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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成 | zh_TW |
| dc.contributor.advisor | Li-Chen Fu | en |
| dc.contributor.author | 湯嘉懿 | zh_TW |
| dc.contributor.author | Jia-yi Tang | en |
| dc.date.accessioned | 2023-06-20T16:27:59Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-06-20 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-02-05 | - |
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Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ... & Ng, A. Y. (2009, May). ROS: an open-source Robot Operating System. In ICRA workshop on open source software (Vol. 3, No. 3.2, p. 5). World Health Organization, World Health Organization. Ageing, & Life Course Unit. (2008). WHO global report on falls prevention in older age. World Health Organization. Brauer, S. G., Burns, Y. R., & Galley, P. (2000). A prospective study of laboratory and clinical measures of postural stability to predict community-dwelling fallers. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 55(8), M469-M476. Thaler-Kall, K., Peters, A., Thorand, B., Grill, E., Autenrieth, C. S., Horsch, A., & Meisinger, C. (2015). Description of spatio-temporal gait parameters in elderly people and their association with history of falls: results of the population-based cross-sectional KORA-Age study. BMC geriatrics, 15(1), 1-8. Hamacher, D., Singh, N. B., Van Dieën, J. H., Heller, M. O., & Taylor, W. R. (2011). Kinematic measures for assessing gait stability in elderly individuals: a systematic review. Journal of The Royal Society Interface, 8(65), 1682-1698. Barak, Y., Wagenaar, R. C., & Holt, K. G. (2006). Gait characteristics of elderly people with a history of falls: a dynamic approach. Physical therapy, 86(11), 1501-1510. Ortells J, Herrero-Ezquerro M T, Mollineda R A. Vision-based gait impairment analysis for aided diagnosis[J]. Medical & biological engineering & computing, 2018, 56(9): 1553-1564. Inman, V. T., & Eberhart, H. D. (1953). The major determinants in normal and pathological gait. JBJS, 35(3), 543-558. Stereo Labs, “zed-openpose,” . [Source code] :https://github.com/stereolabs/zed-openpose Mortaza, N., Abu Osman, N. A., & Mehdikhani, N. (2014). Are the spatio-temporal parameters of gait capable of distinguishing a faller from a non-faller elderly. Eur J Phys Rehabil Med, 50(6), 677-691. Kargar, B. A. H., Mollahosseini, A., Struemph, T., Pace, W., Nielsen, R. D., & Mahoor, M. H. (2014, August). Automatic measurement of physical mobility in Get-Up-and-Go Test using kinect sensor. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3492-3495). IEEE. Verghese, J., Holtzer, R., Lipton, R. B., & Wang, C. (2009). Quantitative gait markers and incident fall risk in older adults. The Journals of Gerontology: Series A, 64(8), 896-901. Murray, M. P., Drought, A. B., & Kory, R. C. (1964). Walking patterns of normal men. JBJS, 46(2), 335-360. Heitmann, D. K., Gossman, M. R., Shaddeau, S. A., & Jackson, J. R. (1989). Balance performance and step width in noninstitutionalized, elderly, female fallers and nonfallers. Physical therapy, 69(11), 923-931. El-Menyar, A., Tilley, E., Al-Thani, H., & Latifi, R. (2019). Females fall more from heights but males survive less among a geriatric population: insights from an American level 1 trauma center. 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Deep learning for fall risk assessment with inertial sensors: Utilizing domain knowledge in spatio-temporal gait parameters. IEEE journal of biomedical and health informatics, 24(7), 1994-2005. Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014). 2d human pose estimation: New benchmark and state of the art analysis. In Proceedings of the IEEE Conference on computer Vision and Pattern Recognition (pp. 3686-3693). Martinez, M., De Leon, P. L., & Keeley, D. (2017, March). Novelty detection for predicting falls risk using smartphone gait data. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2202-2206). IEEE. Fox, D., Burgard, W., Dellaert, F., & Thrun, S. (1999). Monte carlo localization: Efficient position estimation for mobile robots. AAAI/IAAI, 1999(343-349), 2-2. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87631 | - |
| dc.description.abstract | 對於未來的世代來說，老人健康照顧是項至關重要的問題。其中很大一部分的老人在生活中獨自生活，這就會導致許多意外發生，其中跌倒是造成老人受傷和死亡的最常見的原因。現今的人口趨勢中，因為老年人口迅速增加，對於看護者的需求也迅速上升。因此，針對個人居家老年的跌倒預測機器人具有相當大的潛力。跌倒预测模型可以在跌倒發生前對用戶的步態特征進行評估，并有足够的时间提醒老年人调整或采取相应的保护措施。在减少跌倒对人体的伤害的同时，减少跌倒带来的医疗费用，增强老年人独立生活的信心。
本研究旨在以創新的方法，創造一個給家用機器人使用的跌倒預測程式。我們開發了移动型機器人，此機器人將在家中跟隨使用者，並观察人体的行走状态。深度相機隨著時間輸入RGB-D圖像，就可以獲得人體骨骼特徵。而這些特徵會被用來分析提取受试者的步态时空参数和运动学参数，对其跌倒风险进行了评估和预测。將跌倒風險等級分為高跌倒風險和低跌倒風險兩類。考慮資料獲取的成本問題，高跌倒風險樣本不易獲得，採用新穎性檢測模型單類支持向量機在不平衡資料集下對特徵資料進行了訓練和評估。基於上述的研究內容，我們設計並實現了一個基於深度相機的跌倒風險預測系統，系統未來可以面向居家環境下為老年人提供長期的步態健康監測服務。 | zh_TW |
| dc.description.abstract | Elderly health care is getting to be a critical issue when our society becomes aging. A large proportion of the elderly live alone in their lives, which leads to many accidents, among which falls are the most common cause of injury and even death. In today's population trends, the need for caregivers is rapidly growing due to the rapidly increasing elderly population. Therefore, fall prediction robots for the elderly at home have considerable potential. The fall prediction model can evaluate the user's gait features before a fall event occurs, and thus has enough time to remind the elderly to adjust or take corresponding protective measures. While reducing the harm to the human body caused by falls, it also reduces the medical expenses caused by falls, and build the confidence of the elderly to live independently.
This research aims to create a fall prediction program for domestic robots in an innovative way. We have developed a mobile robot that will track the user at home and observe the walking state of the human body. The depth camera inputs RGB-D images over time, and the human skeleton features can be obtained. These features will be used to analyze and extract the spatiotemporal and kinematic parameters of the subjects' gait, and to assess and predict their fall risks. The fall risk level is divided into high fall risk and low fall risk. Considering the cost of data acquisition, it is difficult to obtain high fall risk samples. A novel detection model, one-class support vector machine, which is trained under an unbalanced dataset is used to evaluate the feature data. Given the above research content, we design and implement a fall risk prediction system based on the depth camera, which can provide long-term gait health monitoring services for the elderly in a home environment in the future. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-06-20T16:27:59Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-06-20T16:27:59Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 # 誌謝 i 摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 1.1. Motivation 1 1.2. Research Objective 2 1.3. Contributions 2 1.4. Thesis Outline 3 Chapter 2 Background and Related Works 4 2.1. Fall Detection and Prediction 4 2.1.1. Wearable Systems 4 2.1.2. Ambient Sensors 6 2.2. Fall Prevention Systems 8 2.2.1. Fall Risk Prediciton 8 2.2.2. Gait Analysis System 11 2.2.2.1 Gait Analysis System based on Contact Sensor 11 2.2.2.2 Gait Analysis System based on Environment Awareness 12 2.2.2.3 Gait Analysis System based on Multimodal Data 13 Chapter 3 Preliminaries 15 3.1. OpenPose 15 3.1.1. Architecture 15 3.1.2. Loss Function 16 3.1.3. Calculate heatmap(Sj*(p)) and vectormap(Lc*(p)) 17 3.1.4. Multi-Person Parsing using PAFs 19 3.2. Tracking Algorithm 20 3.2.1. HOG Algorithm 20 3.2.2. KCF Algorithm 21 3.3. Robot Operating System (ROS) 24 Chapter 4 Fall Prediction Robot 25 4.1. System Overview 25 4.2. Gait Analysis 26 4.2.1. Relation of Gait Parameters and Fall Risk 26 4.2.2. Definition of Gait Cycle 27 4.2.3. Definition of Gait Parameter 29 4.2.3.1 Spatiotemporal Parameter 29 4.2.3.2 Kinematics Parameter 30 4.2.3.3 Dynamics Parameter 31 4.2.3.4 Electromyography Parameter 32 4.2.4. Definition of Gait Parameter 32 4.2.5. Gait Parameter Extraction 33 4.2.5.1 Gait spatiotemporal parameter 33 4.2.5.2 Gait Kinematics Parameter 35 4.2.5.3 Personal Characteristic 36 4.2.5.4 Summary of Various Features 37 4.3. Fall Prediction 38 4.4. Navigation Generation 40 4.4.1. Path Planing 40 4.4.2. Design of Visual Object Tracking Module 43 Chapter 5 Experiments 46 5.1. Hardware & Software 46 5.2. Data 47 5.2.1. Sample Expansion based on the Sliding Window Algorithm 48 5.3. Evaluation Metrics 50 5.3.1. Sample Classification 50 5.3.2. Evaluation Criteria 50 5.4. Results 51 5.4.1. Verification of Skeleton Extraction 51 5.4.2. Fall Prediction Evaluation 54 5.4.3. Mobile Fall Prediction Evaluation 57 Chapter 6 Conclusion 60 6.1. Summary 60 6.2. Limitations 60 6.3. Future Works 61 REFERENCES 63 LIST OF FIGURES Figure 2-1. Examples of various fall risk factors [17]. 9 Figure 2-2. Germany's zebris FDM-T gait analysis running platform [30]. 12 Figure 3-1. Architecture of OpenPose framwork [33]. 15 Figure 3-2. An example of a limb [33]. 18 Figure 3-3. Body part matching. (a) Original image with detected joints (b) K-partite graph (c) Tree structure (d) A set of bipartite graphs [33]. 19 Figure 3-4. The entire flow of OpenPose [33]. 20 Figure 4-1. System Overview of our Fall Prediction and Detection Robot. 25 Figure 4-2. Schematic diagram of gait cycle process [45]. Take the hip angle as an example, θ_h1, θ_h2 θ_h3 are maximum hip joint angle and θ_l1 θ_l2 are minimum hip angle. 27 Figure 4-3. Schematic diagram of gait cycle process. 28 Figure 4-4. Definition of step length and stride. 30 Figure 4-5. Horizontal and vertical displacement of the center of mass when walking [46]. 31 Figure 4-6. Joints from human body extracted by ZED-OpenPose [47]. 33 Figure 4-7. SVM classification hyperplane [57]. 39 Figure 4-8. Our indoor environment. 41 Figure 4-9. General framework. 42 Figure 4-10. Navigation framework. 42 Figure 4-11. Visual module framework. . 44 Figure 5-1. OREO Robot in our Home Environment Laboratory.. 46 Figure 5-2. An example of a subject walking as seen from the bottom (left) and top (right) camera in TOAGA dataset [63]. 47 Figure 5-3. Sample expansion based on sliding window size =1... 50 Figure 5-4. The experimental environment for the system. Left image show the subject walking as high fall risk, and right image show the subject had a normal walking... 57 LIST OF TABLES TABLE 4-1. List of fall risk prediction feature. 37 TABLE 4-2. Contents of each module. 43 TABLE 5-1. List of volunteers in TOAGA dataset. 48 TABLE 5-2. An example of keypoints position of a frame in randomly selected subject walking video sample. 52 TABLE 5-3. Details of keypoints of lower limbs in some selected images. 53 TABLE 5-4. PCKh@0.5 for all keypoints, hips, knees and ankles. 54 TABLE 5-5. Parameter details of some samples. 54 TABLE 5-6. Fall prediction evaluation results. 55 TABLE 5-7. Details of mobile fall prediction evaluation. Due to space, the results of the last four tests have been shown. 58 TABLE 5-8. Details of gait parameters when robot is moving or stationary. 58 TABLE 5-9. Details of mobile fall prediction evaluation. 59 TABLE 5-10. Mobile Fall prediction evaluation results. 59 | - |
| dc.language.iso | en | - |
| dc.subject | 步態特征 | zh_TW |
| dc.subject | 跌倒預測 | zh_TW |
| dc.subject | 移動型機器人 | zh_TW |
| dc.subject | 單類支持向量機 | zh_TW |
| dc.subject | 居家環境 | zh_TW |
| dc.subject | Gait feature | en |
| dc.subject | fall prediction | en |
| dc.subject | mobile robot | en |
| dc.subject | one-class support vector machine | en |
| dc.subject | home environment | en |
| dc.title | 基於視覺之步態分析的跌倒預測機器人 | zh_TW |
| dc.title | Vision-based Gait Analysis Robot for Fall Prediction | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 曾士桓;林靜嫻;簡忠漢 | zh_TW |
| dc.contributor.oralexamcommittee | Shih-Huan Tseng;Chin-Hsien Lin;Jong-Hann Jean | en |
| dc.subject.keyword | 步態特征,跌倒預測,移動型機器人,單類支持向量機,居家環境, | zh_TW |
| dc.subject.keyword | Gait feature,fall prediction,mobile robot,one-class support vector machine,home environment, | en |
| dc.relation.page | 67 | - |
| dc.identifier.doi | 10.6342/NTU202300262 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-02-08 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | 2026-03-01 | - |
| 顯示於系所單位： | 電機工程學系 | |
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