基于软件定义智能的睡眠动作识别

doi:10.12263/DZXB.20200069

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电子学报 ›› 2021, Vol. 49 ›› Issue (1) : 85-89. DOI: 10.12263/DZXB.20200069

学术论文

基于软件定义智能的睡眠动作识别

汪成亮, 郑诚, 曾卓

作者信息 +

Sleep Action Recognition Based on Software-Defined Intelligence

WANG Cheng-liang, ZHENG Cheng, ZENG Zhuo

Author information +

文章历史 +

摘要

基于软件定义智能层次化模型设计了一种睡眠动作识别系统，该系统可通过规则推理来应对智能环境中的各种变化因素.设计了一种时间队列实时提取动作特征来训练模型，提出了一种规则提取算法从该模型中提取系统所需规则.该系统基于这些规则可识别9种睡眠动作，每种动作的识别精确率均可达到96%以上，总识别准确率达到98.9%，且比其它系统适应性更强.实验结果表明，该系统通过更新规则可快速应对节点位置、节点数量和用户需求的变化.

Abstract

Based on the hierarchical model of SDI (Software-Defined Intelligence), a system for action recognition during sleep is designed to deal with various changing factors in smart environment through rule-based reasoning. A time queue is designed to extract the characteristics of actions in real-time to train the model, and a rule extraction algorithm is proposed to extract the rules required by the system from the model. Depending on these rules,the proposed system can recognize nine types of sleep actions: The recognition precision of each type can exceed 96%; the total recognition accuracy can reach 98.9%. Importantly, it has more robust adaptability than other systems. Experimental results show that the system can update rules for quickly adapting to changes in node position, the number of nodes, and user requirements.

导出引用

汪成亮, 郑诚, 曾卓. 基于软件定义智能的睡眠动作识别[J]. 电子学报, 2021, 49(1): 85-89. https://doi.org/10.12263/DZXB.20200069

WANG Cheng-liang, ZHENG Cheng, ZENG Zhuo. Sleep Action Recognition Based on Software-Defined Intelligence[J]. Acta Electronica Sinica, 2021, 49(1): 85-89. https://doi.org/10.12263/DZXB.20200069

中图分类号： TP181

参考文献

[1] 汪成亮,王小均.基于三轴传感器的老年人日常活动识别[J].电子学报,2017,45(3):570-576. WANG Cheng-liang,WANG Xiao-jun.Daily activity recognition based on triaxial accelerometer of elderly people[J].Acta Electronica Sinica,2017,45(3):570-576.(in Chinese)
[2] Long X,Fonseca P,Haakma R,et al.Actigraphy-based sleep/wake detection for insomniacs[A].International Conference on Wearable and Implantable Body Sensor Networks (BSN)[C].Eindhoven:IEEE,2017.1-4.
[3] Liu J J,Huang M C,Xu W,et al.Breathsens:A continuous on-bed respiratory monitoring system with torso localization using an unobtrusive pressure sensing array[J].IEEE Journal of Biomedical and Health Informatics,2014,19(5):1682-1688.
[4] Wang H,Zhang D,Wang Y,et al.RT-fall:A real-time and contactless fall detection system with commodity WiFi devices[J].IEEE Transactions on Mobile Computing,2016,16(2):511-526.
[5] 马森,赵文,袁崇义,等.基于规则推理的语义检索若干关键技术研究[J].电子学报,2013,41(5):977-981. MA Sen,ZHAO Wen,YUAN Chong-yi,et al.Research on critical technologies of semantic retrieval based on rule reasoning[J].Acta Electronica Sinica,2013,41(5):977-981.(in Chinese)
[6] Xue Y,Jin L.Discrimination between upstairs and downstairs based on accelerometer[J].IEICE Transactions on Information and Systems,2011,94(6):1173-1177.
[7] Cao Y,Wang F,Lu X,et al.Contactless body movement recognition during sleep via WiFi signals[J].IEEE Internet of Things Journal,2020,7(3):2028-2037.
[8] Zahradka N,cheol Jeong I,Searson P C.Distinguishing positions and movements in bed from load cell signals[J].Physiological Measurement,2018,39(12):125001.