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首页> 外文会议>20th European conference on artificial intelligence >Confidence: Ubiquitous Care System to Support Independent Living
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Confidence: Ubiquitous Care System to Support Independent Living

机译：信心：无处不在的医疗体系支持独立生活

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The Confidence system aims at helping the elderly stay independent longer by detecting falls and unusual movement which may indicate a health problem. The system uses location sensors and wearable tags to determine the coordinates of the user's body parts, and an accelerometer to detect fall impact and movement. Machine learning is combined with domain knowledge in the form of rules to recognize the user's activity. The fall detection employs a similar combination of machine learning and domain knowledge. It was tested on five atypical falls and events that can be easily mistaken for a fall. We show in the paper and demo that neither sensor type can correctly recognize all of these events on its own, but the combination of both sensor types yields highly accurate fall detection. In addition, the detection of unusual movement can observe both the user's micro-movement and macro-movement. This makes it possible for the Confidence system to detect most types of threats to the user's health and well-being manifesting in his/her movement.
机译：Confidence系统旨在通过检测可能指示健康问题的跌倒和异常运动来帮助老年人更长久地保持独立。该系统使用位置传感器和可穿戴标签来确定用户身体部位的坐标，并使用加速度计来检测跌倒的撞击和移动。机器学习与规则知识形式的领域知识相结合，以识别用户的活动。跌倒检测采用机器学习和领域知识的类似组合。在五个非典型跌落和容易被误认为是跌倒的事件上进行了测试。我们在论文和演示中表明，这两种传感器类型都不能单独正确识别所有这些事件，但是两种传感器类型的组合可产生高度准确的跌倒检测。另外，异常运动的检测可以观察用户的微小运动和宏观运动。这使Confidence系统有可能检测到对用户健康和福祉表现出威胁的大多数类型的威胁。

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