面向帕金森病的多模态异构协同感知方法

doi:10.3969/j.issn.0372-2112.2018.03.022

电子学报 ›› 2018, Vol. 46 ›› Issue (3): 659-664.DOI: 10.3969/j.issn.0372-2112.2018.03.022

面向帕金森病的多模态异构协同感知方法

杨晓东^1,2,3,4, 陈益强^1,2,3, 于汉超^1,3,4,6, 张迎伟^1,2,3,4, 钟习^1,2,3,4, 胡子昂^1,2,3,4, 刘弘^5,6

1. 中国科学院计算技术研究所, 北京 100190;
2. 中国科学院大学, 北京 100049;
3. 移动计算与新型终端北京市重点实验室, 北京 100190;
4. 帕金森病研究北京市重点实验室, 北京 100053;
5. 山东师范大学信息科学与工程学院, 山东济南 250358;
6. 山东省分布式计算机软件新技术重点实验室, 山东济南 250014

收稿日期:2016-11-11 修回日期:2017-05-04 出版日期:2018-03-25
- 通讯作者:
- 陈益强
- 作者简介:
- 杨晓东,男,1991年生于山东威海.现为中国科学院计算技术研究所博士研究生.主要研究方向为人机交互与普适计算.E-mail:yangxiaodong@ict.ac.cn;于汉超,男,1985年生于山东潍坊.现为中国科学院计算技术研究所助理研究员.主要研究方向为人机交互与普适计算.E-mail:yuhanchao@ict.ac.cn;张迎伟,女,1992年生于山东菏泽.现为中国科学院计算技术研究所硕士研究生.主要研究方向为人机交互与普适计算.E-mail:zhangyingwei@ict.ac.cn;钟习,女,1991年生于山东枣庄.现为中国科学院计算技术研究所硕士研究生.主要研究方向为人机交互与普适计算.E-mail:zhongxi@ict.ac.cn;胡子昂,男,1991年生于内蒙古巴彦淖尔.现为中国科学院计算技术研究所硕士研究生.主要研究方向为人机交互与普适计算.E-mail:huziang@ict.ac.cn;刘弘,女,1955年生于山东泰安.现为山东师范大学信息科学与工程学院教授,山东省分布式计算机软件新技术重点实验室主任.主要研究方向为人工智能与计算机辅助设计.E-mail:hongliu@sdnu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61502456，No.61572471）; 北京市科学技术委员会资助 (No.Z161100000216140，No.Z171100000117013）; 国家重点研发计划 (No.2016YFB1001401）

Multimode Heterogeneous Collaborative Sensing Method for Parkinson's Disease

YANG Xiao-dong^1,2,3,4, CHEN Yi-qiang^1,2,3, YU Han-chao^1,3,4,6, ZHANG Ying-wei^1,2,3,4, ZHONG Xi^1,2,3,4, HU Zi-ang^1,2,3,4, LIU Hong^5,6

1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China;
4. Beijing Key Laboratory for Parkinson's Disease, Beijing 100053, China;
5. School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong 250358, China;
6. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technonogy, Jinan, Shandong 250014, China

Received:2016-11-11 Revised:2017-05-04 Online:2018-03-25 Published:2018-03-25
- Corresponding author:
- CHEN Yi-qiang
- Supported by:
- National Natural Science Foundation of China (No.61502456, No.61572471); Beijing Municipal Science & Technology Commission (No.Z161100000216140, No.Z171100000117013); National Key Research and Development Program of China (No.2016YFB1001401)

摘要/Abstract

摘要： 利用多模态异构传感器组成身体感知网络（body sensing networks），是连续感知用户日常行为的重要方法之一，但是能源消耗问题一直是限制其发展的主要原因。本文提出了一种面向帕金森病的多模态异构协同感知方法，以降低用户日常行为感知过程中的功耗.该方法将行为感知分为行为识别与状态监测，基于信息论确定识别或监测不同行为的最优传感器组合，进而利用一个多分类器建模的行为识别模型与多个二分类器建模的状态监测模型感知用户行为.通过在公开两个数据集上的实验可以看出，与传统的传感器全部持续工作的方法相比，该方法能够在保证对用户行为有效感知的同时，降低了数据传输和模型计算的功耗（MHEALTH上约40%，PAMAP2上约15%），从而延长感知网络的寿命，实现长时间持续的用户日常行为感知.

关键词: 普适计算, 协同感知, 行为识别, 帕金森病, 多模态异构, 传感器, 物联网

Abstract:

Using multimodal heterogeneous sensors to form body sensing networks (BSNs) is one of the most important ways to continuously sensing users' daily activities, but high energy consumption is the main reason for restricting its development. This paper presents a multimode heterogeneous collaborative sensing method for Parkinson's disease to reduce the energy consumption in sensing the daily activities by BSNs. The proposed method divides activity recognition into two sub-tasks which contain activity detection and status monitoring. And it uses one multi-classifier to model activity detection task and several binary classifiers to model status monitoring tasks, which are based on the chosen optimal sensor sets. Experimental results on two public dataset show that comparing with the conventional method whose sensors run all the time, the energy consumption on data transportation and model computation is reduced by 40% in MHEALTH and 15% in PAMPA2 approximately without losing activity-sensing accuracy. Thus it can help extend the lifetime of BSNs to sense users' daily activities long-termly and continuously.

Key words: pervasive computing, collaborative sensing, activity recognition, Parkinson's disease, multimode heterogeneous, sensors, Internet of things

中图分类号:

TP391

杨晓东, 陈益强, 于汉超, 等. 面向帕金森病的多模态异构协同感知方法[J]. 电子学报, 2018, 46(3): 659-664.

YANG Xiao-dong, CHEN Yi-qiang, YU Han-chao, et al. Multimode Heterogeneous Collaborative Sensing Method for Parkinson's Disease[J]. Acta Electronica Sinica, 2018, 46(3): 659-664.

参考文献

[1] BACHLIN M,PLOTNIK M,ROGGEN D,et al.Wearable assistant for Parkinson's disease patients with the freezing of gait symptom[J].IEEE Transactions on Information Technology in Biomedicine,2010,14(2):436-446.
[2] FERSTER M L,MAZILU S,TROSTER G.Gait parameters change prior to freezing in Parkinson's disease:a data-driven study with wearable inertial units[A].Proceedings of the 10th EAI International Conference on Body Area Networks[C].Sydney,Australia:ICST,2015.159-166.
[3] PIERLEONI P,PALMA L,BELLI A,et al.A real-time system to aid clinical classification and quantification of tremor in Parkinson's disease[A].2014 IEEE-EMBS International Conference on Biomedical and Health Informatics[C].Valencia,Spain:IEEE,2014.113-116.
[4] AHLRICHS C,SAMA A,LAWO M,et al.Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients[J].Medical & Biological Engineering & Computing,2016,54(1):223-233.
[5] TRIPOLITI E E,TZALLAS A T,TSIPOURAS M G,et al.Automatic detection of freezing of gait events in patients with Parkinson's disease[J].Computer Methods and Programs in Biomedicine,2013,110(1):12-26.
[6] DEL D S,GODFREY A,ROCHESTER L.Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson's disease:toward clinical and at home use[J].IEEE Journal of Biomedical and Health Informatics,2015,20(3):838-847.
[7] MOKAYA F,LUCAS R,NOH H Y,et al.Myovibe:vibration based wearable muscle activation detection in high mobility exercises[A].Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing[C].Osaka,Japan:ACM,2015.27-38.
[8] EFTAXIAS K,ENSHAEIFAR S,GEMAN O,et al.Detection of Parkinson's tremor from EMG signals;a singular spectrum analysis approach[A].2015 IEEE International Conference on Digital Signal Processing[C].Singapore:IEEE,2015.398-402.
[9] RAHMAN T,ADAMS A T,RAVICHAMDRAN R V,et al.Dopplesleep:a contactless unobtrusive sleep sensing system using short-range doppler radar[A].Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing[C].Osaka,Japan:ACM,2015.39-50.
[10] GOETZ C G,TILLEY B C,SHAFTMAN S R,et al.Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS):scale presentation and clinimetric testing results[J].Movement Disorders,2008,23(15):2129-2170.
[11] GEDIK B,LIU L,YU P S.ASAP:an adaptive sampling approach to data collection in sensor networks[J].IEEE Transactions on Parallel and Distributed Systems,2007,18(12):1766-1783.
[12] WILLETT R,MARTIN A,NOWAK R.Backcasting:adaptive sampling for sensor networks[A].Proceedings of the 3rd international symposium on Information processing in sensor networks[C].Berkeley,California,USA:ACM,2004.124-133.
[13] NGAI E C H,XIONG J.Adaptive collaborative sensing using mobile phones and stationary sensors[A].2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops[C].Hong Kong,China:IEEE,2011.280-285.
[14] KULLBACK S,LEIBLER R A.On information and sufficiency[J].The Annals of Mathematical Statistics,1951,22(1):79-86.
[15] BANOS O,GARCIA R,HOLGADO-TERRIZA J A,et al.Mhealthdroid:a novel framework for agile development of mobile health applications[A].International Workshop on Ambient Assisted Living[C].Málaga,Spain:Springer International Publishing,2014.91-98.
[16] BANOS O,VILLALONGA C,GARCIA R,et al.Design,implementation and validation of a novel open framework for agile development of mobile health applications[J].Biomedical Engineering Online,2015,14(S2):1-20.
[17] REISS A,STRICKER D.Introducing a new benchmarked dataset for activity monitoring[A].The 16th IEEE International Symposium on Wearable Computers[C].Newcastle,UK:IEEE,2012.108-109.
[18] REISS A,STRICKER D.Creating and benchmarking a new dataset for physical activity monitoring[A].Proceedings of the 5th International Conference on Pervasive Technologies Related to Assistive Environments[C].New York,USA:ACM,2012.40.
[19] HU L,CHEN Y Q,WANG S Q,et al.b-COELM:A fast,lightweight and accurate activity recognition model for mini-wearable devices[J].Pervasive and Mobile Computing,2014,15(1):200-214.
[20] FALLAHI A,HOSSAIN E,ALFA A S.QoS and energy trade off in distributed energy-limited mesh/relay networks:A queuing analysis[J].IEEE Transactions on Parallel and Distributed Systems,2006,17(6):576-592.

面向帕金森病的多模态异构协同感知方法

Multimode Heterogeneous Collaborative Sensing Method for Parkinson's Disease

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	邓海刚, 王传旭, 李成伟, 林晓萌. 深度学习框架下群组行为识别算法综述[J]. 电子学报, 2022, 50(8): 2018-2036.
[2]	魏振春, 傅宇, 马仲军, 吕增威, 石雷, 张本宏. 带时间窗的无线可充电传感器网络多目标路径规划算法[J]. 电子学报, 2022, 50(8): 1819-1829.
[3]	陈嘉兴, 程杰, 董云玲, 刘志华. 基于弯曲声线和测距修正的水下节点定位算法[J]. 电子学报, 2022, 50(7): 1567-1572.
[4]	姜奇, 文悦, 张瑞杰, 魏福山, 马建峰. 面向智能手机的自适应触屏持续认证方案[J]. 电子学报, 2022, 50(5): 1131-1139.
[5]	李玮, 张雨希, 谷大武, 张金煜, 朱晓铭, 刘春, 蔡天培, 李嘉耀. 轻量级密码MANTIS的唯密文故障分析[J]. 电子学报, 2022, 50(4): 967-976.
[6]	王彬, 张伟锋, 赵双祥, 樊昕昱. 微波光子传感技术研究进展综述[J]. 电子学报, 2022, 50(4): 769-781.
[7]	袁冠, 邴睿, 刘肖, 代伟, 张艳梅, 蔡卓. 基于时空图神经网络的手势识别[J]. 电子学报, 2022, 50(4): 921-931.
[8]	陈书仪, 刘亚丽, 林昌露, 李涛, 董永权. 面向物联网的轻量级可验证群组认证方案[J]. 电子学报, 2022, 50(4): 990-1001.
[9]	黄铁军. 脉冲连续摄影原理与超高速高动态成像验证[J]. 电子学报, 2022, 50(12): 2919-2927.
[10]	王义君, 陈忠野, 缪瑞新, 宋忠炎. 基于三角剖分的WSNs元胞感知覆盖算法[J]. 电子学报, 2022, 50(10): 2443-2451.
[11]	孟超, 周倩, 郭林, 王攀, 孙知信. 基于相关性传输模型的无线链路质量估计方法及路由优化算法[J]. 电子学报, 2022, 50(10): 2409-2424.
[12]	汪成亮, 赵凯, 刘嘉敏. 智能环境下基于边缘设备规则推理的数据预部署研究[J]. 电子学报, 2022, 50(10): 2347-2360.
[13]	赵文生, 方宇浩, 王大伟, 刘军. 微波谐振式传感器研究进展[J]. 电子学报, 2022, 50(10): 2530-2541.
[14]	田典, 余宁梅, 周广霖, 李娜. 双线阵无透镜超分辨率扫描成像方法研究[J]. 电子学报, 2022, 50(10): 2503-2516.
[15]	徐频捷, 陈逸杰, 李之南, 赵地. 基于事件驱动的车道线识别算法研究[J]. 电子学报, 2021, 49(7): 1379-1385.