论文标题
使用尖峰神经网络坐姿识别
Sitting Posture Recognition Using a Spiking Neural Network
论文作者
论文摘要
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来识别坐姿。该系统可以从设计的传感器接收表面压力数据，并为指导用户提供适当的坐姿姿势提供反馈。我们使用液态机器和逻辑回归分类器来构建一个尖峰神经网络，用于对15个坐姿进行分类。为了允许该系统将我们的压力数据读取到尖峰神经元中，我们设计了一种算法来将类似地图的数据编码为余弦率稀疏数据。实验结果由19名参与者的15个坐姿组成，表明我们SNN的预测精度为88.52％。
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%.
