基於深度學習之跌倒偵測系統

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姓名

張靜涵(Ching-Han Chang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於深度學習之跌倒偵測系統
(A Fall Detection System Based on the Deep Learning)

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摘要(中)

由於社會人口結構不斷老化，跌倒成為老年人慢性致殘的第三大原因，因此照護產業日漸受到重視，跌倒偵測的技術研究也不斷向前邁進。前人研究中分別使用了感應偵測器與影像偵測法，來做跌倒偵測，直到2006年出現使用機器學習法來做跌倒偵測研究。
近年機器學習重新受到重視，主要來自於深度學習神經網路的貢獻，深度神經網路由人工類神經網路發展而來，其神經網路結構更加複雜多變，足以解決許多電腦視覺上的難題，因此本論文提出基於深度學習方式的跌倒偵測系統。核心技術，使用光流影像處理與3D卷積神經網路擷取跌倒影像特徵，再利用訓練完成的LSTM遞迴神經網路做跌倒預測分類。
為了加強本系統照護功能，這裡設計跌倒偵測系統具有後端通報功能，搭建以Infrastructure as a Service(IaaS)為基礎的雲端服務，雲端服務可同時照護多處住家，當前端偵測到跌倒事件，即可通報雲端服務，並協助醫護人員快速處理。
本論文參考前人研究，使用3D CNN與LSTM設計深度神經網路，並使用光流前處理進行改良，經實驗後Accuracy由68.47%提升至93.18%；另設計不同分類器實驗 (SVM、MLP)與本跌倒偵測系統進行比較，實驗結果都證實，本論文提出的方法擁有較佳的表現水準。

摘要(英)

Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. In previous studies, sensor detectors and image detection methods were used in fall detection research, until 2006, some research used machine learning to do fall detection.
In recent years, machine learning has been paid more attention, mainly from the contribution of deep learning. Deep learning is developed by artificial neural network, and its structure is more complex and varied, enough to solve many computer vision problems. This thesis proposes a fall detection system based on the deep learning. The core technology utilizes the optical image processing and 3D convolution neural network to extract features of images, and then to do fall prediction by training LSTM recurrent neural network.
In order to strengthen the system function of health-care, the front-end fall detection system has a back-end notification function design. To build a cloud service based on Infrastructure as a Service (IaaS), and the service can take care of multiple homes at the same time. When the front-end detects a fall event, it can notify the cloud service and help the healthcare provider handle it quickly.
Based on previous studies, this thesis uses 3D CNN and LSTM to design the deep neural network, and adds the optical flow pretreatment to improve. After the experiment, the accuracy increased from 68.47% to 93.18%. Here also designs different classifiers (SVM, MLP) experiments and compare with the proposed fall detection system. The experimental results confirm that the proposed method has a better performance level.

關鍵字(中)

★ 跌倒偵測
★ 類神經網路
★ 機器學習
★ 深度學習
★ 3D 卷積神經網路
★ 遞迴神經網路

關鍵字(英)

★ fall detection system
★ artificial neural network
★ machine learning
★ deep learning
★ 3D convolution neural network
★ recurrent neural network

論文目次

目錄
摘要 ................................................................................................................ I
ABSTRACT ........................................................................................................ II
致謝 ................................................................................................................ IV
目錄 .................................................................................................................. V
圖目錄 ...............................................................................................................VII
表目錄 ............................................................................................................... X
第一章、緒論 ...............................................................................................1
1-1 研究動機 ........................................................................................... 1
1-2 研究目的 ........................................................................................... 2
1-3 論文架構 ........................................................................................... 3
第二章、相關研究 ................................................................................. 4
2-1 穿戴式感測器偵測 ........................................................................... 5
2-2 周邊感測器偵測 ............................................................................... 5
2-3 影像式偵測 ....................................................................................... 6
2-4 機器學習 ........................................................................................... 9
第三章、深度學習 ................................................................................. 16
3-1 類神經網路 ........................................................................................18
3-1-1 多層感知機 ................................................................................... 20
3-1-2 倒傳遞演算法 .............................................................................. 21
3-2 深度學習 ......................................................................................... 25
3-2-1 卷積神經網絡 ............................................................................. 26
3-2-2 遞迴神經網路 ............................................................................ 29
第四章、跌倒偵測系統 ..................................................................... 33
4-1 系統說明 ........................................................................................ 33
4-1-1 硬體 ............................................................................................. 34
4-1-2 軟體 ............................................................................................. 35
4-1-3 偵測運算流程 ............................................................................. 36
4-2 系統模組 ......................................................................................... 38
4-2-1 光流模組 ..................................................................................... 39
4-2-2 3D CNN 模組 .............................................................................. 41
4-2-3 LSTM 預測模組 .......................................................................... 43
第五章、實驗設計與結果 .................................................................. 48
5-1 資料集說明 ..................................................................................... 48
5-2 訓練 ................................................................................................. 51
5-2-1 使用平台說明 .............................................................................. 51
5-2-2 操作介面 ...................................................................................... 53
5-3 實驗設計 ......................................................................................... 55
5-4 實驗結果 ......................................................................................... 61
5-4-1 控制組實驗結果 .......................................................................... 61
5-4-2 Baseline 實驗結果 ....................................................................... 64
5-4-3 CNN 實驗組實驗結果 ................................................................. 67
5-4-4 MLP 實驗組實驗結果 ................................................................. 70
5-4-5 SVM 實驗組實驗結果 ................................................................. 71
5-4-6 實驗綜合比較 .............................................................................. 73
第六章、結論與未來展望 .................................................................... 77
參考文獻 .................................................................................................... 78

參考文獻

參考文獻
[1] D. Silver,A. Huang,C. J. Maddison,A. Guez,L. Sifre,G. van den Driessche,J.
Schrittwieser,I. Antonoglou,V. Panneershelvam, M. Lanctot, S. Dieleman,D.
Grewe,J. Nham,N. Kalchbrenner,I. Sutskever,T. Lillicrap,M. Leach,K.
Kavukcuoglu,T. Graepel& D.Hassabis, “Mastering the game of Go with
deep neural networks and tree search”, Nature ,vol. 529, pp. 484–489 , January 2016
[2] Muhammad Mubashir, Ling Shao,Luke Seed, “A survey on fall detection:
Principles and approaches”, Neurocomputing , vol. 100, pp.144–152,2013
[3] T. Tamura, T. Yoshimura, M. Sekine, M. Uchida, O. Tanaka, “A wearable
airbag to prevent fall injuries”, IEEE Trans. Inf. Technol. Biomed, vol. 13,
pp.910–914, 2009
[4] H. Ghasemzadeh, R. Jafari, B. Prabhakaran, “A body sensor network with
electromyogram and inertial sensors: multimodal interpretation of muscular
activities”, IEEE Trans. Inf. Technol. Biomed. ,Vol.14, pp. 198–206, 2010
[5] T. Zhang, J. Wang, L. Xu, P. Liu, “Using Wearable Sensor and NMF Algorithm to Realize Ambulatory Fall Detection”, Springer-Verlag, Vol. 4222, pp. 488–491, 2006
[6] M. Kangas, I. Vikman, J. Wiklander, P. Lindgren, L. Nyberg, T. Jamsa, “Sensitivity and speciﬁcity of fall detection in people aged 40 years and over”, Gait Posture,Vol.29, pp.571–574, 2009
[7] A.M. Tabar, A. Keshavarz, H. Aghajan, “Smart Home Care Network Using
Sensor Fusion and Distributed Vision-Based Reasoning”, 4th ACM
International Workshop on Video Surveillance and Sensor Networks, 2006.
[8] M. Alwan, P.J. Rajendran, S. Kell, D. Mack, S. Dalal, M. Wolfe, R. Felder,
“A Smart and Passive Floor-Vibration Based Fall Detector for Elderly”, IEEE International Conference on Information & Communication
Technologies(ICITA), pp.1003–1007, Berkeley, USA 2006
[9] H. Foroughi, A. Naseri, A. Saberi, H.S. Yazdi, “An Eigenspace-Based Approach for Human Fall Detection Using Integrated Time Motion Image and Neural Network”, 9th IEEE International Conference on Signal Processing (ICSP), pp. 1499–1503, Las Vegas, USA 2008.
[10] H. Foroughi, B.S. Aski, H. Pourreza, “Intelligent Video Surveillance for Monitoring Fall Detection of Elderly in Home Environments”, 11th IEEE
International Conference on Computer and Information Technology (ICCIT),
pp. 219–224, Khulna, Bangladesh, 2008.
[11] S.G. Miaou, P.H. Sung, C.Y. Huang, “A Customized Human Fall Detection
System Using Omni-Camera Images and Personal Information”, 1st
Trans-Disciplinary Conference on Distributed Diagnosis and Home
Healthcare(D2H2), pp. 39–42, Arlington, USA 2006.
[12] C. Rougier, J. Meunier, A. St-Arnaud, J. Rousseau, “Fall Detection from Human Shape and Motion History Using Video Surveillance”, 21st IEEE International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), pp. 875–880, Niagara Falls, Canada , 2007.
[13] R. Cucchiara, C. Grana, A. Prati, R. Vezzani, “Probabilistic posture classiﬁcation for human-behavior analysis”, IEEE Transactions on Systems,
Man, and Cybernetics - Part A: Systems and Humans, vol.35, pp. 42–54, 2005
[14] A.H. Khandoker, D.T.H. Lai, R.K. Begg, M. Palaniswami, “Wavelet-based
feature extraction for support vector machines for screening balance
impairments in the elderly”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.15, pp.587–597, 2007
[15] L. Hazelhoff, J. Han, P.H.N. de With, “Video-based fall detection in the home using principal component analysis”, Advanced Concepts for Intelligent Vision Systems, vol. 5259, pp.298–309, 2008
[16] T. Zhang, J. Wang, P. Liu, J. Hou, “Fall detection by embedding an accelerometer in cellphone and using KFD algorithm”, International Journal of Computer Science and Network Security, vol. 6, 2006
[17] 最近鄰居法. [Online]. Available: https://zh.wikipedia.org/wiki/最近鄰居
法.[Accessed: 08-Jun-2017].
[18] Fisher’s lineardiscriminant. [Online].Available:
http://www.it610.com/article/5709122.htm. [Accessed: 08-Jun-2017].
[19] Wikipedia-Support Vector machine. [Online]. Available:
https://en.wikipedia.org/wiki/Support_vector_machine. [Accessed: 08-Jun-2017].
[20] X. Zhuang, J. Huang, G. Potamianos, M. Hasegawa-Johnson, “Acoustic Fall
Detection Using Gaussian Mixture Models And Gmm Super-Vectors”, IEEE
International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pp.69–72, Taipei, Taiwan , 2009
[21] 鄭士奇, ”以高斯混合模型為基礎並使用陰影濾除之動態背景影像模型
建立”, 電機與控制工程系所, 國立交通大學, 新竹市, 2005.
[22] B.U. Toreyin, E.B. Soyer, I. Onaran, and A.E. Cetin, “Falling Person Detection Using Multi-Sensor Signal Processing”, 15th IEEE Signal Processing and Communications Applications Conference, SIU & EURASIP
Journal on Advances in Signal Processing, Vol. 8, Eskisehir, Turkey , 2007
[23] 隱馬爾可夫模型. [Online]. Available: https://zh.wikipedia.org/wiki/隱馬爾
可夫模型.[Accessed: 08-Jun-2017].
[24] H. Foroughi, A. Naseri, A. Saberi, H.S. Yazdi, “An Eigenspace-Based Approach for Human Fall Detection Using Integrated Time Motion Image and Neural Network”, 9th IEEE International Conference on Signal Processing (ICSP),pp.1499–1503, Las Vegas, USA 2008.
[25] H. Foroughi, B.S. Aski, H. Pourreza, “Intelligent Video Surveillance for Monitoring Fall Detection of Elderly in Home Environments”, 11th IEEE
International Conference on Computer and Information Technology (ICCIT),
pp. 219–224, Khulna, Bangladesh, 2008.
[26] W.S. McCulloch, and W. Pitts, “A logical calculus of the ideas immanent in
nervous activity”, Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133, 1943
[27] F. Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”, Psychological Review, vol. 65, No.
6, pp. 386-408, 1958
[28] M. Minsky, and S. Papert, Perceptrons: An Introduction to Computational
Geometry, M.I.T. Press, 1969
[29] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V.
Vanhoucke, and A. Rabinovich. “Going deeper with convolutions”, IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, pp.
1-9,2015
[30] A. Krenker, J. Bes ̌ter and A. Kos, “Introduction to the Artificial Neural
Networks”, Artificial Neural Networks - Methodological Advances and
Biomedical Applications, ISBN: 978-953-307-243-2, 2011
[31] 蘇木春、張孝德編著, 機器學習：類神經網路、模糊系統以及基因演
算法則，第二版, 全華科技圖書，民國一百零一年
[32] 深度學習：使用激勵函數的目的、如何選擇激勵函數 . [Online].
Available:http://mropengate.blogspot.tw/2017/02/deep-learning-role-of-acti
vation.html ,[Accessed: 08-Jun-2017].
[33] Feature extraction using convolution. [Online]. Available:
http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_c
onvolution ,[Accessed: 08-Jun-2017].
[34] Convolutional Neural Networks for Visual Recognition. [Online]. Available:
http://cs231n.github.io/convolutional-networks/#pool, [Accessed: 08-Jun-2017].
[35] S. Ji, W. Xu, M. Yang, and K. Yu ,“3D Convolutional Neural Networks for
Human Action Recognition”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 35, pp. 221-231, 2012
[36] J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities", Proceedings of the National Academy of Sciences of the USA, vol. 79, no. 8, pp. 2554–2558, April 1982.
[37] S. Hochreiter and J. Schmidhuber. “Long short-term memory”. Neural Computation, vol. 9, pp. 1735–1780, 1997.
[38] Deep Learning in a Nutshell: Sequence Learning, [Online]. Available:https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-sequence-learning/. [Accessed: 08-Jun-2017].
[39] Montes, Alberto and Salvador, Amaia and Pascual, Santiago and
Giro-i-Nieto, Xavier, “Temporal Activity Detection in Untrimmed Videos
with Recurrent Neural Networks” in 1st NIPS Workshop on Large Scale Computer Vision Systems, December 2016
[40] S. Ioffe and C. Szegedy. “Batch normalization: Accelerating deep network
training by reducing internal covariate shift.”, arXiv preprint arXiv:1502.03167, 2015.
[41] Overfitting.[Online].Available: https://en.wikipedia.org/wiki/Overfitting.
[Accessed: 08-Jun-2017].
[42] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov.
“Dropout: A simple way to prevent neural networks from overﬁtting”. The
Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014.
[43] Y. N Dauphin, H. de Vries, J. Chung, and Y. Bengio. “Rmsprop and equilibrated adaptive learning rates for non-convex optimization”. arXiv preprint arXiv:1502.04390, 2015
[44] E. Auvinet, C. Rougier, J.Meunier, A. St-Arnaud, J. Rousseau, "Multiple cameras fall dataset", Technical report 1350, DIRO - Université de Montréal, July 2010.
[45] B. Kwolek, M. Kepski, “Human fall detection on embedded platform using
depth maps and wireless accelerometer”, Computer Methods and Programs
in Biomedicine, vol. 117, pp. 489-501, December 2014
[46] I. Charfi, J. Dubois, M. Atri, R. Tourki, "Optimised spatio-temporal descriptors for real-time fall detection: comparison of SVM and Adaboost based classification”, Journal of Electronic Imaging (JEI), Vol. 22, pp.17,
October 2013
[47] Keras: Deep Learning library for Theano and TensorFlow. [Online].
Available: https://keras.io/. [Accessed: 08-Jun-2017].
[48] TensorFlow 和 Caffe、MXNet、Keras 等其他深度學習框架的對比. [Online].
Available: http://www.hksilicon.com/articles/1277383. [Accessed: 08-Jun-2017].
[49] Feichtenhofer, Christoph and Pinz, Axel and Zisserman, Andrew,
”Convolutional Two-Stream Network Fusion for Video Action Recognition”,
Conference on Computer Vision and Pattern Recognition(CVPR), Seattle, USA, 2016
[50] Receiver operating characteristic. [Online]. Available:
https://en.wikipedia.org/wiki/Receiver_operating_characteristic.
[Accessed: 08-Jun-2017].
[51] Cohen′s kappa. [Online]. Available:
https://en.wikipedia.org/wiki/Cohen%27s_kappa. [Accessed: 08-Jun-2017].
[52] Kohavi, Ron. “A study of cross-validation and bootstrap for accuracy estimation and model selection”. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. vol. 2, pp. 1137-1143, 1995

指導教授

王家慶(Jia-Ching Wang)

審核日期

2017-7-20

推文