Technology assisted risk assessment in homecare

The home is a key environment for geriatric care. Research has demonstrated patients discharged to their homes are at a high risk of readmission of transitioning to homecare. The ability for care providers to predict and mitigate risk is limited in this environment. Significant observational requirements hinder continued diagnosis which limits timely interventions before a readmission event. Technology is used to resolve this issue and provide enhanced risk assessments by gathering data, however these technologies present a wide variety of challenges including barriers to use and ethical considerations which hinder an effective solution. This paper seeks to examine the necessary observations homecare providers require to form effective risk assessments and interventions in the home. This is achieved by remote monitoring through Bluetooth low energy (BLE) sensors for the common causes of hospital readmissions including observed difficulties in activities of daily living (ADL), heart rate fluctuations and falls. Risk is assessed by examining these factors as events to deduce behaviour or apparent reduction in capacity to function in daily life. Results obtained when using BLE sensors, heart rate monitors and fall detectors show it is possible to observe and record events of interest to health care providers in the provision of geriatric homecare. Patterns within sensor data could be used in the home environment to form an effective patient risk analysis given remote monitoring access to a patient and prescribed care plan to evaluate outcomes and possibility of readmission. Further experiments will test and validate the risk assessment analysis formed in this paper

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results.Universidad de Malaga Plan Propio-Universidad de MalagaSpanish GovernmentEuropean Commission RTI2018-098371-B-I0

Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications

A Vision-based approach to fall detection for elderly patients receiving home-based care

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityFalls present one of the unintentional accidents for people in the world. The adverse effects of a fall vary with the nature of the fall and the impact with the ground or object. Essentially, falls rarely occur in the daily activities of healthy individuals. The occurrence results in fatal or non-fatal falls. However, the falls are consequential for the elderly people since they result in future related problems or death. As such, elderly patients require additional attention in the case of fall events. Therefore, to mitigate the effect of a fall on an elderly patient, there must be the provision of a fast response mechanism. Response time to medical emergencies plays a key role in patient survival and recovery. As such, medical personnel strive to reduce the response time. Proper and immediate notification of an emergency aids in reducing the response time. In order to substantially reduce the negative effect of the fall or increase the survival chances, patients ought to receive fast medical response. Therefore, the need of a fast and proper notification method that aims at providing relevant information in regards to the nature of emergency of the patient. As such, proper monitoring leads to a reduced response time. Arguably, elderly patients require urgent medical care in case of a fall. This research work proposes a multi-person fall detection system, which implements a vision-based approach for fall detection leveraging on region-based convolution neural network. A fixed camera serves as the input device to capture images of people. The system analyses the image to identify the posture and orientation of the people present in the image. Based on the provided image, the system then classifies the occurrence as a fall or non-fall using the developed model. If it identifies a fall, an alert is then sent to a concerned party. The system achieves a mean average precision of 0.8 in fall detection. Further, the system detects a fall in an image in 3.8 seconds thus improving the response time of the medical personnel to aid in curbing the negative effects of a fall on a patient

Advances in Deep Learning Towards Fire Emergency Application : Novel Architectures, Techniques and Applications of Neural Networks

Paper IV is not published yet.With respect to copyright paper IV and paper VI was excluded from the dissertation.Deep Learning has been successfully used in various applications, and recently, there has been an increasing interest in applying deep learning in emergency management. However, there are still many significant challenges that limit the use of deep learning in the latter application domain. In this thesis, we address some of these challenges and propose novel deep learning methods and architectures. The challenges we address fall in these three areas of emergency management: Detection of the emergency (fire), Analysis of the situation without human intervention and finally Evacuation Planning. In this thesis, we have used computer vision tasks of image classification and semantic segmentation, as well as sound recognition, for detection and analysis. For evacuation planning, we have used deep reinforcement learning.publishedVersio

Automated human fall recognition from visual data

Falls are one of the greatest risks for the older adults living alone at home. This research presents a novel visual-based fall detection approach to support independent living for older adults in an indoor environment. The aim of the research was to investigate appropriate methods for detecting falls through analysing the motion and shape of the human body. Several techniques for automatically detecting falls have been proposed. The existing technologies can be classified into three main groups of fall detectors, namely: ambient device-based, wearable sensor-based and computer vision-based techniques. Ambient device-based techniques use vibration or pressure sensors to capture the sound and vibration for detecting the presence and position of a person. Although these devices are inexpensive and do not disturb the user, the detection rate is rather low and many false alarms are generated. Wearable devices use different sensors such as accelerometer and gyroscopes to capture the human body movement information and detect falls. However, older adults often forget to wear them. Wearable sensors are also known to be too invasive as they require wearing and carrying various uncomfortable devices. Much work has been undertaken to investigate the use of visual-based sensors for fall detection using single, multiple, and omnidirectional cameras. The proposed research reported in this thesis uses a single camera to detect a moving object using a background subtraction algorithm. The next step is to extract robust features which describe the change in human shape and to discriminate falls from other activities like lying and sitting. These features are based on motion, change in the human shape feature, projection histogram features and temporal change of head position. Features extracted from the human silhouette are finally fed into various machine learning classifiers for fall detection evaluation. The ability to distinguish a fall action depends mainly on the quality of the classifier inputs, therefore, the features of the extracted human silhouette play a key role in the effectiveness and robustness of detecting human falls. In this research, the timed Motion History Image (tMHI) method is applied for motion segmentation. In addition, the motion information was combined with other features extracted from the fitted ellipse around the human body to discriminate actual fall from other activities. Fall detection methods can be divided into two main categories; thresh- old based methods and machine learning-based methods. This research presents threshold-based methods to distinguish between Activities of Daily Living (ADL) and falls. Fall events can be detected if the measured features values higher than pre-determined threshold values. Results show that falls can be distinguished from ADL with an accuracy of 99:82%, using our recording dataset. In addition, various machine learning methods were compared to evaluate their abilities to accurately detecting falls. Experimental results show efficiency and reliability of the proposed fall detection approach with high fall detection rate of 99:60% and low false alarm 2:62% tested with UR Fall Detection dataset. Additionally, A set of experiments have been conducted using our recording dataset, the results indicate that the proposed approach achieves high fall detection rate 99:94% and low false alarm 0:02%

Feature Papers "Age-Friendly Cities & Communities: State of the Art and Future Perspectives"

The "Age-Friendly Cities & Communities: States of the Art and Future Perspectives" publication presents contemporary, innovative, and insightful narratives, debates, and frameworks based on an international collection of papers from scholars spanning the fields of gerontology, social sciences, architecture, computer science, and gerontechnology. This extensive collection of papers aims to move the narrative and debates forward in this interdisciplinary field of age-friendly cities and communities

Highly Portable, Sensor-Based System for Human Fall Monitoring

Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user’s body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system

A Conceptual Model using Ambient Assisted Living to Provide a Home Proactive Monitoring System for Elderly People in the Kingdom of Saudi Arabia

The growth in the ageing population is rapidly increasing and their care cost will be a challenging issue in the future. The number of elderly people worldwide (defined as those aged 60 years and older) was 202 million in 1950; this number has since quadrupled to reach 901 million and is expected to triple again by 2100. In particular, the number of elderly people in the Kingdom of Saudi Arabia (KSA) is increasing rapidly, from 5% of the total population in 2015 to a forecasted 20.9% by 2050. Clearly, the cost of taking care of elderly people is already a challenge, but it will be very difficult to meet in the future, when it will lead to a much higher expenditure on healthcare facilities. Furthermore, although elderly people are vulnerable to a decline in their health, they do not wish to live as they did in the 1970s to 1990s. Instead, their desire is to live independently in their own homes and continue to practice normal activities. In fact, Saudi culture is changing, and the children tend not to live with their parents as they used to. However, the literature review indicates that there is a lack of professionally designed systems that can fulfil the growing needs or requirements of elderly people in the KSA. These demographic changes raise a number of challenges related to the elderly people’s quality of life, including health, autonomy, care, social communication, and the utilisation of institutional services. These challenges require novel approaches to provide dependable self-adapting technological innovations. The era of Information and Communication Technology (ICT) has changed the world of the ageing population. Ambient Assisted Living (AAL) aims to improve the quality of life of elderly people, and to provide them with technologies and services that support their daily activities, help them to live longer and remain independently at home. The aims and objectives of this research are to review Ambient Assisted Living Technology, to provide examples of relevant technologies and applications, and to examine attitudes and perceptions of elderly people towards using AAL technologies in the KSA. This research also explores the factors of AAL, identifying those that affect the adoption of these technologies in the KSA, by conducting a systematic review, and using quantitative and qualitative analyses. The questionnaire results showed that elderly Saudi Arabians are willing and intending to accept and use AAL technologies, and that there are many factors that influence their adoption and use of AAL technologies. This provides an insight for solutions to the provision of support for their independent living. Thus, we developed a conceptual model using AAL to provide a Home Proactive Monitoring System (AALHPMS) that supports the stakeholders in adopting AAL technologies. We envisage that the AALHPMS can fulfil the needs and requirements of elderly people, motivate healthcare providers to implement AAL technologies, and assist the Saudi Government to make suitable provision for issues associated with the ageing population. In addition, a knowledge-based-system was built using a rule-based system. Experiments using Smart watches were conducted to monitor the heart rates. Further experiments using ZigBee, Bluetooth beacons, and surveillance cameras technology were also undertaken for monitoring the movement of elderly persons at their home. A website was also developed to disseminate knowledge related to ageing population and AAL technology in Saudi Arabia