81,425 research outputs found
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
Learning to Extract Motion from Videos in Convolutional Neural Networks
This paper shows how to extract dense optical flow from videos with a
convolutional neural network (CNN). The proposed model constitutes a potential
building block for deeper architectures to allow using motion without resorting
to an external algorithm, \eg for recognition in videos. We derive our network
architecture from signal processing principles to provide desired invariances
to image contrast, phase and texture. We constrain weights within the network
to enforce strict rotation invariance and substantially reduce the number of
parameters to learn. We demonstrate end-to-end training on only 8 sequences of
the Middlebury dataset, orders of magnitude less than competing CNN-based
motion estimation methods, and obtain comparable performance to classical
methods on the Middlebury benchmark. Importantly, our method outputs a
distributed representation of motion that allows representing multiple,
transparent motions, and dynamic textures. Our contributions on network design
and rotation invariance offer insights nonspecific to motion estimation
Event detection in field sports video using audio-visual features and a support vector machine
In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation
This paper presents a novel autonomous quality metric to quantify the
rehabilitations progress of subjects with knee/hip operations. The presented
method supports digital analysis of human gait patterns using smartphones. The
algorithm related to the autonomous metric utilizes calibrated acceleration,
gyroscope and magnetometer signals from seven Inertial Measurement Unit
attached on the lower body in order to classify and generate the grading system
values. The developed Android application connects the seven Inertial
Measurement Units via Bluetooth and performs the data acquisition and
processing in real-time. In total nine features per acceleration direction and
lower body joint angle are calculated and extracted in real-time to achieve a
fast feedback to the user. We compare the classification accuracy and
quantification capabilities of Linear Discriminant Analysis, Principal
Component Analysis and Naive Bayes algorithms. The presented system is able to
classify patients and control subjects with an accuracy of up to 100\%. The
outcomes can be saved on the device or transmitted to treating physicians for
later control of the subject's improvements and the efficiency of physiotherapy
treatments in motor rehabilitation. The proposed autonomous quality metric
solution bears great potential to be used and deployed to support digital
healthcare and therapy.Comment: 5 Page
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