3,717 research outputs found
Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data
In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrapand k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead tocorrect classification rates with less than 10% of the original features
Optimized Gated Deep Learning Architectures for Sensor Fusion
Sensor fusion is a key technology that integrates various sensory inputs to
allow for robust decision making in many applications such as autonomous
driving and robot control. Deep neural networks have been adopted for sensor
fusion in a body of recent studies. Among these, the so-called netgated
architecture was proposed, which has demonstrated improved performances over
the conventional convolutional neural networks (CNN). In this paper, we address
several limitations of the baseline negated architecture by proposing two
further optimized architectures: a coarser-grained gated architecture employing
(feature) group-level fusion weights and a two-stage gated architectures
leveraging both the group-level and feature level fusion weights. Using driving
mode prediction and human activity recognition datasets, we demonstrate the
significant performance improvements brought by the proposed gated
architectures and also their robustness in the presence of sensor noise and
failures.Comment: 10 pages, 5 figures. Submitted to ICLR 201
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
Recognizing complex faces and gaits via novel probabilistic models
In the field of computer vision, developing automated systems to recognize people
under unconstrained scenarios is a partially solved problem. In unconstrained sce-
narios a number of common variations and complexities such as occlusion, illumi-
nation, cluttered background and so on impose vast uncertainty to the recognition
process. Among the various biometrics that have been emerging recently, this
dissertation focus on two of them namely face and gait recognition.
Firstly we address the problem of recognizing faces with major occlusions amidst
other variations such as pose, scale, expression and illumination using a novel
PRObabilistic Component based Interpretation Model (PROCIM) inspired by key
psychophysical principles that are closely related to reasoning under uncertainty.
The model basically employs Bayesian Networks to establish, learn, interpret and
exploit intrinsic similarity mappings from the face domain. Then, by incorporating
e cient inference strategies, robust decisions are made for successfully recognizing
faces under uncertainty. PROCIM reports improved recognition rates over recent
approaches.
Secondly we address the newly upcoming gait recognition problem and show that
PROCIM can be easily adapted to the gait domain as well. We scienti cally
de ne and formulate sub-gaits and propose a novel modular training scheme to
e ciently learn subtle sub-gait characteristics from the gait domain. Our results
show that the proposed model is robust to several uncertainties and yields sig-
ni cant recognition performance. Apart from PROCIM, nally we show how a
simple component based gait reasoning can be coherently modeled using the re-
cently prominent Markov Logic Networks (MLNs) by intuitively fusing imaging,
logic and graphs.
We have discovered that face and gait domains exhibit interesting similarity map-
pings between object entities and their components. We have proposed intuitive
probabilistic methods to model these mappings to perform recognition under vari-
ous uncertainty elements. Extensive experimental validations justi es the robust-
ness of the proposed methods over the state-of-the-art techniques.
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
The problem of human activity recognition is central for understanding and
predicting the human behavior, in particular in a prospective of assistive
services to humans, such as health monitoring, well being, security, etc. There
is therefore a growing need to build accurate models which can take into
account the variability of the human activities over time (dynamic models)
rather than static ones which can have some limitations in such a dynamic
context. In this paper, the problem of activity recognition is analyzed through
the segmentation of the multidimensional time series of the acceleration data
measured in the 3-d space using body-worn accelerometers. The proposed model
for automatic temporal segmentation is a specific statistical latent process
model which assumes that the observed acceleration sequence is governed by
sequence of hidden (unobserved) activities. More specifically, the proposed
approach is based on a specific multiple regression model incorporating a
hidden discrete logistic process which governs the switching from one activity
to another over time. The model is learned in an unsupervised context by
maximizing the observed-data log-likelihood via a dedicated
expectation-maximization (EM) algorithm. We applied it on a real-world
automatic human activity recognition problem and its performance was assessed
by performing comparisons with alternative approaches, including well-known
supervised static classifiers and the standard hidden Markov model (HMM). The
obtained results are very encouraging and show that the proposed approach is
quite competitive even it works in an entirely unsupervised way and does not
requires a feature extraction preprocessing step
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