503 research outputs found
New methods for stress assessment and monitoring at the workplace
The topic of stress is nowadays a very important one, not only in research but on social life in general. People are increasingly aware of this problem and its consequences at several levels: health, social life, work, quality of life, etc. This resulted in a significant increase in the search for devices and applications to measure and manage stress in real-time. Recent technological and scientific evolution fosters this interest with the development of new methods and approaches. In this paper we survey these new methods for stress assessment, focusing especially on those that are suited for the workplace: one of todayâs major sources of stress. We contrast them with more traditional methods and compare them between themselves, evaluating nine characteristics. Given the diversity of methods that exist nowadays, this work facilitates the stakeholdersâ decision towards which one to use, based on how much their organization values aspects such as privacy, accuracy, cost-effectiveness or intrusivenes
Affective games:a multimodal classification system
Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in playerâs psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation
Wearables and machine learning for improving runnersâ motivation from an affective perspective
Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletesâ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runnersâ emotions during their training. The solution is based on the analysis of runnersâ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runnersâ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training
Emotional Intelligence via Wearables: A Method for Detecting Frustration
Abstract:
The research discussed in this paper is part of a pilot study in the use of wearable devices incorporating electroencephalogram (EEG) and heartrate sensors to sense for the emotional responses closely correlated to frustration when performing certain tasks. For this study the methodology used a combination of puzzle, arcade style game and a meditation apps to emulate a task based environment and detect frustration and satisfaction emotions. Preliminary results indicate that degree of task completion has an effect on emotions and can be detected by EEG and heartrate changes.
Keywords
Emotional Intelligence; Wearable Devices; EEG; ECG; EQ; Heart Rate; Electroencephalogram
For More Details Visit: http://it-in-industry.com/itii_papers/2016/4116itii03.pd
An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations
Previous research has proven the strong influence of emotions on student engagement and
motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but
there is no standard method for predicting studentsâ affects. However, physiological signals have
been widely used in educational contexts. Some physiological signals have shown a high accuracy
in detecting emotions because they reflect spontaneous affect-related information, which is fresh
and does not require additional control or interpretation. Most proposed works use measuring
equipment for which applicability in real-world scenarios is limited because of its high cost and
intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost
and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using
both inter-subject and intra-subject models, we present an experimental study that aims to explore
the potential application of Hidden Markov Models (HMM) to predict the concentration state from
4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin
temperature. We also study the effect of combining these four signals and analyse their potential use
in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high
accuracy can be achieved with three of the signals when using HMM-based intra-subject models.
However, inter-subject models, which are meant to obtain subject-independent approaches for affect
detection, fail at the same task.This research was partly supported by Spanish Ministry of Science, Innovation and Universities through projects PGC2018-096463-B-I00 and PGC2018-102279-B-I00 (MCIU/AEI/FEDER, UE)
Towards a robotic personal trainer for the elderly
The use of robots in the environment of the elderly has grown significantly in recent years. The idea is to try to increase the comfort and well-being of older people through the employment of some kind of automated processes that simplify daily work. In this paper we present a prototype of a personal robotic trainer which, together with a non-invasive sensor, allows caregivers to monitor certain physical activities in order to improve their performance. In addition, the proposed system also takes into account how the person feels during the performance of the physical exercises and thus, determine more precisely if the exercise is appropriate or not for a specific person.This work was partly supported by the Spanish Government (RTI2018-095390-B-C31) and FCTâFundação para a CiĂȘncia e Tecnologia through the Post-Docscholarship SFRH/BPD/102696/2014 (A. Costa) and UID/CEC/00319/2019
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