PhyCS 2014 Abstracts

Area 1 – Methodologies and Methods
Full Papers
Area 2 – Human Factors
Full Papers
Area 3 – Devices
Full Papers
Area 1 – Methodologies and Methods
Full Papers
Area 3 – Devices
Full Papers
Area 2 – Human Factors
Full Papers
Area 3 – Devices
Full Papers
Area 1 – Methodologies and Methods
Full Papers
Area 2 – Human Factors
Full Papers
Area 1 – Methodologies and Methods
Full Papers
Area 2 – Human Factors
Full Papers
Area 3 – Devices
Full Papers
Area 1 – Methodologies and Methods
Full Papers
Area 2 – Human Factors
Full Papers
Area 1 – Methodologies and Methods
Full Papers
Area 3 – Devices
Full Papers
Area 2 – Human Factors
Full Papers
Area 1 – Methodologies and Methods
Full Papers
Area 3 – Devices
Full Papers
Area 1 – Methodologies and Methods
Full Papers
Area 3 – Devices
Full Papers
Area 2 – Human Factors
Full Papers
Area 3 – Devices
Full Papers
Area 24 – Applications
Full Papers Short Papers Posters
Area 1 – Methodologies and Methods
Full Papers
Paper Nr: 2
Title:
Seven Principles to Mine Flexible Behavior from Physiological Signals for Effective Emotion Recognition and Description in Affective Interactions
Authors:
Rui Henriques and Ana Paiva
Abstract: Measuring affective interactions using physiological signals has become a critical step to understand engagements with human and artificial agents. However, traditional methods for signal analysis are not yet able to effectively deal with the differences of responses across individuals and with flexible sequential behavior. In this work, we rely on empirical results to define seven principles for a robust mining of physiological signals to recognize and characterize affective states. The majority of these principles are novel and driven from advanced pre-processing techniques and temporal data mining methods. A methodology that integrates these principles is proposed and validated using electrodermal signals collected during human-to-human and human-to-robot affective interactions.
Paper Nr: 6
Title:
Directed Effort – A Generic Measurand for Higher Level Behavior Analysis
Authors:
Benedikt Gollan and Alois Ferscha
Abstract: Behavior and body language are essential components of human interaction. In this paper, we propose a meta-level representation of human behavior for interpretative, higher level applications in human-computer interaction systems called Directed Effort. A theoretical framework is described which is derived from behavioral and psychological sciences and which is designed to represent the commitment and interest of people towards objects via behavior analysis in real-life scenarios. Directed Effort, a score which allows the interpretation of detected behavior changes is introduced as a generic measurand. Furthermore, a prototypical implementation is documented to show the potential of the computed meta-level description of behavior.
Area 2 – Human Factors
Full Papers
Paper Nr: 10
Title:
Extracting Emotions and Communication Styles from Vocal Signals
Authors:
Licia Sbattella, Luca Colombo, Carlo Rinaldi, Roberto Tedesco, Matteo Matteucci and Alessandro Trivilini
Abstract: Many psychological and social studies highlighted the two distinct channels we use to exchange information among us—an explicit, linguistic channel, and an implicit, paralinguistic channel. The latter contains information about the emotional state of the speaker, providing clues about the implicit meaning of the message. In particular, the paralinguistic channel can improve applications requiring human-machine interactions (for example, Automatic Speech Recognition systems or Conversational Agents), as well as support the analysis of human-human interactions (think, for example, of clinic or forensic applications). In this work we present PrEmA, a tool able to recognize and classify both emotions and communication style of the speaker, relying on prosodic features. In particular, communication-style recognition is, to our knowledge, new, and could be used to infer interesting clues about the state of the interaction. We selected two sets of prosodic features, and trained two classifiers, based on the Linear Discriminant Analysis. The experiments we conducted, with Italian speakers, provided encouraging results (Ac=71% for classification of emotions, Ac=86% for classification of communication styles), showing that the models were able to discriminate among emotions and communication styles, associating phrases with the correct labels.
Area 3 – Devices
Full Papers
Paper Nr: 14
Title:
Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces
Authors:
Javier Asensio-Cubero, John Q. Gan and Ramaswamy Palaniappan
Abstract: Brain computer interfaces are control systems that allow the interaction with electronic devices by analysing the user’s brain activity. The analysis of brain signals, more concretely, electroencephalographic data, represents a big challenge due to its noisy and low amplitude nature. Many researchers in the field have applied wavelet transform in order to leverage the signal analysis benefiting from its temporal and spectral capabilities. In this study we make use of the so-called second generation wavelets to extract features from temporal, spatial and spectral domains. The complete multiresolution analysis operates over an enhanced graph representation of motor imaginary trials, which uses per-subject knowledge to optimise the spatial links among the electrodes and to improve the filter design. As a result we obtain a novel method that improves the performance of classifying different imaginary limb movements without compromising the low computational resources used by lifting transform over graphs.
Area 4 – Methodologies and Methods
Full Papers
Paper Nr: 19
Title:
A Hierarchical BCI System Able to Discriminate between Non Intentional Control State and Four Intentional Control Activities
Authors:
Julio Abascal, Andoni Arruti, José I. Martín and Javier Muguerza
Abstract: This paper presents a two-level hierarchical approach to recognising intentional and non intentional mental tasks on a brain-computer interface. A clustering process is performed at the first recognition level in order to differentiate Non intentional Control state (NC) patterns from Intentional Control (IC) patterns. At the second level, the IC detected patterns are classified by means of supervised learning techniques, applied to the type of movement (left hand, right hand, tongue or foot imagery movement). The objective is to achieve high correct movement recognition scores, with a low percentage of wrong decisions (that is, low false positive rates), to avoid user frustration. Offline evaluation of the proposed prototype shows 84.5% accuracy, with a 6.7% false positive rate.
Paper Nr: 21
Title:
Design and Validation of a Mental and Social Stress Induction Protocol – Towards Load-invariant Physiology-based Stress Detection
Authors:
Camille Jeunet, Fabien Lotte and Christian Mühl
Abstract: Stress is a major societal issue with negative impacts on health and economy. Physiological computing offers a continuous, direct, and unobtrusive method for stress level assessment and computer-assisted stress management. However, stress is a complex construct and its physiology can vary depending on its source: cognitive workload or social evaluation. To study the feasibility of physiology-based load-invariant psychosocial stress-detection, we designed a stress-induction protocol able to independently vary the relevant types of psychophysiological activity: mental and psychosocial stress. Here, we validate the efficacy of our protocol to induce psychosocial and mental stress. Our participants (N=24) had to perform a cognitive task associated with two workload conditions (low/high mental stress), in two contexts (low/high psychosocial stress), during which we recorded subjects’ self-reports, behaviour, physiology and neurophysiology. Questionnaires showed that the subjectively perceived level of stress varied with the psychosocial stress induction, while perceived arousal and mental effort levels vary with mental stress induction. Behaviour and physiology further corroborated the validity of our protocol. Heart rate and skin conductance globally increased after psychosocial stress induction relative to the non-stressful condition. Moreover, we demonstrated that higher workload tasks (mental stress) led to decrease in performance and a marked increase of heart rate.
Area 5 – Devices
Full Papers
Paper Nr: 23
Title:
Online Detection of P300 related Target Recognition Processes During a Demanding Teleoperation Task – Classifier Transfer for the Detection of Missed Targets
Authors:
Hendrik Woehrle and Elsa Andrea Kirchner
Abstract: The detection of event related potentials and their usage for innovative tasks became a mature research topic in the last couple of years for brain computer interfaces. However, the typical experimental setups are usually highly controlled and designed to actively evoke specific brain activity like the P300 event related potential. In this paper, we show that the detection and passive usage of the P300 related brain activity is possible in highly uncontrolled and noisy application scenarios where the subjects are performing demanding senso-motor task, i.e., telemanipulation of a real robotic arm. In the application scenario, the subject wears an exoskeleton to control a robotic arm, which is presented to him in a virtual scenario. While performing the telemanipulation task he has to respond to important messages. By online analysis of the subject’s electroencephalogram we detect P300 related target recognition processes to infer on upcoming response behavior or missing of response behavior in case a target was not recognized. We show that a classifier that is trained to distinguish between brain activity evoked by recognized task relevant stimuli and ignored frequent task irrelevant stimuli can be applied to classify between brain activity evoked by recognized task relevant stimuli and brain activity that is evoked in case that task relevant stimuli are not recognized.
Area 6 – Human Factors
Full Papers
Paper Nr: 24
Title:
Orientation of Attention in Visual Feedbacks during Neurofeedback Relaxation
Authors:
Mehdi Karamnejad, Diane Gromala, Amber Choo, Chris Shaw and Xin Tong
Abstract: The assumptions underlying differing approaches to interface design result, in part, on how attention is managed and categorized using theories from media studies. The authors propose the term intraface to refer to biofeedback or other interfaces that are designed to support users who direct their attention inward to inner physiological states. In this paper, the role of representing feedback data in abstract forms is compared in an experiment using Neurosky’s neurofeedback device. Although preliminary, the results suggest that mapping biofeedback data from a brain-computer interface (BCI) to highly abstract ambient animations is more effective for relaxation than mapping it to a highly familiar symbolic smiley face icon or to a progress bar. The authors propose that the relative success of the abstract ambient animation can be explained because this representation of biofeedback data is the form that requires the least amount of attention, and that designing biofeedback interfaces that distribute the attention, supports the need of users to the task of directing most of their attention to their inner physiological states.
Area 7 – Devices
Full Papers
Paper Nr: 26
Title:
An EOG-based Sleep Monitoring System and Its Application on On-line Sleep-stage Sensitive Light Control
Authors:
Chih-En Kuo, Sheng-Fu Liang, Yi-Chieh Li, Fu-Yin Cherng, Wen-Chieh Lin, Peng-Yu Chen, Yen-Chen Liu and Fu-Zen Shaw
Abstract: Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. The development of an automatic sleep-staging system that does not rely upon mounting a bulky PSG or EEG recorder on the head will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an electrooculogram (EOG)-based sleep scoring system is proposed. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually. The proposed method was found to be more than 83% accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for home-use sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.
Area 8 – Methodologies and Methods
Full Papers
Paper Nr: 27
Title:
Stress Recognition – A Step Outside the Lab
Authors:
Julian Ramos, Jin-Hyuk Hong and Anind K. Dey
Abstract: Despite the potential for stress and emotion recognition outside the lab environment, very little work has been reported that is feasible for use in the real world and much less for activities involving physical activity. In this work, we move a step forward towards a stress recognition system that works on a close to real world data set and shows a significant improvement over classification only systems. Our method uses clustering to separate the data into physical exertion levels and later performs stress classification over the discovered clusters. We validate our approach on a physiological stress dataset from 20 participants who performed 3 different activities of varying intensity under 3 different types of stimuli intended to cause stress. The results show an f-measure improvement of 130\% compared to using classification only.
Paper Nr: 33
Title:
Addressing Signals Asynchronicity during Psychophysiological Inference – A Temporal Construction Method
Authors:
François Courtemanche, Aude Dufresne, Elise L. LeMoyne and Esma Aimeur
Abstract: Predicting the psychological state of the user using physiological measures is one of the main objectives of physiological computing. While numerous works have addressed this task with great success, a large number of challenges remain to be solved in order to develop recognition approaches that can precisely and reliably feed human-computer interaction systems. This paper focuses on one of these challenges which is the temporal asynchrony between different physiological signals within one recognition model. The paper proposes a flexible and suitable method for feature extraction based on empirical optimisation of windows’ latency and duration. The approach is described within the theoretical framework of the psychophysiological inference and its common implementation using machine learning. The method has been experimentally validated (46 subjects) and results are presented. Empirically optimised values for the extraction windows are provided.
Area 9 – Human Factors
Short Papers
Paper Nr: 4
Title:
R&D of the Japanese Input Method using Life Log on an Eye-controlled Communication Device for Users with Disabilities
Authors:
Kazuaki Shoji, Hiromi Watanabe and Shinji Kotani
Abstract: We aim to enable the smooth communication of persons physically unable to speak. In our past study, we proposed three Japanese input methods using a portable eye- controlled communication device for users with conditions such as cerebral palsy or amyotrophic lateral sclerosis (ALS). However, these methods require nearly 30 seconds to cycle through one Japanese character. In this paper, we suggest a method to estimate the input word using the clues of nearby characters and accumulated experience. In addition, to raise the precision of the prediction, we use the connection between words based on a thesaurus. We have realized precise word conversion via a few input letters, as proved by the result of the simulation experiment.
Area 10 – Methodologies and Methods
Short Papers
Paper Nr: 9
Title:
Inducing Behavior Change in Children with Autism Spectrum Disorders by Monitoring their Attention
Authors:
Margarida Lucas da Silva, Hugo Silva and Daniel Gonçalves
Abstract: Children with Autism Spectrum Disorders (ASD) generally suffer from disorders which affect multiple behavioral aspects, such as communication, emotional awareness, social interaction, lack of attention, among many others. Modern technologies, are opening up new possibilities for computer-mediated interactions with increased outcomes, enabling both children and tutors to have a more effective work in the development of communicative and cognitive skills. In this article we introduce a module implemented in a platform for human-computer interaction, specifically designed for children with ASD, to control their levels of attention and test inducing behavior change. This allows us to shape new behaviors and learning strategies both in tutors and children.
Paper Nr: 12
Title:
Bilateral Motion Spectra – Analysis and Representation of Human Movement
Authors:
Anthony Schultz
Abstract: The body’s bilateral symmetry allows for various kinds of human motion patterns. Our paper presents a method for analyzing and representing motion capture time series that effectively identifies spatial and temporal patterns. We develop a factored representation of joint angle data based on quaternions and a metric pair for comparing different physical states of articulation. This metric pair is used to generate a metric space pair over the set of time series states. The result is represented as a 2-dimensional color image termed a bilateral motion spectrum. Several spectral motifs are presented and characterized.
Area 11 – Human Factors
Short Papers
Paper Nr: 15
Title:
Review of the Use of Electroencephalography as an Evaluation Method for Human-Computer Interaction
Authors:
Jérémy Frey, Christian Mühl, Fabien Lotte and Martin Hachet
Abstract: Evaluating human-computer interaction is essential as a broadening population uses machines, sometimes in sensitive contexts. However, traditional evaluation methods may fail to combine real-time measures, an “objective” approach and data contextualization. In this review we look at how adding neuroimaging techniques can respond to such needs. We focus on electroencephalography (EEG), as it could be handled effectively during a dedicated evaluation phase. We identify workload, attention, vigilance, fatigue, error recognition, emotions, engagement, flow and immersion as being recognizable by EEG. We find that workload, attention and emotions assessments would benefit the most from EEG. Moreover, we advocate to study further error recognition through neuroimaging to enhance usability and increase user experience.
Paper Nr: 16
Title:
A Physiological Evaluation of Immersive Experience of a View Control Method using Eyelid EMG
Authors:
Masaki Omata, Satoshi Kagoshima and Yasunari Suzuki
Abstract: This paper describes that the number of blood-volume pulses (BVP) and the level of skin conductance (SC) increased more with increasing immersive impression with a view control method using eyelid electromyography in virtual environment (VE) than those with a mouse control method. We have developed the view control method and the visual feedback associated with electromyography (EMG) signals of movements of user’s eyelids. The method provides a user with more immersive experiences in a virtual environment because of strong relationship between eyelid movement and visual feedback. This paper reports a physiological evaluation experiment to compare it with a common mouse input method by measuring subjects’ physiological data of their fear of an open high place in a virtual environment. Based on the results, we find the eyelid-movement input method improves the user’s immersive impression more significantly than the mouse input method.
Area 12 – Devices
Short Papers
Paper Nr: 17
Title:
Flexible Pressure Mapping Platform for Mobility Monitoring Applications
Authors:
S. Cruz, D. Dias, J. C. Viana and L. A. Rocha
Abstract: The goal of the work presented here is the development, integration and testing of an innovative technological approach to be the basis for a new product and service for markets associated with the “Health” vector. Our research focuses on a Physiological computing approach, where a polymeric flexible detection system, working as the sensing element is used as an input channel, and a computing system is responsible for the physiological signals synthesis. The proposed solution provides a simpler, lower cost and larger scale manufacturing production of polymer based sensors, along with an electronic interface and the software design. The sensing platform consists in a flexible PCB (Printed Circuit Boards) manufactured using conventional technology (defining the electrical connections and the capacitors dimensions) together with two flexible polymeric membranes (TPU) printed with conductive ink (Plexcore®) for definition of the electrodes. A Capacitance to Digital Converter (CDC) is used to measure the capacitance of the sensors, and a graphical interface in MATLAB allows real-time visualization of data. Current results performed on the pressure sensors indicate the feasibility of the approach.
Area 13 – Methodologies and Methods
Short Papers
Paper Nr: 20
Title:
In-chair Movements of Healthy People during Prolonged Sitting
Authors:
Elisa Marenzi, Gian Mario Bertolotti and Giovanni Danese
Abstract: This paper describes a program designed to detect and give a classification of the in-chair movements done by healthy people while seated for long periods of time. The purpose of this work is to identify the frequency, duration and typology of movements performed by subjects that need to remain seated for a prolonged time. The software finds the time instants of each movement, its duration and whether it is in the sagittal or the lateral plane; in particular it distinguishes between a left and right movement (in the lateral plane) and a forward or backward trunk movement. This information can be useful in many different domains: first of all to monitor the fidgeting phenomenon and consequently the feeling of discomfort in the office environment; it can be adopted to evaluate the fatigue of car and truck drivers; but the most important outcome concerns the clinical setting, in which it can be very helpful for the medical staff in determining an appropriate and personalized rehabilitation strategy for patients with motor limitations in order to prevent the development of pressure ulcers.
Area 14 – Human Factors
Short Papers
Paper Nr: 28
Title:
Can Ultrasonic Doppler Help Detecting Nasality for Silent Speech Interfaces? – An Exploratory Analysis based on Alignement of the Doppler Signal with Velum Aperture Information from Real-Time MRI
Authors:
João Freitas, António Teixeira and Miguel Sales Dias
Abstract: This paper describes an exploratory analysis on the usefulness of the information made available from Ultrasonic Doppler signal data collected from a single speaker, to detect velum movement associated to European Portuguese nasal vowels. This is directly related to the unsolved problem of detecting nasality in silent speech interfaces. The applied procedure uses Real-Time Magnetic Resonance Imaging (RT-MRI), collected from the same speaker providing a method to interpret the reflected ultrasonic data. By ensuring compatible scenario conditions and proper time alignment between the Ultrasonic Doppler signal data and the RT-MRI data, we are able to accurately estimate the time when the velum moves and the type of movement under a nasal vowel occurrence. The combination of these two sources revealed a moderate relation between the average energy of frequency bands around the carrier, indicating a probable presence of velum information in the Ultrasonic Doppler signal.
Area 15 – Methodologies and Methods
Short Papers
Paper Nr: 29
Title:
Relevant Elderly Gait Features for Functional Fitness Level Grouping
Authors:
Marta S. Santos, Vera Moniz-Pereira, André Lourenço, Ana Fred and António P. Veloso
Abstract: Locomotor tasks characterization plays an important role in trying to improve the quality of life of a growing elderly population. This paper focuses on this matter by trying to characterize the locomotion of two population groups with different functional fitness levels (high or low) while executing three different tasks – gait, stair ascent and stair descent. Features were extracted from gait data, and feature selection methods were used in order to get the set of features that allow differentiation between functional fitness level. Unsupervised learning was used to validate the sets obtained and, ultimately, indicated that it is possible to distinguish the two population groups. The sets of best discriminate features for each task are identified and thoroughly analysed.
Area 16 – Devices
Short Papers
Paper Nr: 41
Title:
Precise 3D Deep Brain Stimulation Electrode Location based on Multimodal Neuroimage Fusion
Authors:
Nádia Moreira da Silva, Verena E. Rozanski, Sérgio Neves Tafula and João Paulo Silva Cunha
Abstract: The success of neurosurgery strongly depends on the pre-neurosurgical evaluation phase, in which the delineation of the areas to be removed or to be stimulated must be very accurate. For patients undergoing Deep Brain Stimulation (DBS) it is vital the delineation of the target area prior to surgery, and after the implantation of the DBS lead to confirm the electrodes positioning. In this paper we present a system to accurately determine the 3D position of DBS electrodes implanted within the brain of Parkinson and Dystonia patients. The system was tested using a multimodal dataset from 16 patients (8 with Parkinson`s disease and 8 with dystonia) and, on average, the differences between the detected electrodes positions and the ones estimated manually by an experienced physician were less than a voxel in all cases.
Area 17 – Human Factors
Short Papers
Paper Nr: 42
Title:
BITalino: A Novel Hardware Framework for Physiological Computing
Authors:
Hugo Plácido da Silva, José Guerreiro, André Lourenço, Ana Fred and Raúl Martins
Abstract: Physical computing has spun a true global revolution in the way in which the digital interfaces with the real world. From bicycle jackets with turn signal lights to twitter-controlled christmas trees, the Do-it-Yourself (DiY) hardware movement has been driving endless innovations and stimulating an age of creative engineering. This ongoing (r)evolution has been led by popular electronics platforms such as the Arduino, the Lilypad, or the Raspberry Pi, however, these are not designed taking into account the specific requirements of biosignal acquisition. To date, the physiological computing community has been severely lacking a parallel to that found in the DiY electronics realm, especially in what concerns suitable hardware frameworks. In this paper, we build on previous work developed within our group, focusing on an all-in-one, low-cost, and modular biosignal acquisition hardware platform, that makes it quicker and easier to build biomedical devices. We describe the main design considerations, experimental evaluation and circuit characterization results, together with the results from a usability study performed with volunteers from multiple target user groups, namely health sciences and electrical, biomedical, and computer engineering.
Area 18 – Methodologies and Methods
Short Papers
Paper Nr: 48
Title:
Does the Audience Hear My Heart? – Comparing the Physiological Responses of Listeners with Those of the Composer
Authors:
Benjamin Luke Evans, Nagisa Munekata and Tetsuo Ono
Abstract: Based on the assumption that composers compose with specific “intentions” in mind, we have conducted experiments to compare the “impressions” perceived by individual listeners with those “intentions” of the composer. We recorded physiological signals (skin conductance and finger tip temperature) of both the composer and listeners as they listened to the same music. Listener data was then cumulated and averaged for each song and compared to the data of the composer. Overall tendencies in physiological data, as well as a separate survey taken regarding emotions conveyed in the music, showed similarities between composer “intentions” and listener “impressions”, indicating positive possibilities for using physiological data as an objective index of composers in future studies.
Area 19 – Devices
Short Papers
Paper Nr: 51
Title:
A Telerehabilitation System based on Wireless Motion Capture Sensors
Authors:
Pedro Macedo, José A. Afonso, Luis A. Rocha and Ricardo Simoes
Abstract: The constant growth of the elderly population in the world creates new challenges and opportunities in health care systems. New technological solutions have to be found in order to meet the needs and demands of our aging society. The welfare and quality of life of the elderly population must be a priority. Continuous physical activity will play an important role, due to the increase of the retirement age. However, physiotherapy can be expensive, even when the desire movements are autonomous and simple, also requires people to move to rehabilitation centres. Within this context, this paper describes the development and preliminary tests of a wireless sensor network, based on wearable inertial and magnetic sensors, applied to the capture of human motion. This will enable a personalized home-based rehabilitation system for the elderly or people in remote physical locations.
Area 20 – Methodologies and Methods
Short Papers
Paper Nr: 52
Title:
Towards an Automatic Motion Coaching System – Feedback Techniques for Different Types of Motion Errors
Authors:
Norimichi Ukita, Daniel Kaulen and Carsten Röcker
Abstract: The development of a widely applicable automatic motion coaching system requires one to address a lot of issues including motion capturing, motion analysis and comparison, error detection as well as error feedback. In order to cope with this complexity, most existing approaches focus on a specific motion sequence or exercise. As a first step towards the development of a more generic system, this paper systematically analyzes different error and feedback types. A prototype of a feedback system that addresses multiple modalities is presented. The system allows to evaluate the applicability of the proposed feedback techniques for arbitrary types of motions in a next step.
Paper Nr: 54
Title:
Effectiveness of Three-Dimensional Kinematic Biofeedback on the Performance of Scapula-focused Exercises
Authors:
Ana Antunes, Inês Filipe, Sara Cordeiro, Joana Rosa, Filomena Carnide and Ricardo Matias
Abstract: Three-dimensional (3D) kinematic biofeedback can help identify scapular movement disorders and assist the subjects’ motor relearning process by facilitating changes in physiological and biomechanical function through real-time knowledge of performance and result during or immediately after a task execution. This study assessed the effectiveness of 3D kinematic biofeedback on the quality of the scapula-focused exercises execution, and motor learning transfer during shoulder flexion and a daily activity. Thirty healthy adults with no history of shoulder pain or dysfunction were randomly distributed into two groups. Skin-mounted sensors allowed tracking of the thorax, scapula and humerus, and scapulothoracic and glenohumeral 3D angles were computed after reconstructing upper-extremity motions during daily activities and exercises for different phases of a motor relearning process. The results of this study demonstrate that the execution quality of scapula-focused exercises benefits of real-time 3D kinematic biofeedback and that transfer of learning occurs with a specific motor training intervention.
Area 21 – Devices
Posters
Paper Nr: 39
Title:
Physiological Signal Processing for Emotional Feature Extraction
Authors:
Peng Wu, Dongmei Jiang and Hichem Sahli
Abstract: This paper introduces new approaches of physiological signal processing prior to feature extraction from electrocardiogram (ECG) and electromyography (EMG). Firstly a new signal denoising approach based on the Empirical mode decomposition (EMD) is presented. The EMD can decompose the noisy signal into a number of Intrinsic Mode Functions (IMFs). The proposed algorithm estimates the noise level of each IMF. Experiments show that the proposed EMD-based method provides better denoising resu…