2,946 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
Green multimedia: informing people of their carbon footprint through two simple sensors
In this work we discuss a new, but highly relevant, topic to the multimedia community; systems to inform individuals of their carbon footprint, which could ultimately effect change in community carbon footprint-related activities. The reduction of carbon emissions is now an important policy driver of many governments, and one of the major areas of focus is in reducing the energy demand from the consumers i.e. all of us individually.
In terms of CO2 generated from energy consumption, there are three predominant factors, namely electricity usage, thermal related costs, and transport usage. Standard home electricity and heating sensors can be used to measure the former two aspects, and in this paper we evaluate a novel technique to estimate an individual's transport-related carbon emissions through the use of a simple wearable accelerometer.
We investigate how providing this novel estimation of transport-related carbon emissions through an interactive web site and mobile phone app engages a set of users in becoming more aware of their carbon emissions. Our evaluations involve a group of 6 users collecting 25 million accelerometer readings and 12.5 million power readings vs. a control group of 16 users collecting 29.7 million power readings
Reliable and robust detection of freezing of gait episodes with wearable electronic devices
A wearable wireless sensing system for assisting patients affected by Parkinson's disease is proposed. It uses integrated micro-electro-mechanical inertial sensors able to recognize the episodes of involuntary gait freezing. The system operates in real time and is designed for outdoor and indoor applications. Standard tests were performed on a noticeable number of patients and healthy persons and the algorithm demonstrated its reliability and robustness respect to individual specific gait and postural behaviors. The overall performances of the system are excellent with a specificity higher than 97%
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