System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls

This article presents the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls

Analysis of a hybrid Android system for fall detection

Android personal devices have become an interesting and cost-effective technology to deploy wearable Fall Detection Systems. In contrast with other smartphone-based solutions, this paper describes a fall detection architecture that integrates two-Bluetooth enabled devices: a smartwatch and a smartphone. The evaluation of the system under different fall recognition algorithms and mobility patterns indicates that the simultaneous operation of the two devices as fall detectors clearly improves the specificity of the system when compared to the cases where just one device is employed as a fall detector. The performed analysis also encompasses the study of the battery consumption and the performance of the system under constant monitoring in everyday life conditions.Universidad de Malaga, Campus de Excelencia Internacional Andalucia Tech. This work was supported by European FEDER funds and the Spanish Ministry of Economy and Competitiveness [grant TEC2013-42711-R] (URL: http://www.mineco.gob.es/portal/site/mineco/

ELMOS “Elderly Health Monitor System” as An Android Smartphone-Based Elderly Health Monitor Service

Elderly Health Monitor system named ELMOS as an android smartphone-based elderly health monitor service is essential in preventing elderly health problems, as well as providing a framework or basis for maintaining health awareness. This device comes with three functions which are sensing body temperature, heart rate and fall detection using Arduino. DS18B20 is used for the sense of body temperature. Body temperature could be a basic parameter for monitoring and identification human health. Heartbeat sensor was used for sensing heart rate. Accelerometer MPU 6050 was used to detect a senior citizen falling in real- time and to use the bluetooth communication to notify the administrator of such an event. As a result, we found that the system can be used to measure physiological parameters, such as body temperature, heart rate and fall detection

Personalized fall detection monitoring system based on learning from the user movements

Personalized fall detection system is shown to provide added and more benefits compare to the current fall detection system. The personalized model can also be applied to anything where one class of data is hard to gather. The results show that adapting to the user needs, improve the overall accuracy of the system. Future work includes detection of the smartphone on the user so that the user can place the system anywhere on the body and make sure it detects. Even though the accuracy is not 100% the proof of concept of personalization can be used to achieve greater accuracy. The concept of personalization used in this paper can also be extended to other research in the medical field or where data is hard to come by for a particular class. More research into the feature extraction and feature selection module should be investigated. For the feature selection module, more research into selecting features based on one class data

Improving the Performance of Fall Detection Systems through Walk Recognition

Social problems associated with falls of elderly citizens are becoming increasingly important because of the continuous growth of aging population. Automatic fall detection systems represent a possible answer to some of these problems, as they are useful to obtain help in case of serious injuries and to reduce the long-lie problem. Nevertheless, widespread adoption of these systems is strongly influenced by their usability and trustworthiness, which are at the moment not excellent. In fact, the user is forced to wear the device according to placement and orientation restrictions that depend on the considered fall-recognition technique. Also, the number of false alarms generated is too high to be acceptable in real world scenarios. This paper presents a technique, based on walk recognition, that increases significantly both usability and trustworthiness of a smartphone-based fall detection system. In particular, the proposed technique automatically and dynamically determines the orientation of the device, thus relieving the user from the burden of wearing the device with predefined orientation. Orientation is then used to infer posture and eliminate a large fraction of false alarms (98 %)

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

Early warning smartphone diagnostics for water security and analysis using real-time pH mapping

© 2015, The Author(s). Early detection of environmental disruption, unintentional or otherwise, is increasingly desired to ensure hazard minimization in many settings. Here, using a field-portable, smartphone fluorimeter to assess water quality based on the pH response of a designer probe, a map of pH of public tap water sites has been obtained. A custom designed Android application digitally processed and mapped the results utilizing the global positioning system (GPS) service of the smartphone. The map generated indicates no disruption in pH for all sites measured, and all the data are assessed to fall inside the upper limit of local government regulations, consistent with authority reported measurements. This implementation demonstrates a new security concept: network environmental forensics utilizing the potential of novel smartgrid analysis with wireless sensors for the detection of potential disruption to water quality at any point in the city. This concept is applicable across all smartgrid strategies within the next generation of the Internet of Things and can be extended on national and global scales to address a range of target analytes, both chemical and biological