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基于卡尔曼滤波与k-NN算法的可穿戴跌倒检测技术研究

何坚 周明我 王晓懿

何坚, 周明我, 王晓懿. 基于卡尔曼滤波与k-NN算法的可穿戴跌倒检测技术研究[J]. 电子与信息学报, 2017, 39(11): 2627-2634. doi: 10.11999/JEIT170173
引用本文: 何坚, 周明我, 王晓懿. 基于卡尔曼滤波与k-NN算法的可穿戴跌倒检测技术研究[J]. 电子与信息学报, 2017, 39(11): 2627-2634. doi: 10.11999/JEIT170173
HE Jian, ZHOU Mingwo, WANG Xiaoyi. Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm[J]. Journal of Electronics and Information Technology, 2017, 39(11): 2627-2634. doi: 10.11999/JEIT170173
Citation: HE Jian, ZHOU Mingwo, WANG Xiaoyi. Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm[J]. Journal of Electronics and Information Technology, 2017, 39(11): 2627-2634. doi: 10.11999/JEIT170173

基于卡尔曼滤波与k-NN算法的可穿戴跌倒检测技术研究

doi: 10.11999/JEIT170173
基金项目: 

国家自然科学基金(61602016)

Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm

Funds: 

The National Natural Science Foundation of China (61602016)

  • 摘要: 针对老年人跌倒检测的准确性和实时性需求,该文首先建立了基于姿态角的活动描述模型,研发了集成加速度传感器、陀螺仪和蓝牙的活动感知模块,从而实时采集运动变化数据并使用蓝牙发送到智能手机。其次,选取姿态角及加速度信号向量模作为特征量,通过卡尔曼滤波对数据进行去噪与融合,并应用滑动窗口和k-NN算法实现了可实时感知老年人跌倒并报警的系统。实验证明系统在二分类场景下的跌倒检测准确率为98.9%,而敏感度和特异性分别达到98.9%和98.5%,验证了系统具有良好的实时性和较高的准确率。
  • 田雪原. 人口老龄化与养老保险体制创新[J]. 人口学刊, 2014, 36(1): 5-15. doi: 10.3969/j.issn.1004-129X.2014.01.001.
    TIAN Xueyuan. Population aging and endowment insurance system innovation[J]. Population Journal, 2014, 36(1): 5-15. doi: 10.3969/j.issn.1004-129X.2014.01.001.
    唐雨欣, 郭小牧, 谯治蛟, 等. 北京, 上海社区老年人跌倒现况及影响因素研究[J]. 中华疾病控制杂志, 2017, 21(1): 72-76. doi: 10.16462/j.cnki.zhjbkz.2017.01.017.
    TANG Yuxin, GUO Xiaomu, QIAO Zhijiao, et al. Analysis on prevalence and risk factors for falls among the elderly in communities of Beijing and Shanghai[J]. Chinese Journal of Disease Control Prevention, 2017, 21(1): 72-76. doi 10. 16462/j.cnki.zhjbkz. 2017.01.017.
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    BALDEWIJNS Greet, DEBARD Glen, MERTES Gert, et al. Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms[J]. Healthcare Technology Letters, 2016, 3(1): 6-11. doi: 10.1049/htl.2015. 0047
    MAZUREK Pawel and MORAWSKI Roman Z. Application of nave Bayes classifier in fall detection systems based on infrared depth sensors[C]. Proceedings of the IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing System-Technology and Applications (IDAACS), Warsaw, Poland, 2015: 717-722. doi: 10.1109/ IDAACS.2015.7341397.
    SALMAN KHAN Muhammad, YU Miao, FENG Pengming, et al. An unsupervised acoustic fall detection system using source separation for sound interference suppression[J]. Signal Processing, 2015, 110(C): 199-210. doi: 10.1016/j. sigpro.2014.08.021.
    BECKER C, SCHWICKERT L, MELLONE S, et al. Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors[J]. Zeitschrift Fr Gerontologie Und Geriatrie, 2012, 45(8): 707-715. doi: 10.1007/s00391-012-0403-6.
    WANG Jin, ZHANG Zhongqi, LI Bin, et al. An enhanced fall detection system for elderly person monitoring using consumer home networks[J]. IEEE Transactions on Consumer Electronics, 2014, 60(1): 23-29. doi: 10.1109/ TCE.2014.6780921.
    QU Weihao, LIN Feng, WANG Aosen, et al. Evaluation of a low-complexity fall detection algorithm on wearable sensor towards falls and fall-alike activities[C]. Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium. Philadelphia, PA, United States, 2015: 1-6. doi: 10.1109/ SPMB.2015.7405427.
    QU Weihao, LIN Feng, and XU Wenyao. A real-time low-complexity fall detection system on the smartphone[C]. Proceedings of the IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies. Washington, DC, United States, 2016: 354-356. doi: 10.1109/CHASE.2016.73.
    SALGADO Paulo and AFONSO Paulo. Body fall detection with Kalman filter and SVM[C]. Proceedings of the 11th Portuguese Conference on Automatic Control, Porto, Portugal, 2015, 321 LNEE: 407-416. doi: 10.1007/978-3-319- 10380-8_39.
    BOURKE A K and LYONS G M. A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor[J]. Medical Engineering Physics, 2008, 30(1): 84-90. doi: 10.1016/j.medengphy.2006.12.001.
    陈航科, 张东升, 盛晓超, 等. 基于Kalman滤波算法的姿态传感器信号融合技术研究[J]. 传感器与微系统, 2013, 32(12): 82-85.
    CHEN Hangke, ZHANG Dongsheng, SHENG Xiaochao, et al. Research on signal fusion technology of attitude sensor based on Kalman filtering algorithm[J]. Transducer and Microsystem Technologies, 2013, 32(12): 82-85. doi: 10.3969/ j.issn.1000-9787. 2013.12.023.
    LI Qiang, STANKOVIC John A, HANSON Mark A, et al. Accurate, fast fall detection using gyroscopes and accelerometer derived posture information[C]. Proceedings of the Sixth International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, United States, 2009: 138-143. doi: 10.1109/BSN.2009.46.
    HE Jian and HU Chen. A portable fall detection and alerting system based on k-NN algorithm and remote medicine[J]. China Communications, 2015, 12(4): 23-31. doi: 10.1109/CC. 2015.7114066.
    ERDOGAN Senol Zafer and BILGIN Turgay Tugay. A data mining approach for fall detection by using k-Nearest Neighbour algorithm on wireless sensor network data[J]. IET Communications, 2012, 6(18): 3281-3287. doi: 10.1049/ iet-com.2011.0228.
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出版历程
  • 收稿日期:  2017-02-20
  • 修回日期:  2017-08-10
  • 刊出日期:  2017-11-19

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