103 research outputs found
Neural network modelling of Abbott-Firestone roughness parameters in honing processes
In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.Peer ReviewedPostprint (author's final draft
Improved neural models for roughness in honing processes
In the present work improved neural network models for average roughness Ra in rough honing
processes are studied. Four different adaptive models were tested, which integrate previously
obtained direct and indirect models. Such models allow defining values for process variables from
required average roughness Ra values. A control parameter d is employed for determining the error
of the model, and a sensitivity parameter m measures the convergence speed of the models. Models
were tested for m=1, m=10, m=100 and m=1000. Best model was selected having lowest relative
error between experimental and simulated values.Peer ReviewedPostprint (published version
Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine
Activity-Based Computing 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 for computational cost reduction. 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 (author's final draft
Ambulatory mobility characterization using body inertial system: an application to fall detection
The aim of this paper is to study the use of a prototype of wearable device for long term monitoring of gait and balance using inertial sensors. First, it is focused on the design of the device that can be used all day during the patient daily life activities, because it is small, usable and non invasive. Secondly, we present the system calibration to ensure the quality of the sensors data. Afterwodrs, we focus in the experimental methodology for data harvest from extensive types of falls. Finally a statistical analysis allows us to determine the discriminant information to detect falls.Peer ReviewedPostprint (author’s final draft
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
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
Neural network model for surface roughness in semifinish honing
In the present work, neural networks were used for modelling average roughness Ra as a function of
process parameters: grain size, density of abrasive, pressure of honing stones on the workpiece’s
surface, linear speed and tangential speed. For doing this, first experimental semifinish honing tests
were performed. Then results were used for selecting best configuration of the neural network, taking
into account either one or two hidden layers. In addition, neural models were compared to regression
models.Peer ReviewedPostprint (published version
Perceived distress in assisted gait with a four-wheeled rollator under stress induction conditions
In assisted ambulation, the user’s psychological comfort has a significant impact not only on acceptability of mobility aids but also on overall gait performance. Specifically, in the case of rollators, negative states such as distress may result in balance loss, inefficient manoeuvres, and an increased risk of falling. This paper presents a pilot study to investigate the effect of distress on rollator assisted navigation. To achieve this goal, a novel test protocol is proposed to assess distress while walking with a rollator, using the Self-Assessment Manikin (SAM) questionnaire. First, the participant completes a standardised visual stress induction test and fills in a SAM questionnaire on the dimensions of arousal and valence, to establish personal benchmarks. Then, they complete a course consisting of four navigation tasks with different levels of difficulty that affect the rollator manoeuvrability, filling in a SAM questionnaire after each task. An experiment including 25 healthy volunteers has been completed. Our preliminary results show that stressors like uneven or sloping surfaces increase perceived stress, whereas the shape of the trajectory does not significantly affect stress. The ultimate purpose of this work is to validate a performance-oriented protocol to investigate the dynamics of stress response in assisted walk and to train automatic stress detection systems.The work was supported by the Ministerio de Ciencia e Innovación [RTI2018-096701-B-C22]; Ministerio de Ciencia, Innovación y Universidades [RTI2018-096701-B-C21 (SAVIA:]; Ministerio de Ciencia, Innovación y Universidades [RTI2018-096701-B-C21]; Universidad de Málaga [E3-PROYECTOS DE PRUEBA DE CONCEPTO (E3/02/18)].Peer ReviewedPostprint (published version
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