6,307 research outputs found
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
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 EconomĂa y Competitividad TEC2013-42711-
A Study and Estimation a Lost Person Behavior in Crowded Areas Using Accelerometer Data from Smartphones
As smartphones become more popular, applications are being developed with new and innovative ways to solve problems in the day-to-day lives of users. One area of smartphone technology that has been developed in recent years is human activity recognition (HAR). This technology uses various sensors that are built into the smartphone to sense a person\u27s activity in real time. Applications that incorporate HAR can be used to track a person\u27s movements and are very useful in areas such as health care. We use this type of motion sensing technology, specifically, using data collected from the accelerometer sensor. The purpose of this study is to study and estimate the person who may become lost in a crowded area. The application is capable of estimating the movements of people in a crowded area, and whether or not the person is lost in a crowded area based on his/her movements as detected by the smartphone. This will be a great benefit to anyone interested in crowd management strategies. In this paper, we review related literature and research that has given us the basis for our own research. We also detail research on lost person behavior. We looked at the typical movements a person will likely make when he/she is lost and used these movements to indicate lost person behavior. We then evaluate and describe the creation of the application, all of its components, and the testing process
An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform
With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations
PlaceRaider: Virtual Theft in Physical Spaces with Smartphones
As smartphones become more pervasive, they are increasingly targeted by
malware. At the same time, each new generation of smartphone features
increasingly powerful onboard sensor suites. A new strain of sensor malware has
been developing that leverages these sensors to steal information from the
physical environment (e.g., researchers have recently demonstrated how malware
can listen for spoken credit card numbers through the microphone, or feel
keystroke vibrations using the accelerometer). Yet the possibilities of what
malware can see through a camera have been understudied. This paper introduces
a novel visual malware called PlaceRaider, which allows remote attackers to
engage in remote reconnaissance and what we call virtual theft. Through
completely opportunistic use of the camera on the phone and other sensors,
PlaceRaider constructs rich, three dimensional models of indoor environments.
Remote burglars can thus download the physical space, study the environment
carefully, and steal virtual objects from the environment (such as financial
documents, information on computer monitors, and personally identifiable
information). Through two human subject studies we demonstrate the
effectiveness of using mobile devices as powerful surveillance and virtual
theft platforms, and we suggest several possible defenses against visual
malware
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 EconomĂa y Competitividad TEC2009-13763-C02-0
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