基于力学量信息获取系统的人体摔倒过程识别方法研究
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摘要
当今社会已进入老龄化社会,老年人摔倒发生率高、后果严重,已经成为一个严重的医疗问题和社会问题。通过科学的手段有效预测及检测老年人摔倒,从而减小老年人摔倒带来的伤害问题已成为国内外新的研究热点,具有较高的研究价值及应用意义。然而,国内外目前对老年人摔倒行为过程的研究尚无针对性的模型,对于不同摔倒过程与相似运动状态识别的误判问题比较突出,尤其是对摔倒冲击状态前的预测问题研究尚起步不久,还有许多关键问题尚未解决。
本文研究致力于通过力学量信息获取的相关理论探索解决人体摔倒检测及预测问题,从而进一步提高其检测准确性及预测时效性。论文的主要工作及贡献总结如下:
(1)通过分析人体摔倒行为过程的运动生物力学特性,明确了人体上躯干部位作为摔倒过程识别研究参数提取的特征部位,并据此选择了摔倒过程中上躯干加速度变化及偏离竖直方向的倾斜角度变化作为检测及预测人体摔倒的特征信息量。
(2)采用支持向量机方法对人体摔倒运动过程中及其它日常生活行为过程中的上躯干合加速度及偏离竖直方向的倾斜角度分类,使用最优分类界面上的合加速度及倾角值作为人体摔倒检测方法的阈值信息。分析合加速度阈值、倾角阈值与摔倒过程的时间关系,提出并实现了基于三轴加速度和两轴角速度传感器的人体摔倒检测算法,及基于三轴加速度传感器的人体摔倒检测算法。
(3)提出了一种基于人体运动过程中运动状态序列时序分析法的人体摔倒检测及预测方法。首先融合人体运动过程中特征部位的加速度信息为表征该运动过程特点的加速度时间序列。其次,将人体运动过程作为随机过程研究,研究人体摔倒行为过程中各运动状态之间的转移规律,以及各运动状态下加速度时间序列的出现规律。由于人体摔倒预测研究需要在人体与低势物体碰撞前做出决策,因此建立描述人体摔倒过程中与低势物体碰撞前的过程段的隐马尔可夫模型(HMM),则待识别运动过程在当前时刻前某时间段内的加速度时间序列在模型上的输出概率反映了该时间段内人体运动过程与模型描述的运动过程段的匹配程度,可以评估当前时刻人体与低势物体碰撞的风险,从而实现人体摔倒检测及预测。最后,通过支持向量机对不同运动过程加速度时间序列在模型上的输出概率划分阈值,设计并实现人体摔倒检测及预测算法。
(4)搭建了人体摔倒识别传感信息检测实验平台,进行了人体摔倒检测及预测方法的实验研究工作。在实验样本范围内,本文提出的基于人体运动状态序列时序分析法的人体摔倒过程识别方法可以有效的区分人体摔倒行为及其它日常生活行为,在摔倒检测方面取得了100%的敏感性(Sensitivity)和100%的特异性(Specificity),摔倒预测区间为人体与低势物体碰撞前的200～300ms。以上研究工作成果,在前人研究的基础上进一步丰富了关于人体摔倒检测及预测研究方面的知识。
Fall accident of elders is always a serious problem in social community. As the world aging process quickened, it has became a significant financial burden to modern society. Hence, reliable fall prediction and detection method is essential to reduce fall related injury in independent living facilities, and it has become a hot investigation world wide. However, there is not special model for human fall process research nowadays, so as to lead the misdetection of falls and similar motions, especially for fall prediction methods. Thus, there are still many key issues unsolved.
This paper is committed to explore human fall detection and prediction method through the theories of mechanical information acquisition, so as to improve the detection accuracy and prediction real-time property. Its main works and contributions are summarized as follows:
(1) Through analyzing the sports biomechanics features of human fall process, human upper trunk is defined as the feature region to extract information for fall recognition. And accordingly, variable acceleration and the tilt angle deviate from the vertical orientation of human upper trunk are chosen as the features for fall detection and prediction study.
(2) Classify the resultant accelerations and tilt angles deviate from the vertical orientation of human upper trunk from fall processes and other daily life activities using Support Vector Machine (SVM), and take the acceleration and angle value on the optimal classification boundary as the thresholds to detect fall evens. Through analyzing the relationship between acceleration threshold, angle threshold and time, two human fall detection methods are proposed and realized: one method is based on tri-axial accelerometer and bi-axial gyroscope, the other one is based on tri-axial accelerometer.
(3) To detect and predict fall evens, a human motion states timing analysis based human fall recognition method is proposed. First, extract the acceleration time series (ATS) through human motion process to characterize the process. Then, take human motion movement as random process, to study the transition probabilities between each motion state in human fall process, and the appearance probabilities between motion states and ATS. Because fall prediction result must be decided before the collision during falls, this paper build the Hidden Markov Model (HMM) to describe human motion particularly during falling courses but before the collision between human body and lower objects. Hence, the output probability of ATS, which is from a period time before current time during a motion process, on the HMM represents the marching degree between that motion process and HMM, it can evaluate the risk to fall down at current time, so as to realize the fall detection and prediction method. At last, the author got the output probability thresholds between fall process and other daily life activities through SVM, and designed human fall detection and prediction algorithm.
(4) To practice experiments of human fall detection and prediction algorithms, an experimental platform with information acquisition system for human fall recognition is build. In the limit of experimental samples, the human motion states timing analysis based human fall recognition method in this paper can distinguish fall evens and other daily life activities effectively: 100% sensitivity and 0% specificity (no misdetection) is got in detection experiment, and the prediction time interval is 200~300ms before the collision of human body with lower objects. The results of this study above enriched the knowledge of human fall detection and prediction study based on the previous research.
本文研究致力于通过力学量信息获取的相关理论探索解决人体摔倒检测及预测问题,从而进一步提高其检测准确性及预测时效性。论文的主要工作及贡献总结如下:
(1)通过分析人体摔倒行为过程的运动生物力学特性,明确了人体上躯干部位作为摔倒过程识别研究参数提取的特征部位,并据此选择了摔倒过程中上躯干加速度变化及偏离竖直方向的倾斜角度变化作为检测及预测人体摔倒的特征信息量。
(2)采用支持向量机方法对人体摔倒运动过程中及其它日常生活行为过程中的上躯干合加速度及偏离竖直方向的倾斜角度分类,使用最优分类界面上的合加速度及倾角值作为人体摔倒检测方法的阈值信息。分析合加速度阈值、倾角阈值与摔倒过程的时间关系,提出并实现了基于三轴加速度和两轴角速度传感器的人体摔倒检测算法,及基于三轴加速度传感器的人体摔倒检测算法。
(3)提出了一种基于人体运动过程中运动状态序列时序分析法的人体摔倒检测及预测方法。首先融合人体运动过程中特征部位的加速度信息为表征该运动过程特点的加速度时间序列。其次,将人体运动过程作为随机过程研究,研究人体摔倒行为过程中各运动状态之间的转移规律,以及各运动状态下加速度时间序列的出现规律。由于人体摔倒预测研究需要在人体与低势物体碰撞前做出决策,因此建立描述人体摔倒过程中与低势物体碰撞前的过程段的隐马尔可夫模型(HMM),则待识别运动过程在当前时刻前某时间段内的加速度时间序列在模型上的输出概率反映了该时间段内人体运动过程与模型描述的运动过程段的匹配程度,可以评估当前时刻人体与低势物体碰撞的风险,从而实现人体摔倒检测及预测。最后,通过支持向量机对不同运动过程加速度时间序列在模型上的输出概率划分阈值,设计并实现人体摔倒检测及预测算法。
(4)搭建了人体摔倒识别传感信息检测实验平台,进行了人体摔倒检测及预测方法的实验研究工作。在实验样本范围内,本文提出的基于人体运动状态序列时序分析法的人体摔倒过程识别方法可以有效的区分人体摔倒行为及其它日常生活行为,在摔倒检测方面取得了100%的敏感性(Sensitivity)和100%的特异性(Specificity),摔倒预测区间为人体与低势物体碰撞前的200～300ms。以上研究工作成果,在前人研究的基础上进一步丰富了关于人体摔倒检测及预测研究方面的知识。
Fall accident of elders is always a serious problem in social community. As the world aging process quickened, it has became a significant financial burden to modern society. Hence, reliable fall prediction and detection method is essential to reduce fall related injury in independent living facilities, and it has become a hot investigation world wide. However, there is not special model for human fall process research nowadays, so as to lead the misdetection of falls and similar motions, especially for fall prediction methods. Thus, there are still many key issues unsolved.
This paper is committed to explore human fall detection and prediction method through the theories of mechanical information acquisition, so as to improve the detection accuracy and prediction real-time property. Its main works and contributions are summarized as follows:
(1) Through analyzing the sports biomechanics features of human fall process, human upper trunk is defined as the feature region to extract information for fall recognition. And accordingly, variable acceleration and the tilt angle deviate from the vertical orientation of human upper trunk are chosen as the features for fall detection and prediction study.
(2) Classify the resultant accelerations and tilt angles deviate from the vertical orientation of human upper trunk from fall processes and other daily life activities using Support Vector Machine (SVM), and take the acceleration and angle value on the optimal classification boundary as the thresholds to detect fall evens. Through analyzing the relationship between acceleration threshold, angle threshold and time, two human fall detection methods are proposed and realized: one method is based on tri-axial accelerometer and bi-axial gyroscope, the other one is based on tri-axial accelerometer.
(3) To detect and predict fall evens, a human motion states timing analysis based human fall recognition method is proposed. First, extract the acceleration time series (ATS) through human motion process to characterize the process. Then, take human motion movement as random process, to study the transition probabilities between each motion state in human fall process, and the appearance probabilities between motion states and ATS. Because fall prediction result must be decided before the collision during falls, this paper build the Hidden Markov Model (HMM) to describe human motion particularly during falling courses but before the collision between human body and lower objects. Hence, the output probability of ATS, which is from a period time before current time during a motion process, on the HMM represents the marching degree between that motion process and HMM, it can evaluate the risk to fall down at current time, so as to realize the fall detection and prediction method. At last, the author got the output probability thresholds between fall process and other daily life activities through SVM, and designed human fall detection and prediction algorithm.
(4) To practice experiments of human fall detection and prediction algorithms, an experimental platform with information acquisition system for human fall recognition is build. In the limit of experimental samples, the human motion states timing analysis based human fall recognition method in this paper can distinguish fall evens and other daily life activities effectively: 100% sensitivity and 0% specificity (no misdetection) is got in detection experiment, and the prediction time interval is 200~300ms before the collision of human body with lower objects. The results of this study above enriched the knowledge of human fall detection and prediction study based on the previous research.
引文
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Reece AC, Simpson JM. 1996. Preparing older people to cope after a fall [J]. Physiotherapy. 82(4): 227–35.
Hammadi Nait-Charif and Stephen J. McKenna. 2004. Activity Summarisation and Fall Detection in a Supportive Home Environment [C]. Proceedings of the 17th International Conference on Pattern Recognition 2004(ICPR’04): 323-326
Popescu, Mihail Li, Yun Skubic, Marjorie Rantz, Marilyn. 2008. An acoustic fall detector system that uses sound height information to reduce the false alarm rate [C]. Engineering in Medicine and Biology Society, 2008. 30th Annual International Conference of the IEEE. EMBS 2008. pp4628– 4631
Alwan M., Dalal S., Kell S., Felder R. 2003. Derivation of basic human gait characteristics from floor vibrations [C]. 2003. Summer Bioengineering Conference. June 25–29, Key BiscayneFL.
Majd Alwan , Prabhu Jude Rajendran , Steve Kell , David Mack , Siddharth Dalal , Matt Wolfe , Robin Felder. 2006. A Passive Floor-Vibration Based Fall Detector [C]. 2006 International Conference on Aging, Disability and Independence, Feb 1–4, St.Petersburg FL. 1003-1007
Alwan M, Prabhu Jude Rajendran, Steve Kell, David Mack, Siddharth Dalal, Matt Wolfe, Robin Felder. 2006. A Smart and Passive Floor-Vibration Based Fall Detector for Elderly [C], 2nd IEEE International Conference on Information and Communication Technologies, 2006.
Hwang, J.M, Kang J.Y, Jang Y.W, Kim H. C. 2004. Development of Novel Algorithm and Real-time Monitoring Ambulatory System Using Bluetooth Module for Fall Detection in the Elderly [C]. Proceedings of the 26th Annual International Conference of the IEEE EMBS, 2004. pp. 2204-2208
Jay Chen, Karric Kwong, Dennis Chang, Jerry Luk, Ruzena Bajcsy. 2005. Wearable Sensors for Reliable Fall Detection [C]. Proceedings of the 2005 IEEE. Engineering in Medicine and Biology 27th Annual Conference, pp. 3551-3554
Toshiyo Tamura, Takumi Yoshimura, Masaki Sekine. 2007 A preliminary study to demonstrate the use of an air bag device to prevent fall-related injuries [C]. Proceedings of the 29th Annual International Conference of the IEEE EMBS, 2007. pp. 3833-3836
Singh S.R. 2008. A computational method of forecasting based on fuzzy time series [J]. Mathematics and Computers in Simulation. 79: 539–554
Erdal Kayacan, Baris Ulutas, Okyay Kaynak. 2010. Grey system theory-based models in time series prediction [J]. Expert Systems with Applications. 37: 1784–1789
Yan-Ping Huang, Chung-Chian Hsu, Sheng-Hsuan Wang. 2007. Pattern recognition in time series database: A case study on financial database [J]. Expert Systems with Applications. 33: 199–205
Venkatesh Rajagopalan, Asok Ray, Rohan Samsi, Jeffrey Mayer. 2007. Pattern identification in dynamical systems via symbolic time series analysis [J]. Pattern Recognition. 40: 2897– 2907
Gonzalez-Abril L., Velasco F., Ortega J.A., Cuberos F.J.. 2009. A new approach to qualitative learning in time series [J]. Expert Systems with Applications. 36: 9924–9927
Jiangjiao Duan, Wei Wang, Jianping Zeng, Dongzhan Zhang, Baile Shi. 2009. A prediction algorithm for time series based on adaptive model selection [J]. Expert Systems with Applications. 36: 1308–1314
Levinson S.E., Rabiner L.R., and Sondhi M. M.. 1983. An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition [J]. TheBell System Technical Journal 62 (1983): 1035-1074.
Rabiner, L.R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition [C]. Proceeding of IEEE [C]. 77(2): 257-286
David Gillman, Michael Sipser. 1994. Inference and minimization of hidden Markov chains. Proceedings of the seventh annual conference on Computational learning theory. 1994: 147-158
Katsuyuki Hagiwara, Kenji Fukumizu. 2008. Relation between weight size and degree of over-fitting in neural network regression [J]. Neural Networks, 21(1): 48-58
Francesco Gullo,GiovanniPonti,AndreaTagarelli, SergioGreco.2009. A time series representation model for accurate and fast similarity detection [J]. Pattern Recognition. (42): 2998-3014
Simon Günter, Horst Bunke. 2004. HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components [J]. Pattern Recognition. (37): 2069–2079
Randy Gomez, Tomoki Toda, Hiroshi Saruwatari, Kiyohiro Shikano. 2009. Techniques in rapid unsupervised speaker adaptation based on HMM-Sufficient Statistics [J]. Speech Communication. (51): 42-57
Ming-Hsu Cheng, Meng-Fen Hoa, Chung-Lin Huanga. 2008. Gait analysis for human identification through manifold learning and HMM [J]. Pattern Recognition. (41): 2541-2553
Jose' Gordillo, Eduardo Conde. 2007. An HMM for detecting spam mail [J]. Expert Systems with Applications. 33: 667–682
Julia'n D.Arias-London?o, JuanI.Godino-Llorente, Nicola′s Sa′enz-Lecho′n, V?′ctor Osma-Ruiz,Germa′n Castellanos-Dom?′nguez. 2010. An improved method for voice pathology detection by means of a HMM-based feature space transformation [J]. Pattern Recognition. 43: 3100–3112
Caroni C., Karioti V.. 2004. Detecting an innovative outlierin a set of time series [J]. Computational Statistics & Data Analysis. 46: 561– 570
Centers for Disease Control and Prevention. Injury Statistics Query and Reporting System (WISQARS) [DB/OL]. (2007): http://www.cdc.gov/injury/wisqars/index.html
Web site for Tunstall Group Ltd (2008): www.tunstall.co.uk
Web site for Xsens lnc. http://www.xsens.com/en/general/mti
中国康复医学会论坛2007:http://www.carm.org.cn/BBS/viewthread.php?tid=17625&page=1 &frameon=no
日本资讯网(2008): www.news.kantsuu.com/200810/20081001144247_125554.shtml