Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

由错误坐姿引起的身体健康疾病日益严重和普遍，尤其是久坐的学生和上班族。现有的基于视频的方法和基于传感器的方法可以实现高精度，但它们存在侵犯隐私和依赖特定传感器设备等局限性。在这项工作中，我们提出了 Sitsen，一种基于无线的非接触式坐姿识别系统，仅使用射频信号，既不损害隐私，也不需要使用各种特定的传感器。我们证明，Sitsen 只需一个轻巧且低成本的射频识别标签即可成功识别五种习惯性坐姿。直觉是不同的姿势会引起不同的相位变化。由于接收到的相位读数被环境噪声和硬件缺陷破坏，我们采用一系列信号处理方案来获得干净的相位读数。使用滑动窗口方法提取被测相位序列的有效特征并采用适当的机器学习算法，Sitsen 可以实现鲁棒性和高性能。大量实验在一个有 10 名志愿者的办公室进行。结果表明，我们的系统可以识别不同的坐姿，平均准确率为 97.02%。Sitsen 可以实现稳健和高性能。大量实验在一个有 10 名志愿者的办公室进行。结果表明，我们的系统可以识别不同的坐姿，平均准确率为 97.02%。Sitsen 可以实现稳健和高性能。大量实验在一个有 10 名志愿者的办公室进行。结果表明，我们的系统可以识别不同的坐姿，平均准确率为 97.02%。