运用开端神经网络进行人体姿态识别

doi:10.11959/j.issn.1000-436x.2017262

通信学报 ›› 2017, Vol. 38 ›› Issue (Z2): 122-128.doi: 10.11959/j.issn.1000-436x.2017262

运用开端神经网络进行人体姿态识别

柴铎^1,²,徐诚^1,²,何杰^1,²,张少阳^1,²,段世红^1,²,齐悦^1,²()

¹ 北京科技大学计算机与通信工程学院，北京 100083
² 材料领域知识工程北京市重点实验室，北京100083

出版日期:2017-11-01 发布日期:2018-06-07
作者简介:柴铎（1995-），男，陕西府谷人，北京科技大学工程师，主要研究方向为聊天机器人、机器翻译等深度学习在自然语言处理领域的应用。|徐诚（1988-），男，辽宁开原人，北京科技大学博士生，主要研究方向为室内定位、无线通信网络、模式识别等。|何杰（1983-），男，浙江台州人，博士，北京科技大学副教授，主要研究方向为室内定位、无线通信网络、模式识别等。|张少阳（1991-），男，河北石家庄人，北京科技大学硕士生，主要研究方向为模式识别等。|段世红（1973-），女，山西太原人，博士，北京科技大学副教授，主要研究方向为计算机软件、无线通信网络、模式识别等。|齐悦（1975-），女，辽宁沈阳人，博士，北京科技大学副教授，主要研究方向为计算机软件、无线通信网络、模式识别等。
基金资助:
国家重点研发计划基金资助项目(2016YFC0901303);国家自然科学基金资助项目(61671056);国家自然科学基金资助项目(61302065);国家自然科学基金资助项目(61304257);国家自然科学基金资助项目(61402033);北京市自然科学基金资助项目(4152036);天津市重大科技专项基金资助项目(16ZXCXSF00150)

Inception neural network for human activity recognition using wearable sensor

Duo CHAI^1,²,Cheng XU^1,²,Jie HE^1,²,Shao-yang ZHANG^1,²,Shi-hong DUAN^1,²,Yue QI^1,²()

¹ School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China
² Beijing Key Laboratory of Knowledge Engineering for Materials Science,Beijing 100083,China

Online:2017-11-01 Published:2018-06-07
Supported by:
The National Key R＆D Program of China(2016YFC0901303);The National Natural Science Foundation of China(61671056);The National Natural Science Foundation of China(61302065);The National Natural Science Foundation of China(61304257);The National Natural Science Foundation of China(61402033);Beijing Natural Science Foundation Project(4152036);Tianjin Special Program for Science and Technology(16ZXCXSF00150)

摘要/Abstract

摘要：

通过迁移深度神经网络在图像识别方面的经验，提出了一种基于Inception神经网络和循环神经网络结合的深度学习模型（InnoHAR），该模型端对端地输入多通道传感器的波形数据，利用 1×1 卷积对多通道数据的有机组合，不同尺度的卷积提取不同尺度的波形特征，最大池化过滤微小扰动造成的假阳性，结合时间特征提取（GRU）为时序特征建模，充分利用数据特征完成分类任务。相比已知最优的神经网络模型，在识别准确度上有近 3%的提升，达到了state-of-the-art的水平，同时可以保证低功耗嵌入式平台的实时预测，且在网络结构组成上更加丰富，具有更大的潜力和挖掘空间。

关键词: 人体姿态识别, 行为识别, 开端神经网络, 可穿戴传感器网络, 深度学习

Abstract:

The experience from computer vision was learned,an innovative neural network model called InnoHAR (inception neural network for human activity recognition) based on the inception neural network and recurrent neural network was put forward,which started from an end-to-end multi-channel sensor waveform data,followed by the 1×1 convolution for better combination of the multi-channel data,and the various scales of convolution to extract the waveform characteristics of different scales,the max-pooling layer to prevent the disturbance of tiny noise causing false positives,combined with the feature of GRU helped to time-sequential modeling,made full use of the characteristics of data classification task.Compared with the state-of-the-art neural network model,the InnoHAR model has a promotion of 3% in the recognition accuracy,which has reached the state-of-the-art on the dataset we used,at the same time it still can guarantee the real-time prediction of low-power embedded platform,also with more space for future exploration.

Key words: human activity recognition, activity recognition, inception neural network, wearable sensor network, deep learning

中图分类号:

TP391

柴铎,徐诚,何杰,张少阳,段世红,齐悦. 运用开端神经网络进行人体姿态识别[J]. 通信学报, 2017, 38(Z2): 122-128.

Duo CHAI,Cheng XU,Jie HE,Shao-yang ZHANG,Shi-hong DUAN,Yue QI. Inception neural network for human activity recognition using wearable sensor[J]. Journal on Communications, 2017, 38(Z2): 122-128.

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参考文献 12

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案例	nul	Open Door 1	Open Door 2	Close Door 1	Close Door 2	Open Fridge	Close Fridge	Open Dishwasher	Close Dishwasher	Open Drawer 1	Close Drawer 1	Open Drawer 2	Close Drawer 2	Open Drawer 3	Close Drawer 3	Clean Table	Drink from Cup	Toggle Switch
null	7724	13	6	11	3	57	17	38	33	9	18	8	4	16	8	48	116	35
Open Door 1	4	40	0	8	0	0	0	0	0	0	0	0	0	0	0	0	1	0
Open Door 2	2	0	88	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0
Close Door 1	0	5	0	41	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Close Door 2	2	0	1	0	76	0	0	0	0	0	0	0	0	0	0	0	0	0
Open Fridge	14	0	0	0	0	143	8	1	0	0	0	0	0	0	0	0	0	0
Close Fridge	32	0	0	0	0	26	135	0	0	0	0	0	0	0	0	0	0	0
Open Dishwasher	9	0	0	0	0	1	0	60	3	0	0	0	0	0	0	0	3	0
Close Dishwasher	17	0	0	0	0	1	0	0	40	0	0	0	1	0	0	0	0	0
Open Drawer 1	2	0	0	0	0	0	0	1	0	24	6	10	3	1	0	0	0	0
Close Drawer 1	5	0	0	0	0	0	0	0	1	4	18	1	2	0	0	0	0	0
Open Drawer 2	3	0	0	0	0	0	0	0	0	0	0	18	1	4	0	0	0	0
Close Drawer 2	3	0	0	0	0	0	0	0	0	1	0	0	15	1	6	0	0	0
Open Drawer 3	6	0	0	0	0	0	0	0	0	0	0	1	0	42	13	0	0	0
Close Drawer 3	14	0	0	0	0	0	0	0	0	0	0	2	0	3	34	0	0	0
Clean Table	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	51	0	0
Drink from Cup	72	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	197	0
Toggle Switch	10	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	70

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运用开端神经网络进行人体姿态识别

Inception neural network for human activity recognition using wearable sensor

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