To increase the quality of citizens' lives, we designed a personalized smart chair system to recognize sitting behaviors. The system can receive surface pressure data from the designed sensor and provide feedback for guiding the user towards proper sitting postures. We used a liquid state machine and a logistic regression classifier to construct a spiking neural network for classifying 15 sitting postures. To allow this system to read our pressure data into the spiking neurons, we designed an algorithm to encode map-like data into cosine-rank sparsity data. The experimental results consisting of 15 sitting postures from 19 participants show that the prediction precision of our SNN is 88.52%.

翻译：为了提高公民生活的质量,我们设计了一个个性化的智能椅子系统,以识别坐坐行为。该系统可以从设计好的传感器接收表面压力数据,并提供反馈,引导用户进入正确的坐姿。我们用一个液态机器和一个后勤回归分类器来构建一个螺旋神经网络,用于对15个坐姿进行分类。为了使该系统能够将我们的压力数据读入神经元,我们设计了一个算法,将类似于地图的数据编码成共弦级的散居数据。由19名参与者的15个坐姿组成的实验结果显示,我们的SNN的预测精确度是88.52%。