们观察到采用传感器融合技术可以显着提高密集传感网络的效率。此外，与传统方法相比，传感器融合模型确保了高水平的鲁棒性和有效性。