14 research outputs found
Human activity recognition on smartphones for mobile context awareness
Activity-Based Computing [1] aims to capture the state of the user and its environment
by exploiting heterogeneous sensors in order to provide adaptation to
exogenous computing resources. When these sensors are attached to the subject’s
body, they permit continuous monitoring of numerous physiological signals. This
has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence
(AmI) in daily activity monitoring for elderly people. In this paper,
we present a system for human physical Activity Recognition (AR) using smartphone
inertial sensors. As these mobile phones are limited in terms of energy and
computing power, we propose a novel hardware-friendly approach for multiclass
classification. This method adapts the standard Support Vector Machine (SVM)
and exploits fixed-point arithmetic. In addition to the clear computational advantages
of fixed-point arithmetic, it is easy to show the regularization effect of the
number of bits and then the connections with the Statistical Learning Theory. A
comparison with the traditional SVM shows a significant improvement in terms
of computational costs while maintaining similar accuracy, which can contribute
to develop more sustainable systems for AmI.Peer ReviewedPostprint (published version
Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents
Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an Induction Motors bearings monitoring tool which leverages on stator currents signals processed with a Deep Learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a Deep Learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system
Energy efficient smartphone-based activity recognition using fixed-point arithmetic
In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting
assisted living applications such as remote patient activity monitoring for the disabled
and the elderly. The method exploits fixed-point arithmetic to propose a modified
multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre-
serve the smartphone battery lifetime with respect to the conventional floating-point
based formulation while maintaining comparable system accuracy levels. Experiments
show comparative results between this approach and the traditional SVM in terms of
recognition performance and battery consumption, highlighting the advantages of the
proposed method.Peer ReviewedPostprint (published version
Energy Efficient Smartphone-Based Activity Recognition Using Fixed-Point Arithmetic
In this paper we propose a novel energy efficient approach for the recog- nition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre- serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method
Density Preserving Sampling: Robust and Efficient Alternative to Cross-validation for Error Estimation
Estimation of the generalization ability of a classi-
fication or regression model is an important issue, as it indicates
the expected performance on previously unseen data and is
also used for model selection. Currently used generalization
error estimation procedures, such as cross-validation (CV) or
bootstrap, are stochastic and, thus, require multiple repetitions
in order to produce reliable results, which can be computationally
expensive, if not prohibitive. The correntropy-inspired density-
preserving sampling (DPS) procedure proposed in this paper
eliminates the need for repeating the error estimation procedure
by dividing the available data into subsets that are guaranteed to
be representative of the input dataset. This allows the production
of low-variance error estimates with an accuracy comparable to
10 times repeated CV at a fraction of the computations required
by CV. This method can also be used for model ranking and
selection. This paper derives the DPS procedure and investigates
its usability and performance using a set of public benchmark
datasets and standard classifier
The Hybrid Dynamic Prototype Construction and Parameter Optimization with Genetic Algorithm for Support Vector Machine
The optimized hybrid artificial intelligence model is a potential tool to deal with construction engineering and management problems. Support vector machine (SVM) has achieved excellent performance in a wide variety of applications. Nevertheless, how to effectively reduce the training complexity for SVM is still a serious challenge. In this paper, a novel order-independent approach for instance selection, called the dynamic condensed nearest neighbor (DCNN) rule, is proposed to adaptively construct prototypes in the training dataset and to reduce the redundant or noisy instances in a classification process for the SVM. Furthermore, a hybrid model based on the genetic algorithm (GA) is proposed to simultaneously optimize the prototype construction and the SVM kernel parameters setting to enhance the classification accuracy. Several UCI benchmark datasets are considered to compare the proposed hybrid GA-DCNN-SVM approach with the previously published GA-based method. The experimental results illustrate that the proposed hybrid model outperforms the existing method and effectively improves the classification performance for the SVM
Empirical evaluation of resampling procedures for optimising SVM hyperparameters
Tuning the regularisation and kernel hyperparameters is a vital step in optimising the generalisation performance of kernel methods, such as the support vector machine (SVM). This is most often performed by minimising a resampling/cross-validation based model selection criterion, however there seems little practical guidance on the most suitable form of resampling. This paper presents the results of an extensive empirical evaluation of resampling procedures for SVM hyperparameter selection, designed to address this gap in the machine learning literature. Wetested 15 different resampling procedures on 121 binary classification data sets in order to select the best SVM hyperparameters. Weused three very different statistical procedures to analyse the results: the standard multi-classifier/multidata set procedure proposed by Demˇsar, the confidence intervals on the excess loss of each procedure in relation to 5-fold cross validation, and the Bayes factor analysis proposed by Barber. We conclude that a 2-fold procedure is appropriate to select the hyperparameters of an SVM for data sets for 1000or more datapoints, while a 3-fold procedure is appropriate for smaller data sets
Dynamic Recognition of Driver’s Propensity Based on GPS Mobile Sensing Data and Privacy Protection
Driver’s propensity is a dynamic measurement of driver’s emotional preference characteristics in driving process. It is a core parameter to compute driver’s intention and consciousness in safety driving assist system, especially in vehicle collision warning system. It is also an important influence factor to achieve the Driver-Vehicle-Environment Collaborative Wisdom and Control macroscopically. In this paper, dynamic recognition model of driver’s propensity based on support vector machine is established taking the vehicle safety controlled technology and respecting and protecting the driver’s privacy as precondition. The experiment roads travel time obtained through GPS is taken as the characteristic parameter. The sensing information of Driver-Vehicle-Environment was obtained through psychological questionnaire tests, real vehicle experiments, and virtual driving experiments, and the information is used for parameter calibration and validation of the model. Results show that the established recognition model of driver’s propensity is reasonable and feasible, which can achieve the dynamic recognition of driver’s propensity to some extent. The recognition model provides reference and theoretical basis for personalized vehicle active safety systems taking people as center especially for the vehicle safety technology based on the networking
In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines
In-sample approaches to model selection and error
estimation of support vector machines (SVMs) are not as widespread as out-of-sample methods, where part of the data is removed from the training set for validation and testing purposes, mainly because their practical application is not straightforward and the latter provide, in many cases, satisfactory results. In this paper, we survey some recent and not-so-recent results of the data-dependent structural risk minimization framework and propose a proper reformulation of the SVM learning algorithm, so that the in-sample approach can be effectively applied. The experiments, performed both on simulated and real-world datasets, show that our in-sample approach can be favorably compared to out-of-sample methods, especially in cases where the latter ones provide questionable results. In particular, when the number of samples is small compared to their dimensionality, like in classification of microarray data, our proposal can outperform conventional out-of-sample approaches such as the cross validation, the leave-one-out, or the Bootstrap methods