14,876 research outputs found
Estimating Walking Distance with a Smart Phone
Abstract-A huge body of work utilized signal strength of short range signal (such as WiFi, Bluetooth, ultra sound or Infrared) to build a radio map for indoor localization, by deploying a great number of beacon nodes in the building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system is costly and labor-intensive. To overcome that, some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization. The PDR system does not require to build a beacon-based infrastructure, in which a small number of sensors are put on the pedestrian. These sensors (such as G-sensor and Gyro) are used to estimate the distance and direction that the user traveled. The PDR approach can be generally categorized into two types: footmounted and waist-mounted. In general, the foot-mounted system can get accurate step length, but perform poorly in estimated heading direction. On the other hand, the waistmounted system can estimate direction with high accuracy, but is hard to measure the step length. In this work, we proposed a waist-mounted based PDR using one 3-axis accelerometer and one gyroscope sensor. We utilize vertical acceleration to implement double integral for measuring the user's instant height change and use some physical features of vertical acceleration during the walking to calibrate the measurement. Then based on the Pythagoras' Theorem, we can estimate each step length based on the user's height change during his/her walking. Our experiment results show that the accuracy of placing smartphone on the waist is about 97.35% and placing smart-phone on the chest pocket is about 96.14% in estimating the user's walking distance
Step Detection Algorithm For Accurate Distance Estimation Using Dynamic Step Length
In this paper, a new Smartphone sensor based algorithm is proposed to detect
accurate distance estimation. The algorithm consists of two phases, the first
phase is for detecting the peaks from the Smartphone accelerometer sensor. The
other one is for detecting the step length which varies from step to step. The
proposed algorithm is tested and implemented in real environment and it showed
promising results. Unlike the conventional approaches, the error of the
proposed algorithm is fixed and is not affected by the long distance.
Keywords distance estimation, peaks, step length, accelerometer.Comment: this paper contains of 5 pages and 6 figure
SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone
The number of pedestrian accidents continues to keep climbing. Distraction
from smartphone is one of the biggest causes for pedestrian fatalities. In this
paper, we develop SaferCross, a mobile system based on the embedded sensors of
smartphone to improve pedestrian safety by preventing distraction from
smartphone. SaferCross adopts a holistic approach by identifying and developing
essential system components that are missing in existing systems and
integrating the system components into a "fully-functioning" mobile system for
pedestrian safety. Specifically, we create algorithms for improving the
accuracy and energy efficiency of pedestrian positioning, effectiveness of
phone activity detection, and real-time risk assessment. We demonstrate that
SaferCross, through systematic integration of the developed algorithms,
performs situation awareness effectively and provides a timely warning to the
pedestrian based on the information obtained from smartphone sensors and Direct
Wi-Fi-based peer-to-peer communication with approaching cars. Extensive
experiments are conducted in a department parking lot for both component-level
and integrated testing. The results demonstrate that the energy efficiency and
positioning accuracy of SaferCross are improved by 52% and 72% on average
compared with existing solutions with missing support for positioning accuracy
and energy efficiency, and the phone-viewing event detection accuracy is over
90%. The integrated test results show that SaferCross alerts the pedestrian
timely with an average error of 1.6sec in comparison with the ground truth
data, which can be easily compensated by configuring the system to fire an
alert message a couple of seconds early.Comment: Published in IEEE Access, 202
Map++: A Crowd-sensing System for Automatic Map Semantics Identification
Digital maps have become a part of our daily life with a number of commercial
and free map services. These services have still a huge potential for
enhancement with rich semantic information to support a large class of mapping
applications. In this paper, we present Map++, a system that leverages standard
cell-phone sensors in a crowdsensing approach to automatically enrich digital
maps with different road semantics like tunnels, bumps, bridges, footbridges,
crosswalks, road capacity, among others. Our analysis shows that cell-phones
sensors with humans in vehicles or walking get affected by the different road
features, which can be mined to extend the features of both free and commercial
mapping services. We present the design and implementation of Map++ and
evaluate it in a large city. Our evaluation shows that we can detect the
different semantics accurately with at most 3% false positive rate and 6% false
negative rate for both vehicle and pedestrian-based features. Moreover, we show
that Map++ has a small energy footprint on the cell-phones, highlighting its
promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on
Sensing, Communication, and Networking (IEEE SECON 2014
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
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