CHEN Guoliang, YANG Zhou. Step Counting Algorithm Based on Zero Velocity Update[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 726-730, 788. DOI: 10.13203/j.whugis20150400
Citation: CHEN Guoliang, YANG Zhou. Step Counting Algorithm Based on Zero Velocity Update[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 726-730, 788. DOI: 10.13203/j.whugis20150400

Step Counting Algorithm Based on Zero Velocity Update

Funds: 

The National Natural Science Foundation of China 41371423

National Key Research and Development Program of China 2016YFB0502105

Natural Science Foundation of Jiangsu Province BK20161181

More Information
  • Author Bio:

    CHEN Guoliang, PhD, professor, specializes in the theories and methods of indoor position.chglcumt@163.com

  • Received Date: March 09, 2016
  • Published Date: June 04, 2017
  • A variety of indoor positioning technologies have emerged, such as ultrasound, infrared, wireless local area network, bluetooth, radio frequency identification, and ultra wideband. These systems require additional hardware devices and complicated deployment. Pedestrian Dead Reckoning can provide motion information at a high update rate and achieve high precision over a short time duration, However, without external aids, the system suffers from local anomalies and cumulative error after positioning for a longer length of time. Many researchers use a step length model combined with direction information to calculate the displacement of indoor pedestrians with accuracy step counting method. A step counting algorithm based on zero-velocity update was proposed. By analyzing the pedestrian's moving character and posture, acceleration amplitude measurement used to detection the movement stage of pedestrian was employed to calculate steps with Micro Electro Mechaical System (MEMS). The experimental results show that the scheme proposed in this paper was high precise and had good adaptability to different motion state, reporting accurately more than 98% overall, which is appropriate for complex indoor environment.
  • [1]
    Luoh Leh. ZigBee-based Intelligent Indoor Positioning System Soft Computing[J]. Soft Computing, 2014, 3(18): 443-456 http://www.researchgate.net/publication/257432738_ZigBee-based_intelligent_indoor_positioning_system_soft_computing
    [2]
    叶晨成, 校景中, 肖丽.基于RFID的井下人员定位系统[J].武汉理工大学学报, 2010(15):146-149 doi: 10.3963/j.issn.1671-4431.2010.15.035

    Ye Chencheng, Xiao Jingzhong, Xiao Li. Personnel Positioning System of Underground Mines Based on RFID[J]. Journal of Wuhan University of Technology, 2010(15):146-149 doi: 10.3963/j.issn.1671-4431.2010.15.035
    [3]
    周宝定, 李清泉, 毛庆洲, 等.用户行为感知辅助的室内行人定位[J].武汉大学学报·信息科学版, 2014, 39(6):719-723 http://ch.whu.edu.cn/CN/abstract/abstract3006.shtml

    Zhou Baoding, Li Qingquan, Mao Qingzhou, et al. User Activity Awareness Assisted Indoor Pedestrian Localization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6):719-723 http://ch.whu.edu.cn/CN/abstract/abstract3006.shtml
    [4]
    Jimenez A R, Seco F, et al. A Comparison of Pedestrian Dead-Reckoning Algorithms Using a Low-cost MEMS IMU[C]. IEEE International Symposium on Intelligent Signal Processing, Washington D C, USA, 2009
    [5]
    Ficco M, Palmieri F, Castiglione A. Hybrid Indoor and Outdoor Iocation Services for New Generation Mobile Terminals[J]. Personal and Ubiquitous Computing, 2014, 18(2):271-285 doi: 10.1007/s00779-013-0644-4
    [6]
    Lee S, Kim B, Kim H, et al. Inertial Sensor-based Indoor Pedestrian Localization with Minimum 802.15.4a Configuration[J]. IEEE Transactions on Industrial Informatics, 2011, 7(3): 455-466 doi: 10.1109/TII.2011.2158832
    [7]
    庞晗. 基于MEMS惯性器件的徒步个人导航仪设计与实现[D]. 哈尔滨: 哈尔滨工程大学, 2012

    Pang Han. Design and Realization of a Pedestrian Navigation Device with MEMS Inertial Sensors[D].Harbin: Harbin Engineering University, 2012
    [8]
    韩文正, 冯迪, 李鹏, 等.基于加速度传感器LIS3DH的计步器设计[J].传感器与微系统, 2012, 31(11):97-99 doi: 10.3969/j.issn.1000-9787.2012.11.030

    Han Wenzheng, Feng Di, Li Peng, et al. Design of Pedometer Sensor Based on Acceleration LIS3DH[J]. Transducer and Microsystem Technologies, 2012, 31(11):97-99 doi: 10.3969/j.issn.1000-9787.2012.11.030
    [9]
    Rai A, Chintalapudi K K, Padamanadhan V N, et al. Zee: Zero-effort Crowd Sourcing for Indoor Localization[C]. The 18th Annual International Conference on Mobile Computing and Networking, Singapore, 2012
    [10]
    杨辉. 基于MEMS传感器的高精度行人导航算法研究[D]. 厦门: 厦门大学, 2014

    Yang Hui. Research of High-accuracy Pedestrian Navigation Algorithm Based on MEMS Sensor[D]. Xiamen: Xiamen University, 2014
  • Related Articles

    [1]LIU Jiping, CAO Yuanhui, WANG Yong, REN Fu, DU Qingyun. Evaluating the Accessibility of Medical Services in the 15 min Life Circle Using Internet Pan-Map Resources: A Case Study in Shanghai[J]. Geomatics and Information Science of Wuhan University, 2022, 47(12): 2054-2063. DOI: 10.13203/j.whugis20220565
    [2]ZHAO Zhongguo, ZHANG Feng, ZHENG Jianghua. Evaluation of Landslide Susceptibility by Multiple Adaptive Regression Spline Method[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 442-450. DOI: 10.13203/j.whugis20190136
    [3]WANG Yafei, YUAN Hui, CHEN Biyu, LI Qingquan, WAN Meng, WANG Jiayao, GUO Jianzhong. Measuring Place-Based Accessibility Under Travel Time Uncertainty[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1723-1729. DOI: 10.13203/j.whugis20180015
    [4]LU Yonghua, LI Shuang. Spatial Accessibility of Indoor Emergency Shelters Based on Improved G2SFCA in Shenzhen City[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9): 1391-1398. DOI: 10.13203/j.whugis20180456
    [5]LIU Haiyan, PANG Xiaoping, WANG Yue, LI Zhongxiang. Quantitative Research for Site-selection of Antarctic Year-round Research Stations Based on Logistic Regression[J]. Geomatics and Information Science of Wuhan University, 2017, 42(3): 390-394. DOI: 10.13203/j.whugis20150260
    [6]FU Zhongliang, YANG Yuanwei, GAO Xianjun, ZHAO Xingyuan, LU Yuefeng, CHEN Shaoqin. Road Networks Matching Using Multiple Logistic Regression[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 171-177. DOI: 10.13203/j.whugis20150112
    [7]Lu Jun, Dai Wujiao, Zhang Zhetao. Modeling Dam Deformation Using Varying Coefficient Regression[J]. Geomatics and Information Science of Wuhan University, 2015, 40(1): 139-142.
    [8]NONG Yu, WANG Kun, DU Qingyun. Modeling Land Use Change Using Multinomial Logistic Regression[J]. Geomatics and Information Science of Wuhan University, 2011, 36(6): 743-746.
    [9]FANG Zhixiang, LI Qingquan, SHAW Shihlung. Representation of Location-Specific Space-Time Accessibility Based on Time Geography Framework[J]. Geomatics and Information Science of Wuhan University, 2010, 35(9): 1091-1095.
    [10]Wu Zian. The Independent Variablas-snooping in the Regression Analysis of Dam Deformation[J]. Geomatics and Information Science of Wuhan University, 1993, 18(1): 20-26.
  • Cited by

    Periodical cited type(28)

    1. 董威威,蒋贵国,梁小雅,钟孟君,程珍旎. 基于改进引力模型的成渝地区双城经济圈城市经济联系网络演变研究. 四川师范大学学报(自然科学版). 2025(01): 82-93 .
    2. 詹庆明,李轩,樊智宇. 利用非负矩阵分解识别县域人群活动时空特征. 测绘地理信息. 2024(02): 108-113 .
    3. 李静波,关雪峰,曾星,杨昌兰,邢巍然,吴华意. 长株潭城市群建成区时空扩展特征及驱动力分析. 武汉大学学报(信息科学版). 2024(06): 1028-1039 .
    4. 何明卫,赵庆文,李健波. 组团城市通勤流网络特征及影响因素分析——以贵阳市为例. 昆明理工大学学报(自然科学版). 2024(04): 249-260 .
    5. 胡秋实,李锐,吴华意,刘朝辉,蔡晶. 顾及城市场景变化的人口分析单元表达. 武汉大学学报(信息科学版). 2024(10): 1788-1799 .
    6. 薛山,廖一兰,李春林,胡艺. 不同人口流动模式下城市传染病时空传播模型适用性研究. 地球信息科学学报. 2023(01): 208-222 .
    7. 张伟丽,郝智娟,王伊斌,魏瑞博. 城市群人口流动空间网络及影响因素. 地理科学. 2023(01): 72-81 .
    8. 张伟丽,郝智娟,王伊斌,魏瑞博. 城市群人口流动空间网络及影响因素. 地理科学. 2023(02): 72-81 .
    9. 郭联欢,洪昕晨. 城市社会感知研究与应用进展. 中外建筑. 2023(06): 12-17 .
    10. 严雪心,周婕,盛富斌,牛强. 大城市近郊区产业类型对就业人口流动的差异化影响——以武汉市为例. 经济地理. 2023(10): 63-74 .
    11. 真诗泳,林钦贤,张露丹,李嘉政,林玉英,陈诚,潘自宝,胡喜生. 基于多源数据的城市内部空间交互特征:以福州市主城区为例. 科学技术与工程. 2023(35): 14937-14946 .
    12. 赵凯旭,张帅兵,黄晓军,李恩龙,武风奇. 新冠疫情管控期间西安市人口分布演变及影响因素探测——基于多源时空大数据视角. 人口与发展. 2022(01): 140-150 .
    13. 甄峰,李哲睿,谢智敏. 基于人口流动的城市内部空间结构特征及其影响因素分析——以南京市为例. 地理研究. 2022(06): 1525-1539 .
    14. 龚丽丽,蔡忠亮,李伯钊,牛彦芬,苏世亮. 街道尺度下城市居民出行特征分析. 测绘地理信息. 2022(S1): 69-73 .
    15. 段艳慧,郭伟,赵学胜,张晓莹,张冰瑞. 基于Landsat影像和统计数据的北京市人口密度制图. 北京测绘. 2022(08): 1096-1101 .
    16. 赵虎,尚铭宇,张悦,张浩楠,李鹏乾. 区域中心城市就业空间格局及优化策略研究——以山东省青岛市为例. 山东建筑大学学报. 2022(06): 61-69 .
    17. 孙艳玲,李俊蓉,张夏坤,裴新蕊,刘子菲,郭鹏. 基于昼夜人口变化的应急避难场所可达性评价——以天津市西青区为例. 安全与环境学报. 2022(06): 3342-3349 .
    18. 张爱民. 基于大数据的江西省就业收入和社会保障研究. 科技广场. 2022(05): 71-78 .
    19. 何建华,覃荣诺,丁愫,李江,岳桥兵. 基于乡村宜居性和人口流动网络特征的农村居民点重构. 武汉大学学报(信息科学版). 2021(03): 402-409 .
    20. 罗桑扎西,甄峰,张姗琪. 复杂网络视角下的城市人流空间概念模型与研究框架. 地理研究. 2021(04): 1195-1208 .
    21. 季航宇,蔡忠亮,姜莉莉,李桂娥,李伯钊. 出租车出行的空间不平等及其与人口结构的关联. 武汉大学学报(信息科学版). 2021(05): 766-776 .
    22. 刘正廉,桂志鹏,吴华意,秦昆,吴京航,梅宇翱,赵晶. 融合建筑物与POI数据的精细人口空间化研究. 测绘地理信息. 2021(05): 102-106 .
    23. 付诗航,刘耀林,方莹,杨孝军,刘艳芳,彭明军. 基于SCD的公共交通换乘时空模式——以武汉市为例. 武汉大学学报(信息科学版). 2020(07): 1089-1098 .
    24. 甘田,刘鼎. 基于手机数据的职住空间关系研究——以重庆市主城区为例. 城市交通. 2020(05): 36-44+119 .
    25. 刘艳芳,方飞国,刘耀林,罗名海. 时空大数据在空间优化中的应用. 测绘地理信息. 2019(03): 7-20 .
    26. 杨朗,周丽娜,张晓明. 基于手机信令数据的广州市职住空间特征及其发展模式探究. 城市观察. 2019(03): 87-96 .
    27. 花磊,彭宏杰,杨秀锋,刘秀芸,袁绪英,吴宜进. 基于腾讯位置大数据的长江经济带人口流动空间分析. 华中师范大学学报(自然科学版). 2019(05): 815-820 .
    28. 宋小冬,杨钰颖,钮心毅. 上海典型产业园区职工居住地、通勤距离的变化及影响机制. 城市发展研究. 2019(12): 53-61 .

    Other cited types(17)

Catalog

    Article views (2487) PDF downloads (789) Cited by(45)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return