Kenneth Palacio-Baus

Semantic Recommender Systems for Digital TV: From Demographic Stereotyping to Personalized Recommendations

Compared to analog transmissions, Digital Television (DTV) standards allows a higher number of available TV stations and consequently, a larger entertainment offer. In this context, Recommender Systems (RS) support users in choosing entertainment content by narrowing their options to a reduced set based on their preferences an interests. However, new users or those having incomplete profiles prevent the system to produce accurate recommendations, which is more noticeable in early stages of the RS. This paper proposes the use of a demographic stereotyping approach based on minimal user attributes acquired during user registration. Furthermore, we propose an experimental procedure that can be used to compare the system accuracy for the created stereotypes and for users making extensive use of the system.

Semiautomatic validation of RR time series in an ECG stress test database

This paper reports an automatic method for characterizing the quality of the RR-time series in the stress test database known as DICARDIA. The proposed methodology is simple and consists in subdividing the RR time series in a set of windows for estimating the quantity of artifacts based on a threshold value that depends on the standard deviation of RR-time series for each recorded lead. In a first stage, a manual annotation was performed considering four quality classes for the RR-time series (Reference lead, Good Lead, Low Quality Lead and Useless Lead). Automatic annotation was then performed varying the number of windows and threshold value for the standard deviation of the RR-time series. The metric used for evaluating the quality of the annotation was the Matching Ratio. The best results were obtained using a higher number of windows and considering only three classes (Good Lead, Low Quality Lead and Useless). The proposed methodology allows the utilization of the online available DICARDIA Stress Test database for different types of research.

Characterizing artifacts in RR stress test time series

Electrocardiographic stress test records have a lot of artifacts. In this paper we explore a simple method to characterize the amount of artifacts present in unprocessed RR stress test time series. Four time series classes were defined: Very good lead, Good lead, Low quality lead and Useless lead. 65 ECG, 8 lead, records of stress test series were analyzed. Firstly, RR-time series were annotated by two experts. The automatic methodology is based on dividing the RR-time series in non-overlapping windows. Each window is marked as noisy whenever it exceeds an established standard deviation threshold (SDT). Series are classified according to the percentage of windows that exceeds a given value, based upon the first manual annotation. Different SDT were explored. Results show that SDT close to 20% (as a percentage of the mean) provides the best results. The coincidence between annotators classification is 70.77% whereas, the coincidence between the second annotator and the automatic method providing the best matches is larger than 63%. Leads classified as Very good leads and Good leads could be combined to improve automatic heartbeat labeling.

Mechatronic Design of a Lower Limb Exoskeleton

This chapter presents a lower limb exoskeleton mechatronic design. The design aims to be used as a walking support device focused on patients who suffer of partial lower body paralysis due to spine injuries or caused by a stroke. First, the mechanical design is presented and the results are validated through dynamical simulations performed in Autodesk Inventor and MATLAB. Second, a communication network design is proposed in order to establish a secure and fast data link between sensors, actuators, and microprocessors. Finally, patient-exoskeleton system interaction is presented and detailed. Movement generation is performed by means of digital signal processing techniques applied to electromyography (EMG) and electrocardiography (EEG) signals. Such interaction system design is tested and evaluated in MATLAB whose results are presented and explained. A proposal of real-time supervisory control is also presented as a part of the integration of every component of the exoskeleton.

TV program recommender using user authentication on middleware Ginga

The system proposed in this article aims to identify and recognize television users with the objective of offering personalized television programming. In this setting, the authentication and recommendation mechanisms used require to collect the necessary information in an implicit manner as much as possible, such that the leisure and entertainment objectives this broadcasting medium brings are not interrupted. The design proposed for the implementation of the interactive application uses an authentication process based on facial recognition and a recommendation algorithm based on contextual information, which is mainly implicitly captured. Experimental obtained results show that the system offers more accurate recommendations when the user exhibits a habitual behavior; e.g. watching TV programs of a same category in a specific channel and schedule.

Evaluating reliability of ultrashort heart rate variability parameters in metabolic syndrome subjects

Heart rate variability (HRV) analysis is barely employed in healthcare environments mainly because of the lack of standard values determining the sympathovagal balance and the difficulty to register RR stationary series. Recent studies have proposed the use of shorter HRV series. For this work, we use a public metabolic syndrome subjects database retrieved during oral glucose tolerance test. In order to explore ultra-short HRV measures reliability we employ an autoregressive model using Burg method, such that short RR sequences can be evaluated while maintaining a good frequency resolution. RR, SD, rMSSD, LF, HF, LFn and LF/HF were computed for different RR sequences (10 min, 5 min, 1 min, 30 s, 10 s). To evaluate the reliability we used the intraclass correlation coefficient (ICC). Additionally, we compared the sympathovagal balance parameters (LFn, LF/HF) among the stages (basal and 30 min). Considering 10 min long registers as references, parameters obtained from 5 min long series present ICC values above 0.78 for all cases. One min long registers present ICC values above 0.70 only for temporal parameters in both RR series and rMSSD. By comparing LFn and LF/HF parameters among the basal state and 30 min, we observed a significant increase of the sympathetic tone (p <; 0.05). However, these differences are important only for 10 and 5 min series. In general, we observe that temporal parameters exhibit higher reliability than those the spectral ones. Nonetheless, registers duration below one min do not present adequate results for the spectral parameters in this work.

Decategorizing demographically stereotyped users in a semantic recommender system

In the domain of Digital Television (DTV) broadcasting technology, the enhancement of signals features over classic analog signal transmission allows increasing the amount of content available for TV viewers. Recommender Systems (RS) arose as a suitable choice to assist users in the overwhelming task of selecting audiovisual content, however, the cold-start problem normally associated to the lack of information in early RS stages, causes that user stereotyping approaches are employed meanwhile the lack of information in user profiles is overcome. This paper presents an experimental approach aimed to determine the best conditions for which users who were categorized within a determined stereotype during the cold-start stage, could migrate to a new state in which they receive personalized recommendations. Experimental results show that the best condition under the selected demographic stereotyping scheme for this transition is directly related to the number of TV programs that a user has rated while making use of the system.

CinC Challenge 2013: comparing three algorithms to extract fetal ECG

This paper reports a comparison between three fetal ECG (fECG) detectors developed during the CinC 2013 challenge for fECG detection. Algorithm A1 is based on Independent Component Analysis, A2 is based on fECG detection of RS Slope and A3 is based on Expectation-Weighted Estimation of Fiducial Points. The proposed methodology was validated using the annotated database available for the challenge. Each detector was characterized in terms of its performance by using measures of sensitivity, (Se), positive predictive value (P+) and delay time (td). Additionally, the database was contaminated with white noise for two SNR conditions. Decision fusion was tested considering the most common types of combination of detectors. Results show that the decision fusion of A1 and A2 improves fQRS detection, maintaining high Se and P+ even under low SNR conditions without a significant td increase.