Comparison and Characterization of Android-Based Fall Detection Systems

Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones’ potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.Ministerio de Economía y Competitividad TEC2009-13763-C02-0

Analysis of Android Device-Based Solutions for Fall Detection

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.Ministerio de Economía y Competitividad TEC2013-42711-

M-health review: joining up healthcare in a wireless world

In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint

An ambient assisted living solution for mobile environments

An Ambient Assisted Living (AAL) mobile health application solution with biofeedback based on body sensors is very useful to perform a data collection for diagnosis in patients whose clinical conditions are not favourable. This system allows comfort, mobility, and efficiency in all the process of data collection providing more confidence and operability. A physical fall may be considered something natural in the life span of a human being from birth to death. In a perfect scenario it would be possible to predict when a fall will occur in order to avoid it. Falls represent a high risk for senior people health. Those falls can cause fractures or injuries causing great dependence and debilitation to the elderly and even death in extreme cases. Falls can be detected by the accelerometer included in most of the available mobile phones or portable digital assistants (PDAs). To reverse this tendency, it can be obtained more accurate data for patients monitoring from the body sensors attached to the human body (such as, electrocardiogram (ECG), electromyography (EMG), blood volume pulse (BVP), electro dermal activity (EDA), and galvanic skin response (GSR)). Then, this dissertation reviews the related literature on this topic and introduces a mobile solution for falls prevention, detection, and biofeedback monitoring. The proposed system collects sensed data that is sent to a smartphone or tablet through Bluetooth. Mobile devices are used to process and display information graphically to users. The falls prevention system uses collected data from sensors in order to control and advice the patient or even to give instructions to treat an abnormal condition to reduce the falls risk. In cases of symptoms that last more time it can even detect a possible disease. The signal processing algorithms plays a key role in the fall prevention system. These algorithms in real time, through the capture of biofeedback data, are needed to extract relevant information from the signals detected to warn the patient. Monitoring and processing data from sensors is realized by a smartphone or tablet that will send warnings to users. All the process is performed in real time. These mobile devices are also used as a gateway to send the collected data to a Web service, which subsequently allows data storage and consultation. The proposed system is evaluated, demonstrated, and validated through a prototype and it is ready for use

Smart system for children's chronic illness monitoring

[EN] Sick children need a continuous monitoring, but this involves high costs for the government and for the parents. The use of information and communication technologies (ICT) jointly with artificial intelligence and smart devices can reduce these costs, help the children and assist their parents. This paper presents a smart architecture for children's chronic illness monitoring that will let the caregivers (parents, teachers and doctors) to remotely monitor the health of the children based on the sensors embedded in the smartphones and smart wearable devices. The proposed architecture includes a smart algorithm developed to intelligently detect if a parameter has exceeded a threshold, thus it may imply an emergency or not. To check the correct operation of this system, we have developed a small wearable device that is able to measure the heart rate and the body temperature. We have designed a secure mechanism to stablish a Bluetooth connection with the smartphone. In addition, the system is able to perform the data fusion in both the information packetizing process, which contributes to improve the protocol performance, and in the measured values combination, where it is used a stochastic approach. As a result, our system can fusion data from different sensors in real-time and detect automatically strange situations for sending a warning to the caregivers. Finally, the consumed bandwidth and battery autonomy of the developed device have been measured.This work has been partially supported by the "Ministerio de EducaciOn, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Sendra, S.; Parra-Boronat, L.; Lloret, J.; Tomás Gironés, J. (2018). Smart system for children's chronic illness monitoring. Information Fusion. 40:76-86. https://doi.org/10.1016/j.inffus.2017.06.002S76864

System for monitoring and supporting the treatment of sleep apnea using IoT and big data

[EN] Sleep apnea has become in the sleep disorder that causes greater concern in recent years due to its morbidity and mortality, higher medical care costs and poor people quality of life. Some proposals have addressed sleep apnea disease in elderly people, but they have still some technical limitations. For these reasons, this paper presents an innovative system based on fog and cloud computing technologies which in combination with IoT and big data platforms offers new opportunities to build novel and innovative services for supporting the sleep apnea and to overcome the current limitations. Particularly, the system is built on several low-power wireless networks with heterogeneous smart devices (i.e, sensors and actuators). In the fog, an edge node (Smart IoT Gateway) provides IoT connection and interoperability and pre-processing IoT data to detect events in real-time that might endanger the elderly's health and to act accordingly. In the cloud, a Generic Enabler Context Broker manages, stores and injects data into the big data analyzer for further processing and analyzing. The system's performance and subjective applicability are evaluated using over 30 GB size datasets and a questionnaire fulfilled by medicals specialist, respectively. Results show that the system data analytics improve the health professionals' decision making to monitor and guide sleep apnea treatment, as well as improving elderly people's quality of life. (C) 2018 Elsevier B.V. All rights reserved.This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's "Horizon 2020'' research and innovation program as part of the ACTIVAGE project under Grant 732679 and the Interoperability of Heterogeneous IoT Platforms project (INTER-IoT) under Grant 687283.Yacchirema-Vargas, DC.; Sarabia-Jácome, DF.; Palau Salvador, CE.; Esteve Domingo, M. (2018). System for monitoring and supporting the treatment of sleep apnea using IoT and big data. Pervasive and Mobile Computing. 50:25-40. https://doi.org/10.1016/j.pmcj.2018.07.007S25405

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

ZigBee Based Control System for People with Multiple Disabilities: A Review

Most of the existing smart home monitoring and control systems do not accommodate special needy users to manage their home appliances. A wireless sensor network based system for smart home automation will be designed, built and tested to address such missing functionality. The to be implemented system’s major contribution is that it is customized to provide the special need residents with tools and services to monitor and operate home appliances remotely. The implemented system provides home residents with disabilities to take advantage of the advancement in technology. It enables them to perform their daily activities by remotely monitoring and controlling their home appliances without having to depend on others. The system will be programmed so that it can be configured to adjust to the customer’s disability providing them with better and convenient lifestyle. It is worth mentioning that the system is scalable and can be extended to include more and different services and tools. The system is portable, compact, affordable and easy to use