Dissertationen zum Thema „Human Fall detection“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-23 Dissertationen für die Forschung zum Thema "Human Fall detection" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Dissertationen für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
DROGHINI, DIEGO. „Ambient Intelligence: Computational Audio Processing For Human Fall Detection“. Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263538.
Der volle Inhalt der QuelleAt present, Ambient Intelligence represents the challenge of the future. To obtain an ecosystem that is fully functional and calibrated to the user need, numerous systems, each of them dedicated to a specific task, must be integrated. One of these sub-systems is the human fall detection. Both research community and governments gave particular attention to the human fall detection because the fall is the first cause of death for people over 65. In this thesis, the human fall detection is addressed from an audio perspective: a dataset named A3FALL, composed of a corpus of several audio fall events of every-day objects and both simulated and real human falls recorded in 3 different rooms, has been presented. In particular, a special floor acoustic sensor (FAS) has been developed from this purpose and used to record the dataset together with an array of a microphone array. Different approaches that work with a different knowledge base according to the specific task have been proposed: first, two supervised approaches have been described that have highlighted the peculiarities of the audio drop detection and demonstrated the effectiveness of the proposed sensor. The human falls hardly available for systems development, unsupervised systems have been proposed that do not need examples of the target class in the learning phase. It has been shown that unsupervised approaches have better performance than the art state systems, but they do work well in not very complex scenarios. Finally, methods that work under more realistic conditions have been developed and described. A system where the user intervenes by correcting the system’s operation for a considerable reduction of false alarms is proposed. Then a few-shot learning approach that without any user intervention can achieve promising results using only a few examples of human fall in the training phase has been presented. The thesis concludes with an extensive evaluation of a Siamese Convolutional Autoencoder based approach. It has been shown that this approach outperforms all the previously proposed systems when assessed in a complex scenario.
Ojetola, O. „Detection of human falls using wearable sensors“. Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/93d006a7-540d-4ceb-8e19-df03e2f6c67f/1.
Der volle Inhalt der QuelleMastorakis, Georgios. „Human fall detection methodologies : from machine learning using acted data to fall modelling using myoskeletal simulation“. Thesis, Kingston University, 2018. http://eprints.kingston.ac.uk/42275/.
Der volle Inhalt der QuelleNa, Hana. „A study on detection of risk factors of a toddler's fall injuries using visual dynamic motion cues“. Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/3214.
Der volle Inhalt der QuelleRuneskog, Henrik. „Continuous Balance Evaluation by Image Analysis of Live Video : Fall Prevention Through Pose Estimation“. Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297541.
Der volle Inhalt der QuelleDjupinlärningstekniken Kroppshållningsestimation är ett lovande medel gällande att följa en person och identifiera dess kroppshållning. Eftersom kroppshållning och balans är två närliggande koncept, kan användning av kroppshållningsestimation appliceras till fallprevention. Genom att härleda läget för en persons tyngdpunkt och därefter läget för dess tryckcentrum, kan utvärdering en persons balans genomföras utan att använda kraftplattor eller sensorer och att enbart använda kameror. I denna studie har en kroppshållningsestimationmodell tillsammans med en fördefinierad kroppsviktfördelning använts för att extrahera läget för en persons tryckcentrum i realtid. Den föreslagna metoden använder två olika metoder för att utvinna djupseende av bilderna från kameror - stereoskopi genom användning av två RGB-kameror eller genom användning av en RGB-djupseende kamera. Det estimerade läget av tryckcentrat jämfördes med läget av samma parameter utvunnet genom användning av tryckplattan Wii Balance Board. Eftersom den föreslagna metoden var ämnad att fungera i realtid och utan hjälp av en GPU, blev valet av kroppshållningsestimationsmodellen inriktat på att maximera mjukvaruhastighet. Därför användes tre olika modeller - en mindre och snabbare modell vid namn Lightweight Pose Network, en större och mer träffsäker modell vid namn High-Resolution Network och en model som placerar sig någonstans mitt emellan de två andra modellerna gällande snabbhet och träffsäkerhet vid namn Pose Resolution Network. Den föreslagna metoden visade lovande resultat för utvinning av balansparametrar i realtid, fastän den största felfaktorn visade sig vara djupseendetekniken. Resultaten visade att användning av en mindre och snabbare kroppshållningsestimationsmodellen påvisar att hålla måttet i jämförelse med större och mer träffsäkra modeller vid användning i realtid och utan användning av externa dataprocessorer.
Lin, Chia-Hua. „A Real-Time Human Posture Classifier and Fall-Detector“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1401707860.
Der volle Inhalt der QuelleAslamy, Benjamin. „Utveckling av ett multisensorsystem för falldetekteringsanordningar“. Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188401.
Der volle Inhalt der QuelleAccidental falls among the elderly is a major public health problem. As a result, a variety of systems have been developed for remote monitoring of the elderly to permit early detection of falls. The majority of the research that has been done so far in fall accidents has focused on developing new more successful algorithms spe- cifically to identify fall from non-fall. Although the statistics show that mortality and injuries caused by falls are increasing every year in conjunction with the in- creasing proportion of older people in the population. This thesis is about improving the current fall detection devices by covering the gaps and meet the needs of the current fall detection techniques. The improve- ments that have been identified is to provide a secure assessment of the patient's health and be able to call for aid more quickly when a fall occurs. Another im- provement is the mobility for the elderly to be outdoors and have the ability to per- form daily activities without being limited by the location position. In summary it can be said that a multisensor system in form of a prototype has been designed to cover the deficiencies and improvements that have been identi- fied. Apart from detection of falls and body movements through an accelerometer sensor the prototype does also include a sensor for detecting vital signs in form of ECG. It also supports cellular and wireless network communication in form of GPRS and Wi-Fi to enable freedom of movement for the elderly. Furthermore, the prototype includes a sensor for GPS that provides information about location position.
Shaafi, Aymen. „Secured and trusted remote wireless health monitoring systems for assisted living of elderly people“. Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5208.
Der volle Inhalt der QuelleAging population is one of the key problems for the vast majority of many countries. The number of elderly people who suffer from multiple diseases and need continuous monitoring of their vital signs increases everyday, resulting in additional healthcare costs. Modern healthcare systems in geriatric medicine often require elderly presence at the hospital which conflict with their demand for independence and privacy. Recent developments on remote e-health monitoring, provides a wide range of solutions. However, most of the devices are designed for specific medical sensing and operate independently from each other. There is still a lack of integrated framework with high interoperability and continuous online monitoring support for further correlation analysis. This thesis is a step towards a remote, complete, and continuous data gathering system for elderly people with various types of health problems. Our research spirit is motivated by immediate demand in a secured and trusted remote wireless health monitoring System for assisted living Elderly people, combining various data sources. To create such a complete system we divide it into subsystems, in order to make it feasible and easy to implement, thus allowing us to update each subsystem individually in the future studies without affecting other integrated subsystems. The main focus is on a complete remote e-health monitoring system. The list of main contributions contains (1) propose a new approach for security of monitored devices and propose a solution to prevent MiTM attacks and reduce energy consumption, (2) we propose reliable fall detection,(3) investigating and developing a novel recognition method of daily activities for monitored elderly patient, (4) propose an approach to enhance the reliability of the system and to reduce false alarms and unnecessary interventions, (5) propose and develop a sign language to text converter algorithm using multi-sensor fusion analysis. As a result, we expect to provide a monitoring system with reliable accuracy in the detection of abnormal events, and raise an alarm upon detection of such events to seek help and assistance
Liao, Kai-Chieh, und 廖楷捷. „Human Tracking System and Fall Detection“. Thesis, 2008. http://ndltd.ncl.edu.tw/handle/58887516245737991221.
Der volle Inhalt der Quelle國立中正大學
電機工程所
96
In recent years, because the decline in birth rate and the increase of national average life span, special attention is gradually shifting to the security of the elderly people and children, fall is one of the often seen accidental among them. The purpose of this thesis is to detect the situation and to examine the possibility whether or not the personages fall while moving by a monitoring system. Based on a result of detection, the doctor and medical treatment-nursing group could put forward a medical treatment plan and carry on improvement of the pedestrian''s activity environment. The application of computer vision systems has widely spread along with the progress of the computer software and hardware. In the recent ten years, the researchers show their interest in the research of identifying and analyzing the humans'' motions or behaviors. The academia has a quite good result of study in the distinguishing of human motion posture at present. This thesis proposed a detection system on examining and judging of the pedestrian who falls for the abnormal behavior or personage''s own independence. This thesis mainly divides into two focal points: the fist is individual distinguish personages and the second is the detection of fall. Firstly, the personage''s information and definition of the upper part of the body information were found, this result was carry on and be the method for human tracking. Due to the complexional information of human, the group of other moving objects could be leach out. Besides, the part of fall detection, we detect the person in video who falls based on the information of its horizontal, vertical projections. The function of this system can be used on human tracking and fall detection, and it could be achieved without define models and large number of mathematics operation.
Liu, Yin-Chu, und 劉殷助. „Hidden Conditonal Random Fields for Human Fall Detection“. Thesis, 2014. http://ndltd.ncl.edu.tw/handle/55383181007118280640.
Der volle Inhalt der Quelle國立臺灣科技大學
電子工程系
102
In recent years, the global population has begun to age rapidly. Automatic fall detection for senior citizens has become an important issue for smart home. In this research, we propose a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on Hidden Conditional Random Fields model, and an intelligent combination of height estimation and appearance cues. Our system can efficiently distinguish “fall-down incidents” from “fall-like incidents” such as sit-down and squat. Experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate. Also, the proposed system outperforms Hidden Markov Chain and Cuboids in terms of detection rate.
HSU, FANG-YU, und 許芳瑜. „Human Fall Detection Based on Multiple Cameras and Deep Learning“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/x55af9.
Der volle Inhalt der Quelle國立中正大學
電機工程研究所
106
In recent years, due to the development of medical technology, people are more and more long-lived, and proportion of the elderly population has increased year by year. Therefore, the medical care of the elderly is becoming more and more important. We used computer vision and image processing technology to surveil the medical care of the elderly family. In this paper, we use two cameras to capture different views in the same scene for abnormal behavior events (such as falling down) detection and identification. This paper combines long-term 2D human contour to establish and identify abnormal behavior models with deep learning architecture. The anomalous behavior recognition algorithm proposed in this paper consists of three main steps. First, we detect 2D human contour from surveillance cameras. Second, we use motion history image (MHI) method and combine long-term sequence actions into several MHI images. Finally, we use deep learning technology (CNN and CNN + LSTM architectures) to recognize the abnormal behavior of MHI sequences. This method not only recognizes the motions of walking, standing, falling down, but recognizes rising to avoid excessive false alarms. This paper compared different segments of the abnormal behavior identification. Due to the falling frames are about 3-5 frames, the abnormal behavior (falling down) recognition rate will be low if we didn't divide the sequences in long-time recognition. We compared the performance of abnormal behaviors that used different training methods and different ground truth (GT) of the same deep learning architecture (CNN + LSTM architectures). We also compared the performance that used different architectures (CNN architectures and CNN + LSTM architectures) and single/double cameras. In this paper, we experienced the method that used two-stage training and GT was the majority decision. We got the accuracy of abnormal behaviors identification is 97.66%.
CHEN, CHAS-HSUN, und 陳昭勳. „Human Fall Detection Based on Video Analysis and LRCN Model“. Thesis, 2019. http://ndltd.ncl.edu.tw/handle/74353j.
Der volle Inhalt der Quelle國立臺灣科技大學
自動化及控制研究所
107
In recent years, due to medical progress, human life has become longer and longer, coupled with the phenomenon of “less child” in the country, causing the population to age. The home-cared issue is often on the news page, and the fall is easy to cause damage to the elderly. Therefore, we use computer vision. And deep learning techniques to detect the inconvenience of movement or the activity status of the elderly, and to solve the problem of insufficient manpower in the nursing center. In view of the fact that previous theses often use manual tags to define the parameters of falls. This thesis uses two-dimension image vision technology with deep learning algorithms to propose a system that can automatically identify and detect falls of the elderly. The system structure of this thesis can be divided into three parts. Firstly, the image information of the moving object is obtained by Dense Optical Flow. Then use the Sliding Window technology to divide the big movie into many small video clips, each of clips have 10 images. Finally, the LRCN (CNN+RNN) hybrid model in depth learning technology is used to analyze the optical flow image and discriminate the occurrence of the fall event. The CNN extraction feature model uses the VGG16 architecture, while the RNN timing discrimination model uses the LSTM architecture as the hidden layer. Finally, we tune the hyperparameters and then use five cross-validation to check whether the model has overfitting problems or not. The experimental results show that the identification of the fall event can achieve sensitivity 99.76693% and accuracy 97.67701%.
Lin, You-Rong, und 林佑融. „A Robust Fall Detection Scheme Using Human Shadow and SVM Classifiers“. Thesis, 2012. http://ndltd.ncl.edu.tw/handle/12696932794927637870.
Der volle Inhalt der Quelle國立臺灣科技大學
電子工程系
100
We present a novel real-time video-based human fall detection system in this thesis. Because the system is based on a combination of shadow-based features and various human postures, it can distinguish between fall-down and fall-like incidents with a high degree of accuracy. To support effective operation in different viewpoints, we propose a new feature called virtual height that can estimate the body height without 3D model reconstruction. As a result, the model is low computational complexity. Our experiment results demonstrate that the proposed system can achieve a high detection rate and a low false alarm rate.
Huang, Yi-Chang, und 黃逸昌. „A Human Fall Detection System Using an Omni-Directional Camera in Practical Environments“. Thesis, 2008. http://ndltd.ncl.edu.tw/handle/65392259969658027917.
Der volle Inhalt der Quelle中原大學
電子工程研究所
96
In recent years, the number of the elderly increases rapidly. Consequently the health care of the elderly attracts more and more attention and we need to invest a lot of resources to maintain its quality. This thesis proposes a vision-based fall detection system using an omni-directional camera for the elderly and patients at home and in health-care institutions, hoping to detect the fall accident immediately when it happens and notify the medical personnel to provide the emergency care in time. In order to make the system more practical in real environments, we consider the practical environmental factors that may take place in our daily life. For instance, the occurrence of light source glimmer and turning a light on and off and leaving over static abandoned objects in the environment and resulting in multiple targets. The former can be solved by detecting the degree of luminance changes and the latter can be solved by using the static characteristic of abandoned objects. In addition we divide the fall down patterns in omni-directional images into non-radial and radial directions according to the angle associated with a body line. We further categorize the radial fall down patterns into inward and outward directions. We extract suitable features for these three different fall down patterns, including angle and length variation associated with the body line and Motion History Images. Given these features, a simple thresholding and decision tree technique is adopted for fall detection. Experimental results show that the proposed system has overcome the practical environmental factors of light source glimmer and static abandoned objects, increasing the practicability of the system in a real-world environment. In fall-down detection, since multiple fall-down patterns are considered, the recognition accuracy of the fall down system is improved from 0.73 to 0.87 and the Kappa value is improved from 0.47 to 0.75. These results show that we have proposed an effective fall down detecting system in this thesis.
CHEN, WEI-CHIEH, und 陳暐傑. „Universal Human Posture and Behavior Detection System – A Fall of the Elderly Example“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/52r4ms.
Der volle Inhalt der Quelle中華大學
電機工程學系
106
In recent years, the aging society has become a common phenomenon in all countries. There is no exception in Taiwan. Therefore, the care of the elderly is becoming more and more important. In the elderly accident, falling is the second most important factor. The occurrence of falls often causes fractures, and causes physical and mental injury. It can be seen that falls are a threat to the health of the elderly. With the performance of the computer is getting faster, CCD costs are getting lower and lower, enabling people to analysis of real-time images through image processing. This paper proposes a fall detection system that uses OpenPose[12] to extract skeletons through a camera. It is mainly used to detect human fall. This system can detect and judge whether a pedestrian falls in a multi-person environment.
Liao, Yi-Ting, und 廖翊廷. „Slip and Fall Detection using Spatiotemporal Characteristics of Human Object for video Surveillance System“. Thesis, 2009. http://ndltd.ncl.edu.tw/handle/58287973684223901071.
Der volle Inhalt der Quelle國立清華大學
電機工程學系
97
Fall and slip of the elderly are the main concerns for home care or day care center. We proposed a method to detect a slip event and a fall event by computing integrated spatiotemporal energy (ISTE) map that includes motion and time of motion occurrence as our motion feature. The extracted human shape is represented by an ellipse that provides crucial information of human motion activities. We use this features to detect the events in the video with non-fixed frame rate. In this work, we assume that the person is on the ground with no or little motion after the fall accident. Our experiments are demonstrated in the indoor and the outdoor scenes, where the illuminate condition varies. So the threshold of the background subtraction and the parameters of the smoothing filter are adjusted independently. Experimental results show that our method is effective for fall and slip detection. The total number of testing frames is about , and we use an Intel Core2 Duo 1.8GHz CPU on a Microsoft Windows XP operating system.
Sung, Pei-Hsu, und 宋佩栩. „A Customized Human Fall Detection System Using an Omni-Directional Camera and Personal Information“. Thesis, 2006. http://ndltd.ncl.edu.tw/handle/22546835967301344668.
Der volle Inhalt der Quelle中原大學
電子工程研究所
94
Due to the advancement of technology and medicine, people begin to pay more attention to the quality improvement of health care. Many researches show that the fall accident occupies 80% of all accidents in a hospital. The fall accident may cause the condition of a patient deteriorated, producing complications and extending the patient’s stay in the hospital. As a result, it increases the burden of a family and seriously wastes medical resources from the society. Thus, preventing the fall accident and detect it immediately is one of the important topics regarding the quality improvement of health care. This thesis proposes a reliable tele-care system that can detect the fall accident immediately, notify medical personnel when the accident occurs, prevent the patient’s condition from deteriorating due to late treatment, and reduce the burden of medical personnel. A unique feature of the proposed system is that we use a MapCam to capture 360∘scense simultaneously and eliminate any blind spot. Furthermore, personal information is integrated into the system and makes it smarter by customizing the system for each individual. With personal information (including basic personal data, danger factor, electronic health history, etc), we can adjust the detection sensitivity on a case by case basis to reduce unnecessary alarms, and put more attention on the elderly with special diseases or conditions. We also propose another fall detection algorithm for various falling directions and walking paths. The experimental results show that using a simple fall detection algorithm and combining it with simple personal information can raise fall detection accuracy and reliability effectively in a particular environment. When the algorithm itself is robust enough, perhaps the detection accuracy can be increased only if biomedical signals are considered as well. The experimental results also show that the new fall detection algorithm proposed here can do a good job in an indoor environment for all fall cases (different walking paths and falling directions). The successful recognition rate and kappa value of our system with personal information are 0.92 and 0.92, respectively, showing that we have a reliable system.
Lin, Ping-Min, und 林秉旻. „A real-time fall detection system using human body contours information and kNN classifier“. Thesis, 2008. http://ndltd.ncl.edu.tw/handle/17980118041306113240.
Der volle Inhalt der Quelle國立交通大學
多媒體工程研究所
96
In the province of Human Computer Interaction, monitor system is an important study. As long as the situation of aging society becomes more and more serious, the care costs will increase plenty. That is the reason so many domestic and foreign scholars throw themselves into the research of elderly care monitor system in order to support the existing care system and reduce the huge expenditures of labor costs. This research used and integrated the human face detection system developed by our laboratory to get the characteristics of the human body and track that. And also used k-th Nearest Neighbor classification to classify the human postures. Then using the information of the changing rate collected by many experiments this research finally can develop a fall detection system.
Lin, Meng-Wei, und 林孟緯. „The Study of Fall Detection System Based on Human Shape Features and Motion History“. Thesis, 2017. http://ndltd.ncl.edu.tw/handle/zbvmp5.
Der volle Inhalt der Quelle國立交通大學
生醫工程研究所
106
The issue of health care of elder is more important in recent years. Therefore, the research of fall detection system is very flourishing. In this thesis, we propose a fall detection system based on image processing. This system can detect falls of people in surveillance environment by motion and human shape in image. When system detect fall, it will send message to reduce waiting time and increase the survival rate. There are two contributions of this thesis. First, we proposes a set of features by foreground and human shape. Train a classifier make the result of fall detection system more accurately. Finally, we completes the prototype of system in fall detection base on our proposed feature and motion history image, and the system can distinguish between falls and normal activities such as sitting, lying or crouching. From experimental results, our proposed method can increase efficacy of fall detection. We also prove our system can detect fall in several continuous movement by video. Finally, we integrate our proposed system with accelerometer to increase the accuracy of fall detection system.
Nizam, Y., Jamil M. M. Abdul, M. N. H. Mohd, Mansour Youseffi und Morgan C. T. Denyer. „A novel algorithm for human fall detection using height, velocity and position of the subject from depth maps“. 2018. http://hdl.handle.net/10454/16944.
Der volle Inhalt der QuelleHuman fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches include some sort of wearable devices, ambient based devices or non-invasive vision-based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on the height, velocity and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information. Finally position of the subject is identified for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 94.81% with sensitivity of 100% and specificity of 93.33%.
Partly sponsored by Center for Graduate Studies. This work is funded under the project titled “Biomechanics computational modeling using depth maps for improvement on gait analysis”. Universiti Tun Hussein Onn Malaysia for provided lab components and GPPS (Project Vot No. U462) sponsor.
Ni, Wei-En, und 倪偉恩. „Two Camera-based 3D Human Body Model Construction and Hierarchical Fuzzy Classifiers for Posture Recognition and Fall Detection“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/6f8g34.
Der volle Inhalt der QuelleBatista, Miriam Raquel da Silva Mamede. „WearIoT: swearable IoT human emergency system“. Master's thesis, 2018. http://hdl.handle.net/10071/18211.
Der volle Inhalt der QuelleThe health area was one of the many beneficiaries of technological evolution, giving rise to new concepts that aim to improve or even prolong people’s lives. Wearable monitoring systems, along with wireless communications, form the basis of an emerging class of sensor networks. These information technologies enable the early detection of abnormal conditions and help in their prevention. The goal is to create one of these systems composed by a network of sensors that is implemented in a garment through conductive wires with connected sensors. In contact with the human body it has the function of doing several readings, e.g., body temperature, heartbeat, among others. Another goal is to detect user falls. The detection of falls is increasingly important for the user, as it is a situation that can endanger people’s health. For the development of this concept, Mobile Communications and the Global Positioning System are used. The first is a technology that allows to create emergency calls in response to system alarms, the second indicates the geographical location. To complement the system there is an online platform that registers the position of the user as well as his data. There is also an alert area in which the user can check his alarming values. In case of emergency the system contacts the emergency services or in special cases help can be obtained through an UAV.
Chia, Po Chun, und 賈博鈞. „Applying Biped Humanoid Robot Technologies to Fall Down Scenario Designs and Detections for Human“. Thesis, 2010. http://ndltd.ncl.edu.tw/handle/78868669441203945307.
Der volle Inhalt der Quelle長庚大學
醫療機電工程研究所
98
Preventing Falling down is an important issue for the aging society; therefore, the fall detection is crucial for the healthcare system. Fall signal is generally collected from a 3-axis accelerometer which is placed on the human’s chest, and the collected signal is further analyzed to develop the fall detection algorithms. Nevertheless, it is not easy to collect realistic fall signals because the experiments of falls may cause serious injuries. Therefore, most of collected fall signals are conservative and cannot completely represent the situations of actual falls. That means the volunteer may perform a slow fall when collecting the fall signal. Especially, it is hardly to collect the fall signals from high risk fall situations such as falls from stairs. Biped humanoid robot researches are fast increasing in recent years, because the torso structures of the biped humanoid robots is similar to the human beings. This thesis proposes a biped humanoid robot based fall scenario simulation system. The proposed fall scenario simulation system constructs the gait pattern libraries for the different fall scenarios which are similar to the falls of human beings. A 3-axis accelerometer is also placed on the chest of the biped humanoid robot to measure the fall signals. In order to verify the proposed approach, a motion capture system is employed in this study to measure the fall motions. At the same time, the fall motions collected from the motion capture system and the fall signals collected from the 3-axis accelerometer are synchronously recorded to verify the signal correlations between the biped humanoid robot and the human beings. Experiment results shows that the signal correlations between the biped humanoid robot and the human beings for typical forward, side and backward falls. Based on this correlation performance, the high risk fall signals such as falls from stairs and slip falls are collected from the biped humanoid robots only. Therefore, the proposed biped humanoid robot based fall scenario simulation system may effectively collect the fall signals for the further fall detection algorithm studies.