Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000

VCare: A Personal Emergency Response System to Promote Safe and Independent Living Among Elders Staying by Themselves in Community or Residential Settings

‘Population aging’ is a growing concern for most of us living in the twenty first century, primarily because many of us in the next few years will have a senior person to care for - spending money towards their healthcare expenditures AND/OR having to balance a full-time job with the responsibility of care-giving, travelling from another city to be with this elderly citizen who might be our parent, grand-parent or even community elders. As informal care-givers, if somehow we were able to monitor the day-to-day activities of our elderly dependents, and be alerted when wrong happens to them that would be of great help and lower the care-giving burden considerably. Information and Communication Technology (ICT) can certainly help in such a scenario, with tools and techniques that ensure safe living for the individual we are caring for, and save us from a lot of worry by providing us with anytime access into their lives or activities, and as a result check their functional state. However, we should be mindful of the tactics that could be adopted by harm causers to steal data stored in these products and try to curb the associated service costs. In short, we are in need of robust, cost-effective, useful, and secure solutions to help elders in our society to ‘age gracefully’. This work is a little step taken towards that direction. ‘Population aging’ is a growing concern for most of us living in the twenty first century, primarily because many of us in the next few years will have a senior person to care for - spending money towards their healthcare expenditures AND/OR having to balance a full-time job with the responsibility of care-giving, travelling from another city to be with this elderly citizen who might be our parent, grand-parent or even community elders. As informal care-givers, if somehow we were able to monitor the day-to-day activities of our elderly dependents, and be alerted when wrong happens to them that would be of great help and lower the care-giving burden considerably. Information and Communication Technology (ICT) can certainly help in such a scenario, with tools and techniques that ensure safe living for the individual we are caring for, and save us from a lot of worry by providing us with anytime access into their lives or activities, and as a result check their functional state. However, we should be mindful of the tactics that could be adopted by harm causers to steal data stored in these products and try to curb the associated service costs. In short, we are in need of robust, cost-effective, useful, and secure solutions to help elders in our society to ‘age gracefully’. This work is a little step taken towards that direction. Advisor: Tadeusz Wysock

The OCarePlatform : a context-aware system to support independent living

Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

Elderly Fall Detection Systems: A Literature Survey

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial

Remote health monitoring system for the elderly based on mobile computing and IoT

This document presents the work done in the Master’s thesis in Telecommunications and Computer Engineering and describes the development, implementation and subsequent of a Remote Health Monitoring System for the Elderly based on Mobile Computing and IoT. Due to increasing technological innovation over the last decades, the average life expectancy of humans is increasing year-by-year. Although this is an excellent step forward for humanity, it has led older population to being more prone to illness and accidents such as falls. In this work a study is made on the existing literature in nonintrusive remote health monitoring systems, towards the design and implementation of an IoT system capable of identifying falls and monitor cardiac data. A Systematic Literature Review (SLR) method was considered, taking into account the existing literature on remote health monitoring systems, fall detection algorithms and IoT. The Design Science Research (DSR) methodology was used to seek to enhance technology and science knowledge about this dissertation’s topic, through the creation of an innovative artifact. The system includes a smart watch (LILYGO T-WATCH-2020-V2), programmable in C under Arduino IDE to detect falls and a photoplethysmography monitoring unit (PPG) based on a Onyx 9560 Bluetooth oximeter, capable of measuring the user’s blood oxygen percentage (SpO2) and heart rate, in real time. It also provides remote monitoring through a user-friendly website to visualize live data about the health status of the user. The system was tested in volunteers to show the effectiveness of remote health monitoring systems for the elderly population.Este documento apresenta o trabalho realizado na tese de Mestrado em Engenharia de Telecomunicações e Informática e descreve o desenvolvimento, implementação e validação de um Sistema de Monitorização Remota da Saúde para Idosos. Devido à crescente inovação tecnológica ao longo dos anos, a esperança média de vida dos seres humanos está a aumentar anualmente. Embora seja um excelente passo em frente para a humanidade, tem levado à população mais idosa a ser propensa a doenças e acidentes, tais como quedas. Neste trabalho, efectua-se um estudo sobre a literatura existente em sistemas não intrusivos de monitorização remota da saúde, com vista à concepção e implementação de um sistema IoT capaz de identificar quedas e monitorizar dados cardíacos. Foi concebida uma Revisão Sistemática da Literatura (SLR), tendo em conta literatura existente sobre sistemas de monitorização da saúde, algoritmos de detecção de quedas e IoT. A metodologia Design Science Research (DSR) foi utilizada para procurar melhorar os conhecimentos tecnológicos sobre o tema desta dissertação, através da criação de um artefacto inovador. O sistema inclui um relógio inteligente (LILYGO T-WATCH-2020-V2), programável em C sob a IDE Arduino para detectar quedas e um dispositivo de monitorização fotopletismográfico (PPG) baseada num oxímetro Onyx 9560 Bluetooth, capaz de medir a percentagem de oxigénio no sangue (SpO2) e o ritmo cardíaco. Fornece ainda monitorização remota através de um website para visualizar dados em direto sobre a saúde do utilizador. O sistema foi testado em voluntários para mostrar a eficácia dos sistemas de monitorização remota da saúde em idosos