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Yes / Human 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.

Injuries associated with fall accidents pose a significant health problem to society, both in terms of human suffering and economic losses. Existing fall intervention approaches are facing various limitations. This dissertation presented an effort to advance indirect type of injury prevention approach. The overall objective was to develop a new fall event detection algorithm and a new integrative ambulatory measurement (IAM) framework which could further improve the fall detection algorithm's performance in detecting slip-induced backward falls. This type of fall was chosen because slipping contributes to a major portion of fall-related injuries. The new fall detection algorithm was designed to utilize trunk angular kinematics information as measured by Inertial Measurement Units (IMU). Two empirical studies were conducted to demonstrate the utility of the new detection algorithm and the IAM framework in fall event detection. The first study involved a biomechanical analysis of trunk motion features during common Activities of Daily Living (ADLs) and slip-induced falls using an optical motion analysis system. The second study involved collecting laboratory data of common ADLs and slip-induced falls using ambulatory sensors, and evaluating the performance of the new algorithm in fall event detection. Results from the current study indicated that the backward falls were characterized by the unique, simultaneous occurrence of an extremely high trunk extension angular velocity and a slight trunk extension angle. The quadratic form of the two-dimensional discrimination function showed a close-to-ideal overall detection performance (AUC of ROCa = 0.9952). The sensitivity, specificity, and the average response time associated with the specific configuration of the new algorithm were found to be 100%, 95.65%, and 255ms, respectively. The individual calibration significantly improved the response time by 2.4% (6ms). Therefore, it was concluded that slip-induced backward fall was clearly distinguishable from ADLs in the trunk angular phase plot. The new algorithm utilizing a gyroscope and orientation sensor was able to detect backward falls prior to the impact, with a high level of sensitivity and specificity. In addition, individual calibration provided by the IAM framework was able to further enhance the fall detection performance. / Ph. D.

This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing peoples physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall.<p> In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application.<p> This wearable system works in two modes: indoor and outdoor, depending on users demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment.<p> For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life.

This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing peoples physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall.<p> In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application.<p> This wearable system works in two modes: indoor and outdoor, depending on users demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment.<p> For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life.

The World Health Organization claims that there are more than 285 million blind and visually impaired people in the world. In the US, 25 million Americans suffer from total or partial vision loss. As a result of their impairment, they struggle with mobility problems, especially the risk of falling. According to the National Council On Aging, falls are among the primary causes for fatal injury and they are the most common cause of non-fatal trauma-related hospital admissions among older adults. Visibility, an organization that helps visually impaired people, reports that people with visual impairments are twice as likely to fall as their sighted counterparts. The Centers for Disease Control and Prevention reported that 2.5 million American adults were treated for fall-related injuries in 2013, leading to over 800,000 hospitalizations and over 27,000 deaths. The total cost of fall injuries in the United States in 2013 was $31 billion, and the financial total is expected to rise to $67.7 billion by 2020. Reducing the amount of these unexpected hospital visits saves money and expands the quality of life for the affected population. Technology has completely revolutionized how nowadays activities are conducted and how var- ious tasks are accomplished, and mobile devices are at the center of this paradigm shift. According to the Pew Research Center, 64% of American adults own a smartphone currently, and this number is trending upward. Mobile computing devices have evolved to include a plethora of data sensors that can be manipulated to create solutions for humanity, including fall prevention. Fall prevention is an area of research that focuses on strengthening safety in order to prevent falls from occurring. Many fall prevention systems use sensing devices to measure the likelihood of a fall. Sensor data are usually processed using computer vision, data mining, and machine learning techniques. This work pertains to the implementation of a smartphone-based fall prevention system for the elderly and visually impaired. The system consists of two modules: fall prevention and fall detection. Fall prevention is in charge of identifying tripping hazards in the user’s surroundings. Fall detection is in charge of detecting when falls happen and alerting a person of interest. The proposed system is challenged by multiple problems: it has to run in near real time, it has to run efficiently in a smartphone hardware, it has to process structured and unstructured environments, and many more related to image analysis (occlusion, motion blur, computational complexity, etc). The fall prevention module is divided into three parts, floor detection, object-on-floor detection, and distance estimation. The evaluation process of the best approach for floor detection achieved an accuracy of 92%, a precision of 88%, and a recall of 92%. The evaluation process of the best approach for object-on-floor detection achieved an accuracy of 90%, a precision of 56%, and a recall of 78%. The evaluation process of the best approach for distance estimation achieved a MSE error of 0.45 meters. The fall detection module is approached from two perspectives, using inertial measuring units (IMU) embedded in today’s smartphones, and using a 2D camera. The evaluation process of the solution using IMUs achieved an accuracy of 83%, a precision of 89%, and a recall of 58.2%. The evaluation process of the solution that uses a 2D camera achieved an accuracy of 85.37% and a recall of 70.97%.

De nos jours, la détection de chute est un défi pour la santé, notamment pour la surveillance des personnes âgées. Le but de cette thèse est de concevoir un système de détection de chute basée sur une surveillance par caméra et d’étudier les aspects algorithmiques et architecturaux. Notre système se compose de quatre modules : la segmentation d’objet, le filtrage, l’extraction de caractéristiques et la reconnaissance qui permettent en plus de la détection de chute d’identifier leur type afin de définir un niveau d’alerte. En premier lieu, différents algorithmes ont été étudiés et comparés comme le Background Subtraction-Neural Network; le Background Subtraction-Template Matching (BGS-TM); le Background Subtraction-Hidden Markov Model ; et le Gaussian Mixture Model. Le BGS/TM présentant le meilleur taux de reconnaissance a alors été retenu. Une nouvelle base de donnée DTU-HBU a été construite et classifiée selon différentes actions : chute, non-chute (assis, couché, rampant, etc.) selon trois angles de caméra (face, côtés et de biais). Le second objectif fut de définir une méthode de conception permettant de sélectionner les architectures présentant la meilleure performance. Un premier travail fut de définir des modèles de la consommation et du temps d’exécution pour différentes cibles (processeur, FPGA). A titre d’exemple, la plateforme ZYNQ a été considérée. Les modèles proposés présentent un taux erreur inférieur à 3,5%. Une méthodologie de conception DSE basée sur deux techniques de parallélisme (Intra-task et inter-task) et couplant le taux de reconnaissance (ACC) a été définie. Les résultats obtenus montrent que l’ACC atteint 98,3% pour une énergie de 29,5 mJ/f. / Nowadays, fall detection is a major challenge in the public health care domain, especially for the elderly living alone and rehabilitants in hospitals. This thesis presents an exploration for a Fall Detection System based on camera under an algorithmic and architectural point of view. Our system includes four modules: Object Segmentation, Filter, Feature Extraction and Recognition and give an urgent alarm for detecting different kinds of fall. Firstly, different algorithms for the Fall Detection System are proposed and compared the efficiency among Background Subtraction-Neural Network, Background Subtraction-Template Matching (BGS/TM), Background Subtraction-Hidden Markov Model, and Gaussian Mixture Model. Therefore, the selected BGS/TM with 91.67% (Recall), 100% (Precision) and 95.65% (Accuracy) will be implemented on ZYNQ platform. Moreover, a DUT-HBU database which is classified with different actions: fall, non-fall in three camera directions is used to evaluate the efficiency of this system. Secondly, the aim is to explore low cost architectures for this system, new power consumption and execution time models for processor core and FPGA are defined according to the different configurations of architecture and applications. The error rates of the proposed models don’t exceed 3.5%. The models are then extended to hardware/software architectures to explore low cost architecture by defining a suitable Design Space Exploration methodology. Two techniques for parallelization which are based on intra-task and inter-task static scheduling are applied with the aim to enhance the accuracy and the power consumption of this system reaches 98.3% with energy per frame of 29.5mJ/f.

In the world that we already live people are more and more on the run and population ageing significantly raise, new technologies are trying to bring best they can to meet humans’ expectations. Survey’s results, that was done during technology conference with elderly on Blekinge Institute of Technology showed, that no one of them has any kind of help in their home but they would need it. This Master thesis present human health state monitoring to focus on fall detection. Health care systems will not completely stop cases when humans are falling down, but further studying causes can prevent them.In this thesis, integration of sensors for vital parameters measurements, human position and measured data evaluation are presented. This thesis is based on specific technologies compatible with Arduino Uno and Arduino Mega microcontrollers, measure sensors and data exchange between data base, MATLAB/Simulink and web page. Sensors integrated in one common system bring possibility to examine the patient health state and call aid assistance in case of health decline or serious injury risk.System efficiency was based on many series of measurement. First phase a comparison between different filter was carried out to choose one with best performance. Kalman filtering and trim parameter for accelerometer was used to gain satisfying results and the final human fall detection algorithm. Acquired measurement and data evaluation showed that Kalmar filtering allow to reach high performance and give the most reliable results. In the second phase sensor placement was tested. Collected data showed that human fall detection is correctly recognized by system with high accuracy. Designed system as a result allow to measure human health and vital state like: temperature, heartbeat, position and activity. Additionally, system gives online overview possibility with actual health state, historical data and IP camera preview when alarm was raised after bad health condition.

Aging population is a one of the key problems for the vast majority of so called "more economically developed countries" (MEDC). The amount of elderly people who suffer from multiple disease and require permanent monitoring of their vital parameters has increased recently resulting in extra healthcare costs. Modern healthcare systems exploited in geriatric medicine are often obtrusive and require patients presence at the hospital which interferes with their demand in independent life style. Recent developments on telecare market provide a wide range of wireless solutions for distant monitoring of medical parameters and health assistance. However, most of the devices are programmed for spot checking and operate independently from each other. There is still a lack of integrated framework with high interoperability and on-line continuous monitoring support for further correlation analyses. The current study is a step towards complete and continuous data collection system for elderly people with various types of health problems. Research initiative is motivated by recent demand in reliable multi-functional remote monitoring systems, combining different data sources. The main focus is made on fall detection methods, interoperability, real-life testing and correlation analyses. The list of main contributions contains (1) investigating communication functionalities, (2) developing algorithm for reliable fall detection, (3) multi-sensor fusion analyses and overview of the latest multi-sensor fusion approaches, (4) user study involving healthy volunteers and elderly people. Evaluation is performed through a series of computer simulation and real-life testing in collaboration with the local medical authorities. As a result we expect to obtain a monitoring system with reliable communication capabilities, inbuilt on-line processing, alarm generating techniques and complete functionality for integration with similar systems or smart-home environment. / En åldrande befolkning utgör ett av de viktigaste problemen för de allra flesta så kallade "mer ekonomiskt utvecklade länder" (MEDC). Mängden äldre människor som lider av multi-sjukdomar och kräver ständig övervakning av vitala parametrar har ökat på senare tid, vilket resulterar i ökade sjukvårdskostnader. Geriatrikens moderna sjukvårdssystem kräver ofta att patienterna är närvarande på sjukhuset, vilket kraftigt begränsar en självständig och oberoende livsstil. Den senaste utvecklingen på telemedicinområdet erbjuder ett brett utbud av trådlösa lösningar inom hälsovård för distansövervakning av medicinska parametrar. De flesta lösningarna innebär punktkontroll av enskilda parametrar och arbetar oberoende av varandra. Det saknas fortfarande integrerade lösningar med hög interoperabilitet och kontinuerlig on-line övervakningsstöd för att kunna genomföra ytterligare korrelationsanalyser. Detta arbete utgör ett steg mot ett fullständigt och kontinuerligt datainsamlingssystem för äldre personer med olika typer av hälsoproblem. Forskningsinitiativet motiveras av senaste tidens efterfrågan på tillförlitliga multifunktionella system för distansövervakning, som kombinerar olika datakällor. Huvudfokus utgörs av falldetektionsmetoder, interoperabilitet, verkliga tester och korrelationsanalyser. Listan över de främsta bidragen innehåller (1) att undersöka kommunikationsfunktionaliteter, (2) utveckla en algoritm för tillförlitlig falldetektion, (3) multisensor-fusion-analyser och översikt över multisensor-fusion-strategier, (4) en användarstudie med friska frivilliga äldre. Utvärderingen sker genom en serie av datorsimuleringar och tester i verklig miljö i samarbete med lokala hälso- och sjukvårdsmyndigheter. Målet är ett övervakningssystem med tillförlitliga kommunikationsmöjligheter, inbyggd on-line-bearbetning, tekniker för larmgenerering och funktionalitet för integration med liknande system eller i en smart hemmiljö.