Edgar Martínez García

Easiness of acceptance metric for effective human robot interactions in therapeutic pet robots

It is essential for robots to be highly usable to achieve increased acceptance among target population. In this paper, we put forward a metric that measures easiness of acceptance using the length of time to initiate interaction, total interaction time, time needed to identify perception-reaction phenomenon, its occurrence, number of violations and affective interaction factor. This measure would be useful as a parameter to compare different target groups, robots, and different nature of interaction. Experiments were conducted to validate the proposed metric in an eldercare nursing home using a therapeutic pet robot ERIC (Elderly Rehabilitation Interactive Companion), a dog-like robot. Results of the statistical analysis showed no significant difference in Easiness of Acceptance (EOA) estimation for between subject experiments in groups with similar characteristics for pre and post training trials. But, significant differences in EOA estimation was witnessed for within subject experiments for pre and post training trials.

Particle Swarm Optimization as a New Measure of Machine Translation Efficiency

The present work proposes a new approach to measuring efficiency of evolutionary algorithm-based Machine Translation. We implement some attributes of evolutionary algorithms performing cosine similarity objective function of a Particle Swarm Optimization (PSO) algorithm then, we evaluate an English text set for translation precision into the Spanish text as a simulated benchmark, and explore the backward process. Our results show that PSO algorithm can be used for translation of multiple language sentences with one identifier only, in other words the technology presented is language-pair independent. Specifically, we indicate that our cosine similarity objective function improves the velocity attribute of the PSO algorithm, making the complex cost functions unnecessary.

Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals

This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to detect the QRS complex of electrocardiogram signals. The method is performed in a window framework. First, the Wavelet transform is applied to the ECG signal to remove noise. Next, the Hilbert transform is applied to detect dominant peak points in the signal. Finally, the adaptive threshold technique is applied to detect R-peaks, Q, and S points. The performance of the algorithm is evaluated against the MIT-BIH arrhythmia database, and the numerical results indicated significant detection accuracy.

Predicting the Short-Term Exchange Rate Between United State Dollar and Czech Koruna Using Hilbert-Huang Transform and Fuzzy Logic

In this paper, the combination of the Hilbert-Huang Transform, fuzzy logic and an embedding theorem is described to predict the short-term exchange rate from United States dollar to Czech Koruna. By Using the Hilbert-Huang Transform as an adaptive filter, the proposed method decreases the embedding dimension space from five (original samples) to four (de-noising samples). This dimension space provides the number of inputs to the fuzzy rule base system, which causes the number of rules, the time for training and the inference process to decrease. Experimental results indicated that this method achieves higher accuracy prediction than the direct use of original data.

Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.

Development of a handheld EMG device for assessment and assistive purposes during post stroke therapy

This paper presents the development of a hand held Electromyograph (EMG) device for assisting the subject during therapy. Surface Elctromyogram (sEMG) is acquired, processed and utilized for assessment and assisting therapy. Developed device includes integrated wireless module to transmit raw EMG through wireless link to a PC for processing information required for assessment of therapy. For observatory assessment, LabVIEW based interface was developed. The interface will be used to develop interactive computer games for motivating the subjects during therapy sessions.