Device-free and device-bound activity recognition using radio signal strength
Device-free and device-bound activity recognition using radio signal strength
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DOI：
10.1145/2459236.2459254
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发表时间：
2013-03
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影响因子：
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通讯作者：
Markus Scholz;T. Riedel;Mario Hock;M. Beigl
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文献类型：
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作者：
Markus Scholz;T. Riedel;Mario Hock;M. Beigl
背景资料：我们调查直接使用802.15.4无线电信号强度指示（RSSI）的人类活动识别时，1）用户携带无线节点（设备绑定），当2）用户在无线传感器网络（WSN）中移动没有无线传感器网络节点（设备自由）。我们调查识别的可行性方面的网络拓扑结构，主题和房间的几何形状（门打开，半，关闭）。方法：在一个2人的办公室8个无线节点安装在一个三维拓扑结构。两名受试者在臀部配备了传感器节点。当受试者进行6种不同的活动或房间是空的时，记录加速度和RSSI。我们应用机器学习进行分析，并将我们的结果与加速度数据进行比较。结果：所有节点的10倍交叉验证给出了0.896（设备绑定），0.894（无设备）和0.88（加速度计）的准确度。拓扑调查表明，类似的精度可以达到只有5（设备绑定）或4（无设备）选定的节点。将来自一个受试者的训练数据应用于另一个受试者，反之亦然，在RSSI上显示出比在加速度上更高的识别差异。门状态的改变对两个系统的影响小于主体改变;门关闭时的影响最小。结论：802.15.4 RSSI适合于活动识别。3D拓扑在活动类型方面很有帮助。对受试者的歧视似乎是可能的。实际的系统不仅要适应长期的环境分散，还要考虑典型的几何变化。必须开发适应性强的识别模型。
Background: We investigate direct use of 802.15.4 radio signal strength indication (RSSI) for human activity recognition when 1) a user carries a wireless node (device-bound) and when 2) a user moves in the wireless sensor net (WSN) without a WSN node (device-free). We investigate recognition feasibility in respect to network topology, subject and room geometry (door open, half, closed). Methods: In a 2 person office room 8 wireless nodes are installed in a 3D topology. Two subjects are outfitted with a sensor node on the hip. Acceleration and RSSI are recorded while subject performs 6 different activities or room is empty. We apply machine learning for analysis and compare our results to acceleration data. Results: 10-fold cross-validation with all nodes gives accuracies of 0.896 (device-bound), 0.894 (device-free) and 0.88 (accelerometer). Topology investigation reveals that similar accuracies may be reached with only 5 (device-bound) or 4 (device-free) selected nodes. Applying trained data from one subject to the other and vice-versa shows higher recognition difference on RSSI than on acceleration. Changing of door state has smaller effect on both systems than subject change; with least impact when door is closed. Conclusion: 802.15.4 RSSI suited for activity recognition. 3D topology is helpful in respect to type of activities. Discrimination of subjects seems possible. Practical systems must adapt no only to long-term environmental dispersion but consider typical geometric changes. Adaptable, robust recognition models must be developed.