Arruda Dos Vinhos, Portugal
Arruda Dos Vinhos, Portugal

Time filter

Source Type

Myklebust H.,Research Center for Training and Performance | Nunes N.,New University of Lisbon | Hallen J.,Research Center for Training and Performance | Gamboa H.,New University of Lisbon | Gamboa H.,PLUX Inc
BIOSIGNALS 2011 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing | Year: 2011

Aims: Experience morphology of acceleration signals, extract useful information and classify time periods into defined techniques during cross-country skiing. Method: Three Norwegian cross-country skiers ski skated one lap in the 2011 world championship sprint track as fast as possible with 5 accelerometers attached to their body and equipment. Algorithms for detecting ski/pole hits and leaves and computing specific ski parameters like cycle times (CT), poling/pushing times (PT), recovery times (RT), symmetry between left and right side and technique transition times were developed based on thresholds and validated against video. Results: In stable and repeated techniques, pole hits/leaves and ski leaves were detected 99% correctly, while ski hits were more difficult to detect (77%). From these hit and leave values CT, PT, RT, symmetry and technique transitions were successfully calculated. Conclusion: Accelerometers can definitely contribute to biomechanical research in cross-country skiing and studies combining force, position and accelerometer data will probably be seen more frequently in the future.


Canento F.,Telecommunications Institute of Portugal | Fred A.,Telecommunications Institute of Portugal | Silva H.,Telecommunications Institute of Portugal | Silva H.,PLUX Inc | And 3 more authors.
Proceedings of IEEE Sensors | Year: 2011

We present an experimental setup, sensor data handling, and evaluation framework for emotion recognition, based on multimodal biosignal sensor data. For labeled data acquisition we developed an emotion elicitation block, with a bank of labeled videos containing different triggering stimuli. A biosignal acquisition apparatus was used to collect multimodal data, namely: Electromyography (EMG); Electrocardiography (ECG); Electrodermal Activity (EDA); Blood Volume Pulse (BVP); Peripheral Temperature (SKT); and Respiration (RESP). An automated biosignal processing and feature extraction toolbox was developed to convert raw data into meaningful parameters. Experimental results revealed trends associated with triggering events, providing a baseline for emotion recognition. Through LOOCV with a k-NN classifier, we obtained recognition rates of 81% to distinguish between positive and negative emotions, and of 70% to distinguish between positive, neutral, and negative emotions. © 2011 IEEE.


Silva H.,Telecommunications Institute of Portugal | Silva H.,PLUX Inc | Lourenco A.,Telecommunications Institute of Portugal | Lourenco R.,DEETC | And 3 more authors.
Proceedings of IEEE Sensors | Year: 2011

In this paper we present a study and evaluation of a custom single differential sensor design for ECG data acquisition, recurring to electro-textile electrodes as the interface between the sensor and the skin. Our work is focused on improving current signal acquisition methods for ECG biometrics, targeting wearable, continuous and unobtrusive applications. A circuit with virtual ground was also devised for enhanced usability. The purpose is to build upon and further extend the state-of-the-art in the field, improving existing signal acquisition conditions by: minimizing the number of electrical contact points with the subject's body; eliminating the need of gel in the interface with the skin; and devising a non-intrusive design that can be easily integrated into wearable devices. Experimental analysis has been performed to compare the proposed approach with a reference acquisition sensor, and results validate the potential of our method. © 2011 IEEE.


Canento F.,Telecommunications Institute of Portugal | Silva H.,Telecommunications Institute of Portugal | Silva H.,PLUX Inc | Fred A.,Telecommunications Institute of Portugal
Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health, MindCare 2012, in Conjunction with BIOSTEC 2012 | Year: 2012

We present an overview and study on the applicability of multimodal electrophysiological data acquisition and processing to emotion recognition. We build on previous work in the field and further explore the emotion elicitation process, by using videos to stimulate emotions in several participants. Electrophysiological data from Electrocardiography (ECG), Blood Volume Pulse (BVP), Electrodermal Activity (EDA), Respiration (RESP), Electromyography (EMG), and Peripheral Temperature (SKT) sensors was acquired and used to classify the negative and positive emotions. We evaluate the emotional status identification accuracy both in terms of the target emotions and those reported by the participants, with recognition rates above 70% through Leave One Out Cross Validation (LOOCV) with a k-NN Classifier.


Araujo T.,New University of Lisbon | Anjos A.,Polytechnic Institute of Lisbon | Nunes N.,PLUX Inc | Rebelo P.,Polytechnic Institute of Lisbon | Gamboa H.,New University of Lisbon
BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015 | Year: 2015

Introduction: Neuromuscular electrical stimulation (NMES) is used by physical therapists in the clinic. The efficacy of NMES is limited by the rapid onset muscle fatigue. The role of NMES parameters is muscle fatigue is not clear. Objective: To determine the effects of shape waveform on muscle fatigue, during NMES. Methods: Twelve healthy subjects participated in the study. Subjects were assigned to 1 of 3 groups, randomly. Group assignment determined the order in which they were tested using 3 different shape waveforms. Maximal voluntary isometric contraction (MVIC) was measured during the first session. Fatigue test was applied with amplitude required to elicit 50% of the MVIC. In each 3 testing sessions torque of contraction and level comfort were measured, and percent fatigue was calculated. Analysis of variance tests for dependent samples was used to determine the effect of shape waveform on muscle fatigue and comfort scores Results: The results showed no one shape waveform was most fatigable and that SQ wave induced more uncomfortable stimulus.


Costa N.,New University of Lisbon | Araujo T.,New University of Lisbon | Araujo T.,PLUX Inc | Nunes N.,PLUX Inc | And 2 more authors.
SIGMAP 2012, WINSYS 2012 - Proceedings of the International Conference on Signal Processing and Multimedia Applications and Wireless Information Networks and Systems | Year: 2012

Large amounts of data, increasing every day, are stored and transferred through the internet. These data are normally weakly structured making information disperse, uncorrelated, non-transparent and difficult to access and share. Semantic Web, proposed by the World Wide Web Consortium (W3C), addresses this problem by promoting semantic structured data, like ontologies, enabling machines to perform more work involved in finding, combining, and acting upon information on the web. Pursuing this vision, a Knowledge Acquisition System was created, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and JSON Schema to define that structure, enabling new ways of acquiring and storing knowledge semantically structured. A novel Human Computer Interaction framework was developed with this knowledge system, enabling the end user to, practically, configure all kinds of electrical devices and control them. With the data structured by a Schema, the software becomes robust, error - free and human readable. To show the potential of this tool, the end user can configure an electrostimulator, surpassing the specific use of many Electrophysiology software. Therefore, we provide a tool for clinical, sports and investigation where the user has the liberty to produce their own protocols by sequentially compile electrical impulses.


Pimentel A.,New University of Lisbon | Gomes R.,New University of Lisbon | Olstad B.H.,Norwegian School of Sport Sciences | Gamboa H.,New University of Lisbon | Gamboa H.,PLUX Inc
Interacting with Computers | Year: 2015

Electromyographic (EMG) signals play a key role in many clinical and biomedical applications. They can be used for identifying patients with muscular disabilities, assessing lower-back pain, kinesiology and motor control. There are three common applications of the EMG signal: (1) to determine the activation timing of the muscle; (2) to estimate the force produced by the muscle and (3) to analyze muscular fatigue through analysis of the frequency spectrum of the signal. We have developed an EMG tool that was incorporated in an existing web-based biosignal acquisition and processing framework. This tool can be used on a post-processing environment and provides not only frequency and time parameters, but also an automatic detection of starting and ending times for muscular voluntary contractions using a threshold-based algorithm with the inclusion of the Teager-Kaiser energy operator. The algorithm for the muscular voluntary contraction detection can also be reported after a real-Time acquisition, in order to discard possible outliers and simultaneously compare activation times in different muscles. This tool covers all known applications and allows a careful and detailed analysis of the EMG signal for both clinicians and researchers. The detection algorithm works without user interference and is also user-independent. It manages to detect muscular activations in an interactive process. The user simply has to select the signal's time interval as input, and the outcomes are provided afterwards. © The Author 2015.


Araujo T.,New University of Lisbon | Dias P.,New University of Lisbon | Nunes N.,PLUX Inc | Gamboa H.,New University of Lisbon
BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015 | Year: 2015

Objectives: This study aims to evaluate the influence of standard electrical stimulation on human electrophysiology. Methods: A total of 10 healthy subjects were submitted to the same protocol. The electrical stimuli were applied on the median nerve of the left wrist. Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) signals were acquired from the index finger through an oximeter and from both the abductor pollicis muscle and the 3rd palmar interosseous muscle of the right hand, respectively. Nerve stimulation was performed using increasing intensities current: range from 5 to 30 mA, with 1mA step and applying 20 stimuli per step. Heart Rate (HR) and Heart Rate Variability (HRV) were computed, from the analysis of the latency between BVP pulses, in basal state and during stimulation. EDA parameters response latency, response rise time and readaptation slope were computed for each burst. Discussion: Electrical stimulation reveals to influence several parameters of the Autonomic Nervous System (ANS). It was easily detected an EDA rise response for each of the applied bursts and also an increase of the HRV during stimulation.


Gomes A.L.,New University of Lisbon | Paixao V.,PLUX Inc | Gamboa H.,New University of Lisbon | Gamboa H.,Champalimaud Center for the Unknown
BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015 | Year: 2015

The Human Activity Recognition (HAR) systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according to the performed activities. In this work, a framework for human activity recognition in accelerometry (ACC) based on our previous work and with new features and techniques was developed. The new features set covered wavelets, the CUIDADO features implementation and the Log Scale Power Bandwidth creation. The Hidden Markov Models were also applied to the clustering output. The Forward Feature Selection chose the most suitable set from a 423th dimensional feature vector in order to improve the clustering performances and limit the computational demands. K-means, Affinity Propagation, DBSCAN and Ward were applied to ACC databases and showed promising results in activity recognition: from 73:20%±7:98% to 89:05%±7:43% and from 70:75%±10:09% to 83:89%±13:65% with the Hungarian accuracy (HA) for the FCHA and PAMAP databases, respectively. The Adjust Rand Index (ARI) was also applied as clustering evaluation method. The developed algorithm constitutes a contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.


Loading PLUX Inc collaborators
Loading PLUX Inc collaborators