Monitoring behaviour in smart homes using sensors can offer insights into changes in the independent ability and long-term health of residents. Passive Infrared motion sensors (PIRs) are standard, however may not accurately track the full duration of movement. They also require line-of-sight to detect motion which can restrict performance and ensures they must be visible to residents. Channel State Information (CSI) is a low cost, unintrusive form of radio sensing which can monitor movement but also offers opportunities to generate rich data. We have developed a novel, self-calibrating motion detection system which uses CSI data collected and processed on a stock Raspberry Pi 4. This system exploits the correlation between CSI frames, on which we perform variance analysis using our algorithm to accurately measure the full period of a resident's movement. We demonstrate the effectiveness of this approach in several real-world environments. Experiments conducted demonstrate that activity start and end time can be accurately detected for motion examples of different intensities at different locations.
翻译:智能家庭使用传感器的监测行为可以使人们深入了解居民独立能力和长期健康的变化。被动红外运动传感器(PIRs)是标准的,但可能无法准确跟踪整个移动时间,还需要有直观的观察来检测运动,这可以限制工作绩效,确保居民能够看到这些运动。频道国家信息是一种低成本、无侵扰的无线电遥感形式,可以监测运动,但也提供了生成丰富数据的机会。我们开发了一个新型的自我校准运动探测系统,该系统使用在Raspberry Pi 4. 鱼群中收集和处理的CSI数据,该系统利用CSI框架之间的相互关系,我们利用这一框架进行差异分析,利用我们的算法准确测量居民整个移动时间。我们在若干现实世界环境中展示了这一方法的有效性。所进行的实验表明,不同地点不同强度的动作实例可以精确地探测活动开始和结束时间。