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-

THaW publications

In 2013, the National Science Foundation\u27s Secure and Trustworthy Cyberspace program awarded a Frontier grant to a consortium of four institutions, led by Dartmouth College, to enable trustworthy cybersystems for health and wellness. As of this writing, the Trustworthy Health and Wellness (THaW) project\u27s bibliography includes more than 130 significant publications produced with support from the THaW grant; these publications document the progress made on many fronts by the THaW research team. The collection includes dissertations, theses, journal papers, conference papers, workshop contributions and more. The bibliography is organized as a Zotero library, which provides ready access to citation materials and abstracts and associates each work with a URL where it may be found, cluster (category), several content tags, and a brief annotation summarizing the work\u27s contribution. For more information about THaW, visit thaw.org

Evolution of Indoor Positioning Technologies: A Survey

Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee

Find Me A Safe Zone:A Countermeasure for Channel State Information Based Attacks

Recently, channel state information (CSI) is shown to be an effective side-channel to perform attacks in public environments. Prior work has demonstrated that by analyzing how the CSI measurements of the wireless signal are affected by the mobile user's finger movements or gestures, an attacker can recover the user's input with a high success rate. Furthermore, the setup of this new attack is trivial, where the adversary only needs to place one or two malicious wireless devices near the target user. It would be difficult for many users to identify the nearby malicious devices while they want to continue to use mobile applications in public places. This dilemma makes protection of CSI-based attacks an urgent need. This article presents the first countermeasure for CSI-based attacks. Our key insight is that the success of any CSI-based attack requires high-quality CSI measurements; and we can significantly reduce the risk of information leakage by directing the user to a nearby location where the CSI readings are inherently noisy. To this end, we develop a regression based method to assess the risk of CSI-based attacks and exploit a well-established localization technique to identify potential malicious wireless devices. We then use this information to guide the user to a safe zone. We evaluate our approach by applying it to protect pattern lock and keystrokes in various indoor and outdoor environments. Experimental results show that our approach can effectively protect mobile users against CSI-based attacks

Smartphone Loss Prevention System Using BLE and GPS Technology

Being an all-in-one gadget, smartphones play a vital role in our everyday lives. However, millions of people suffer every year by losing their phones. A lost phone creates a huge security threat and data loss possibility to the users. Some preventive measures are available to protect from unauthorized access. Moreover, there are some post-loss solutions to track down, retrieve data from a lost locked phone, and protect the privacy and security of lost phone data, but those have some drawbacks as well. Considering the situation, our proposed system offers a preventive solution which will protect the smartphones from getting lost. Our system involves a smartwatch which will be connected to smartphone via Bluetooth Low Energy (BLE) and keep track of the distance between the smartphone and the smartwatch worn by user in real-time. The system will be able to identify if the distance goes beyond 20 feet or a customizable distance given by the user and immediately raise an alert in the smartwatch, creating vibration and sound in public places. The system allows users to mark their safe location (e.g., house, office) and radius where their smartphone will be safe, and they don’t need alerts. We have developed 3 different models to implement this system with different approaches using Ranging, Haversine formula and Geofencing. For our work, we aim to perceive how accurate our models are in terms of calculating distance and safe location tracking as well as alert response time and models impact on battery life of both smartphone and smartwatch. We have developed an Android application and a smartwatch application that run on both smartwatch and smartphone for each model and compared their performances based on our evaluation parameters. We conducted experiments under various real-world conditions and the system incorporated with Model 1 can generate alert with 96% accuracy when user is away from the smartphone beyond the threshold distance in an unsafe location. This affordable solution will ensure prevention from smartphones getting lost in public places in an effective way securing confidentiality and data protection to users