H. Australasia, Available, 2010.

F. Abdat, C. Maaoui, and A. Pruski, Bimodal System for Emotion Recognition from Facial Expressions and Physiological Signals Using Feature-Level Fusion, 2011 UKSim 5th European Symposium on Computer Modeling and Simulation, pp.24-29, 2011.
DOI : 10.1109/EMS.2011.21

S. Mobyen-uddin-ahmed, M. Begum, and . Islam, Heart rate and interbeat interval computation to diagnose stress using ecg sensor signal, MRTC Report, 2010.

A. Akbas, Evaluation of the physiological data indicating the dynamic stress level of drivers. Scientific research and essays, pp.430-439, 2011.

A. Alberdi, A. Aztiria, and A. Basarab, Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review, Journal of Biomedical Informatics, vol.59, pp.49-75, 2016.
DOI : 10.1016/j.jbi.2015.11.007

URL : https://hal.archives-ouvertes.fr/hal-01272036

M. Alzantot and M. Youssef, UPTIME: Ubiquitous pedestrian tracking using mobile phones, 2012 IEEE Wireless Communications and Networking Conference (WCNC), pp.3204-3209, 2012.
DOI : 10.1109/WCNC.2012.6214359

J. Ang, R. Dhillon, A. Krupski, E. Shriberg, and A. Stolcke, Prosody-based automatic detection of annoyance and frustration in human-computer dialog, INTERSPEECH. Citeseer, 2002.

D. Anguita, A. Boni, and S. Ridella, A digital architecture for support vector machines: theory, algorithm, and fpga implementation, IEEE Transactions on Neural Networks, vol.14, issue.5, pp.993-1009, 2003.
DOI : 10.1109/TNN.2003.816033

D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. , Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine, International Workshop on Ambient Assisted Living, pp.216-223, 2012.
DOI : 10.1007/978-3-642-35395-6_30

A. Armato, E. Nardini, A. Lanata, G. Valenza, C. Mancuso et al., An FPGA Based Arrhythmia Recognition System for Wearable Applications, 2009 Ninth International Conference on Intelligent Systems Design and Applications, pp.660-664, 2009.
DOI : 10.1109/ISDA.2009.246

S. Aslan, E. Oruklu, and J. Saniie, Realization of area efficient QR factorization using unified division, square root, and inverse square root hardware, 2009 IEEE International Conference on Electro/Information Technology, pp.245-250, 2009.
DOI : 10.1109/EIT.2009.5189620

D. Alan and . Baddeley, Selective attention and performance in dangerous environments, British journal of psychology, vol.63, issue.4, pp.537-546, 1972.

J. Bakker, M. Pechenizkiy, and N. Sidorova, What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data, 2011 IEEE 11th International Conference on Data Mining Workshops, pp.573-580, 2011.
DOI : 10.1109/ICDMW.2011.178

J. Beuchat and A. Tisserand, Small Multiplier-Based Multiplication and Division Operators for Virtex-II Devices, International Conference on Field Programmable Logic and Applications, pp.513-522, 2002.
DOI : 10.1007/3-540-46117-5_54

URL : https://hal.archives-ouvertes.fr/inria-00072094

B. Bolmont, F. Thullier, H. Jacques, and . Abraini, Relationships between mood states and performances in reaction time, psychomotor ability, and mental efficiency during a 31-day gradual decompression in a hypobaric chamber from sea level to 8848 m equivalent altitude, Physiology & Behavior, vol.71, issue.5, pp.71469-476, 2000.
DOI : 10.1016/S0031-9384(00)00362-0

F. Bousefsaf, C. Maaoui, and A. Pruski, Remote assessment of the Heart Rate Variability to detect mental stress, Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques
DOI : 10.4108/icst.pervasivehealth.2013.252181

URL : https://hal.archives-ouvertes.fr/hal-01379403

M. Bradley, J. Peter, and . Lang, The International affective digitized sounds (IADS)[: stimuli, instruction manual and affective ratings. NIMH Center for the Study of Emotion and Attention, 1999.

M. Bsoul, H. Minn, and L. Tamil, Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG, IEEE Transactions on Information Technology in Biomedicine, vol.15, issue.3, pp.416-427, 2011.
DOI : 10.1109/TITB.2010.2087386

URL : http://www.utdallas.edu/~hxm025000/MBsoul_MTCIHC_10.pdf

A. Burns, R. Barry, . Greene, J. Michael, . Mcgrath et al., SHIMMER??? ??? A Wireless Sensor Platform for Noninvasive Biomedical Research, IEEE Sensors Journal, vol.10, issue.9, pp.1527-1534, 2010.
DOI : 10.1109/JSEN.2010.2045498

T. John, . Cacioppo, G. Louis, and . Tassinary, Inferring psychological significance from physiological signals, American Psychologist, vol.45, issue.1, p.16, 1990.

A. Calderbank and I. Daubechies, Wavelet Transforms That Map Integers to Integers, Applied and Computational Harmonic Analysis, vol.5, issue.3, pp.332-369, 1998.
DOI : 10.1006/acha.1997.0238

T. Chandola, E. Brunner, and M. Marmot, Chronic stress at work and the metabolic syndrome: prospective study, BMJ, vol.332, issue.7540, pp.521-525, 2006.
DOI : 10.1136/bmj.38693.435301.80

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, p.27, 2011.
DOI : 10.1145/1961189.1961199

S. Lawrence, . Chen, S. Thomas, T. Huang, R. Miyasato et al., Multimodal human emotion/expression recognition, Automatic Face and Gesture Recognition Proceedings. Third IEEE International Conference on, pp.366-371, 1998.

S. Cohen, T. Kamarck, and R. Mermelstein, A Global Measure of Perceived Stress, Journal of Health and Social Behavior, vol.24, issue.4, pp.385-396, 1983.
DOI : 10.2307/2136404

W. Thomas, . Colligan, M. Eileen, and . Higgins, Workplace stress: Etiology and consequences, Journal of workplace behavioral health, vol.21, issue.2, pp.89-97, 2006.

A. Stephen, T. Coombes, . Higgins, M. Kelly, . Gamble et al., Attentional control theory: Anxiety, emotion, and motor planning, Journal of Anxiety Disorders, vol.23, issue.8, pp.1072-1079, 2009.

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

C. Mullan-crain, K. Kroeker, S. Benjamin, and . Halpern, Interactive and cumulative effects of multiple human stressors in marine systems, Ecology Letters, vol.104, issue.12, pp.1304-1315, 2008.
DOI : 10.4319/lo.2000.45.5.1144

V. Christopher, . Dayas, M. Kathryn, . Buller, W. James et al., Stressor categorization: acute physical and psychological stressors elicit distinctive recruitment patterns in the amygdala and in medullary noradrenergic cell groups, European Journal of Neuroscience, vol.14, issue.7, pp.1143-1152, 2001.

C. Liyanage, P. C. Silva, and . Ng, Bimodal emotion recognition, Automatic Face and Gesture Recognition Proceedings. Fourth IEEE International Conference on, pp.332-335, 2000.

Z. Dharmawan and L. Rothkrantz, Analysis of computer games player stress level using eeg data, 2007.

F. David, . Dinges, L. Robert, J. Rider, . Dorrian et al., Optical computer recognition of facial expressions associated with stress induced by performance demands, Aviation, space, and environmental medicine, issue.6, pp.76-172, 2005.

D. Janessa, . Drake, P. Jack, and . Callaghan, Elimination of electrocardiogram contamination from electromyogram signals: an evaluation of currently used removal techniques, Journal of electromyography and kinesiology, vol.16, issue.2, pp.175-187, 2006.

R. Fernandez and R. W. Picard, Modeling drivers??? speech under stress, Speech Communication, vol.40, issue.1-2, pp.145-159, 2003.
DOI : 10.1016/S0167-6393(02)00080-8

R. W. Poh and . Picard, icalm: Wearable sensor and network architecture for wirelessly communicating and logging autonomic activity, Information Technology in Biomedicine IEEE Transactions on, vol.14, issue.2, pp.215-223, 2010.

J. Frank, S. Mannor, and D. Precup, Activity Recognition with Mobile Phones, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.630-633, 2011.
DOI : 10.1007/11748625_1

H. Nico and . Frijda, The emotions: Studies in emotion and social interaction, 1986.

N. Prerana, A. Gawale, . Cheeran, G. Nidhi, and . Sharma, Android application for ambulant ecg monitoring, International Journal of Advanced Research in Computer and Communication Engineering, vol.3, issue.5, pp.6465-6468, 2014.

S. Ghedira, P. Pino, and G. Bourhis, Conception and Experimentation of a Communication Device with Adaptive Scanning, ACM Transactions on Accessible Computing, vol.1, issue.3, p.14, 2009.
DOI : 10.1145/1497302.1497304

URL : https://hal.archives-ouvertes.fr/hal-01313007

B. Joao, S. Gomes, M. M. Krishnaswamy, . Gaber, A. Pedro et al., Mars: a personalised mobile activity recognition system, 2012 IEEE 13th International Conference on Mobile Data Management, pp.316-319, 2012.

K. Gopalan, On the effect of stress on certain modulation parameters of speech, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), pp.101-104, 2001.
DOI : 10.1109/ICASSP.2001.940777

R. Grasse, Y. Morère, and A. Pruski, Aided navigation for disabled people: route recognition with augmented hmms, 8th Conf. for the Advancement of Assistive Technology, 2005.

M. Haak, S. Bos, S. Panic, and L. Rothkrantz, Detecting stress using eye blinks and brain activity from eeg signals. Proceeding of the 1st driver car interaction and interface, pp.35-60, 2008.

J. Hainaut and B. Bolmont, Effects of Mood States and Anxiety as Induced by the Video-Recorded Stroop Color-Word Interference Test in Simple Response Time Tasks on Reaction Time and Movement Time, Perceptual and Motor Skills, vol.20, issue.3, pp.721-729, 2005.
DOI : 10.1002/cne.920180503

W. Handouzi, C. Maaoui, A. Pruski, and A. Moussaoui, Objective model assessment for short-term anxiety recognition from blood volume pulse signal, Biomedical Signal Processing and Control, vol.14, pp.217-227, 2014.
DOI : 10.1016/j.bspc.2014.07.008

URL : https://hal.archives-ouvertes.fr/hal-01323831

H. John, S. Hansen, and . Patil, Speech under stress: Analysis, modeling and recognition, Speaker classification I, pp.108-137, 2007.

A. Jennifer, R. W. Healey, and . Picard, Detecting stress during real-world driving tasks using physiological sensors. Intelligent Transportation Systems, IEEE Transactions on, vol.6, issue.2, pp.156-166, 2005.

J. Hernandez, R. R. Morris, and R. W. Picard, Call Center Stress Recognition with Person-Specific Models, International Conference on Affective Computing and Intelligent Interaction, pp.125-134, 2011.
DOI : 10.1109/TITB.2009.2036164

E. Hoffmann, Brain training against stress: Theory, methods and results from an outcome study, Stress Report, vol.4, issue.2, pp.1-24, 2005.

S. Clifford, . Hopkins, J. Roy, . Ratley, S. Daniel et al., Evaluation of voice stress analysis technology, System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, pp.20-20, 2005.

M. A. Seyyed-abed-hosseini, M. Khalilzadeh, S. Bagher-naghibi-sistani, and . Homam, Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals, Iranian journal of neurology, vol.14, issue.3, p.142, 2015.

M. Huiku, . Uutela, . Van-gils, . Korhonen, . Kymäläinen et al., Assessment of surgical stress during general anaesthesia, British Journal of Anaesthesia, vol.98, issue.4, pp.447-455, 2007.
DOI : 10.1093/bja/aem004

K. Irick, M. Debole, V. Narayanan, and A. Gayasen, A Hardware Efficient Support Vector Machine Architecture for FPGA, 2008 16th International Symposium on Field-Programmable Custom Computing Machines, pp.304-305, 2008.
DOI : 10.1109/FCCM.2008.40

M. Jabon, J. Bailenson, E. Pontikakis, L. Takayama, and C. Nass, Facial expression analysis for predicting unsafe driving behavior, IEEE Pervasive Computing, vol.10, issue.4, pp.84-95, 2011.
DOI : 10.1109/MPRV.2010.46

E. Jang, . Park, C. Kim, Y. Huh, J. Eum et al., Emotion recognition through ans responses evoked by negative emotions, The Fifth International Conference on Advances in Computer-Human Interactions (ACHI), pp.218-223, 2012.

E. Jovanov, O. Amanda, D. Lords, . Raskovic, G. Paul et al., Stress monitoring using a distributed wireless intelligent sensor system, IEEE Engineering in Medicine and Biology Magazine, vol.22, issue.3, pp.2249-55, 2003.
DOI : 10.1109/MEMB.2003.1213626

S. Jung and W. Chung, Wide and high accessible mobile healthcare system in IP-based wireless sensor networks, 2013 IEEE SENSORS, pp.1-4, 2013.
DOI : 10.1109/ICSENS.2013.6688326

M. Karim, Novel simple decision stage of pan & tompkins qrs detector and its fpga-based implementation, Innovative Computing Technology (INTECH), 2012 Second International Conference on, pp.331-336, 2012.

N. Katenka, E. Levina, and G. Michailidis, Local Vote Decision Fusion for Target Detection in Wireless Sensor Networks, IEEE Transactions on Signal Processing, vol.56, issue.1, pp.329-338, 2008.
DOI : 10.1109/TSP.2007.900165

S. Nikolaos, C. D. Katertsidis, D. I. Katsis, and . Fotiadis, Intrepid, a biosignal-based system for the monitoring of patients with anxiety disorders, Information Technology and Applications in Biomedicine 9th International Conference on, pp.1-6, 2009.

G. Kavya and V. Thulasibai, Vlsi implementation of telemonitoring system for high risk cardiac patients, Indian Journal of Science and Technology, vol.7, issue.5, p.571, 2014.

J. Kim and E. André, Emotion recognition based on physiological changes in music listening. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.30, issue.12, pp.2067-2083, 2008.

M. Kose, O. D. Incel, and C. Ersoy, Online human activity recognition on smart phones, Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data, pp.11-15, 2012.

R. Jennifer, . Kwapisz, M. Gary, . Weiss, A. Samuel et al., Activity recognition using cell phone accelerometers, ACM SigKDD Explorations Newsletter, vol.12, issue.2, pp.74-82, 2011.

L. Lam and S. Suen, Application of majority voting to pattern recognition: an analysis of its behavior and performance, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.27, issue.5, pp.553-568, 1997.
DOI : 10.1109/3468.618255

B. Lee and N. Burgess, Improved small multiplier based multiplication, squaring and division, 11th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, 2003. FCCM 2003., pp.91-97, 2003.
DOI : 10.1109/FPGA.2003.1227245

J. Lee, B. Noh, S. Jang, D. Park, Y. Chung et al., Stress detection and classification of laying hens by sound analysis. Asian- Australasian journal of animal sciences, pp.592-598, 2015.

I. Lefter, J. Gertjan, . Burghouts, J. Léon, and . Rothkrantz, Recognizing Stress Using Semantics and Modulation of Speech and Gestures, IEEE Transactions on Affective Computing, vol.7, issue.2, pp.162-175, 2016.
DOI : 10.1109/TAFFC.2015.2451622

S. Jennifer, . Lerner, E. Ronald, . Dahl, R. Ahmad et al., Facial expressions of emotion reveal neuroendocrine and cardiovascular stress responses, Biological psychiatry, vol.61, issue.2, pp.253-260, 2007.

W. Liao, W. Zhang, Z. Zhu, and Q. Ji, A real-time human stress monitoring system using dynamic bayesian network, Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on, pp.70-70, 2005.

T. Lin and L. John, Quantifying mental relaxation with eeg for use in computer games, International conference on internet computing, pp.409-415, 2006.

A. Liu and D. Salvucci, Modeling and prediction of human driver behavior, Intl. Conference on HCI, 2001.

G. Stephane and . Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE transactions on pattern analysis and machine intelligence, vol.11, issue.7, pp.674-693, 1989.

J. Manikandan, V. Venkataramani, and . Avanthi, FPGA Implementation of Support Vector Machine Based Isolated Digit Recognition System, 2009 22nd International Conference on VLSI Design, pp.347-352, 2009.
DOI : 10.1109/VLSI.Design.2009.23

J. Ryan, . Marker, S. Katrina, and . Maluf, Effects of electrocardiography contamination and comparison of ecg removal methods on upper trapezius electromyography recordings, Journal of Electromyography and Kinesiology, vol.24, issue.6, pp.902-909, 2014.

A. Martin, Fusion de classifieurs pour la classification d'images sonar. arXiv preprint arXiv:0806, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00286591

B. Massot, N. Baltenneck, C. Gehin, A. Dittmar, and E. Mcadams, EmoSense: An Ambulatory Device for the Assessment of ANS Activity???Application in the Objective Evaluation of Stress With the Blind, IEEE Sensors Journal, vol.12, issue.3, pp.543-551, 2012.
DOI : 10.1109/JSEN.2011.2132703

B. Iris, . Mauss, D. Michael, and . Robinson, Measures of emotion: A review, Cognition and emotion, vol.23, issue.2, pp.209-237, 2009.

F. Michael, E. E. Labbé, and A. R. Kuczmierczyk, Health psychology: A psychobiological perspective, 2013.

I. Mohino-herranz, R. Gil-pita, J. Ferreira, M. Rosa-zurera, and F. Seoane, Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones, Sensors, vol.31, issue.10, pp.1525607-25627, 2015.
DOI : 10.1016/j.bspc.2013.01.007

E. Monmasson, N. Marcian, and . Cirstea, FPGA Design Methodology for Industrial Control Systems???A Review, IEEE Transactions on Industrial Electronics, vol.54, issue.4, pp.1824-1842, 2007.
DOI : 10.1109/TIE.2007.898281

J. Naveteur, Chronic pain and electrodermal activity, Douleur et Analg??sie, vol.21, issue.2, pp.81-85, 2008.
DOI : 10.1007/s11724-008-0085-4

J. Timothy-noteboom, R. Kerry, . Barnholt, M. Roger, and . Enoka, Activation of the arousal response and impairment of performance increase with anxiety and stressor intensity, Journal of Applied Physiology, vol.20, issue.5, pp.2093-2101, 2001.
DOI : 10.1080/00222895.1978.10735150

D. Novák, L. Lhotská, V. Eck, and M. Sorf, Eeg and vep signal processing, Cybernetics, Faculty of Electrical Eng, pp.50-53, 2004.

T. Lay-nwe, S. W. Foo, and L. Silva, Speech emotion recognition using hidden markov models, Speech communication, vol.41, issue.4, pp.603-623, 2003.

J. Oster, J. Behar, R. Colloca, Q. Li, Q. Li et al., Open source java-based ecg analysis software and android app for atrial fibrillation screening, Computing in Cardiology 2013, pp.731-734, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01737144

J. Pan, J. Willis, and . Tompkins, A Real-Time QRS Detection Algorithm, IEEE Transactions on Biomedical Engineering, vol.32, issue.3, pp.230-236, 1985.
DOI : 10.1109/TBME.1985.325532

M. Pantic, J. Leon, and . Rothkrantz, Toward an affect-sensitive multimodal humancomputer interaction, Proceedings of the IEEE, pp.1370-1390, 2003.

M. Papadonikolakis and C. Bouganis, A scalable FPGA architecture for non-linear SVM training, 2008 International Conference on Field-Programmable Technology, pp.337-340, 2008.
DOI : 10.1109/FPT.2008.4762412

M. Papadonikolakis and C. Bouganis, A novel FPGA-based SVM classifier, 2010 International Conference on Field-Programmable Technology, pp.283-286, 2010.
DOI : 10.1109/FPT.2010.5681485

T. Partala and V. Surakka, Pupil size variation as an indication of affective processing, International Journal of Human-Computer Studies, vol.59, issue.1-2, pp.185-198, 2003.
DOI : 10.1016/S1071-5819(03)00017-X

M. Abhilasha, . Patel, K. Pankaj, A. Gakare, and . Cheeran, Real time ecg feature extraction and arrhythmia detection on a mobile platform, Int. J. Comput. Appl, issue.23, pp.4440-4485, 2012.

C. Pavlatos, A. Dimopoulos, G. Manis, and . Papakonstantinou, Hardware implementation of pan & tompkins qrs detection algorithm, Proceedings of the EMBEC05 Conference. Citeseer, 2005.

W. Scott and P. , Pupil size, information overload, and performance differences, Psychophysiology, vol.11, issue.5, pp.559-566, 1974.

A. Pentland and A. Liu, Modeling and Prediction of Human Behavior, Neural Computation, vol.83, issue.1, pp.229-242, 1999.
DOI : 10.1109/3468.553220

W. Rosalind, E. Picard, J. Vyzas, and . Healey, Toward machine emotional intelligence: Analysis of affective physiological state. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.23, issue.10, pp.1175-1191, 2001.

C. John and . Platt, 12 fast training of support vector machines using sequential minimal optimization Advances in kernel methods, pp.185-208, 1999.

J. Rand, A. Hoover, S. Fishel, J. Moss, J. Pappas et al., Real-Time Correction of Heart Interbeat Intervals, IEEE Transactions on Biomedical Engineering, vol.54, issue.5, pp.946-950, 2007.
DOI : 10.1109/TBME.2007.893491

P. Rani, J. Sims, R. Brackin, and N. Sarkar, Online stress detection using psychophysiological signals for implicit human-robot cooperation, Robotica, vol.20, issue.06, pp.673-685, 2002.
DOI : 10.1017/S0263574702004484

A. Thomas, E. Ranney, R. Mazzae, . Garrott, J. Michael et al., Nhtsa driver distraction research: Past, present, and future, Driver distraction internet forum, 2000.

S. Reisman, Measurement of physiological stress, Proceedings of the IEEE 23rd Northeast Bioengineering Conference, pp.21-23, 1997.
DOI : 10.1109/NEBC.1997.594939

A. Riener, A. Ferscha, and M. Aly, Heart on the road, Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI '09, pp.99-106, 2009.
DOI : 10.1145/1620509.1620529

G. Rigas and Y. Goletsis, Panagiota Bougia, and Dimitrios I Fotiadis. Towards driver's state recognition on real driving conditions, International Journal of Vehicular Technology, 2011.

M. Rossi, S. Feese, O. Amft, N. Braune, S. Martis et al., AmbientSense: A real-time ambient sound recognition system for smartphones, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp.230-235, 2013.
DOI : 10.1109/PerComW.2013.6529487

J. Leon, P. Rothkrantz, J. Wiggers, R. J. Van-wees, and . Van-vark, Voice stress analysis, International conference on text, speech and dialogue, pp.449-456, 2004.

A. Said, A. William, and . Pearlman, An image multiresolution representation for lossless and lossy compression, IEEE Transactions on Image Processing, vol.5, issue.9, pp.1303-1310, 1996.
DOI : 10.1109/83.535842

L. Salahuddin and D. Kim, Detection of acute stress by heart rate variability (hrv) using a prototype mobile ecg sensor, International Conference on Hybrid Information Technology (ICHiT/06), pp.453-459, 2006.

S. Scherer, H. Hofmann, M. Lampmann, M. Pfeil, and S. Rhinow, Friedhelm Schwenker, and Günther Palm. Emotion recognition from speech: Stress experiment, LREC, 2008.

H. Selye, The stress of life, 1956.

N. Sharma and T. Gedeon, Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer methods and programs in biomedicine, pp.1287-1301, 2012.

P. Siirtola and J. Röning, Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data, International Journal of Interactive Multimedia and Artificial Intelligence, vol.1, issue.5, pp.38-45, 2012.
DOI : 10.9781/ijimai.2012.155

G. Stemmler, M. Heldmann, C. A. Pauls, and T. Scherer, Constraints for emotion specificity in fear and anger: The context counts, Psychophysiology, vol.38, issue.2, pp.275-291, 2001.
DOI : 10.1111/1469-8986.3820275

R. Stojanovi´cstojanovi´c, . Karadagli´ckaradagli´c, D. Mirkovi´cmirkovi´c, and . Milo?evi´cmilo?evi´c, A fpga system for qrs complex detection based on integer wavelet transform, Measurement Science Review, vol.11, issue.4, pp.131-138, 2011.

J. Ridley and S. , Studies of interference in serial verbal reactions, Journal of experimental psychology, vol.18, issue.6, p.643, 1935.

V. N. Vapnik and V. Vapnik, Statistical learning theory, 1998.

K. Nishchal, S. Verma, . Singh, K. Jayesh, . Gupta et al., Smartphone application for fault recognition, Sensing Technology (ICST), 2012 Sixth International Conference on, pp.1-6, 2012.

A. Michael, M. Vidulich, M. Stratton, G. Crabtree, and . Wilson, Performancebased and physiological measures of situational awareness, Aviation, Space, and Environmental Medicine, 1994.

J. L. and P. Wijsman, Sensing stress: stress detection from physiological variables in controlled and uncontrolled conditions, 2014.

P. Wittels, B. Johannes, R. Enne, K. Kirsch, and H. Gunga, Voice monitoring to measure emotional load during short-term stress, European Journal of Applied Physiology, vol.87, issue.3, pp.278-282, 2002.
DOI : 10.1007/s00421-002-0625-1

Z. Wu, W. Fu, R. Xue, and W. Wang, A Novel Line Space Voting Method for Vanishing-Point Detection of General Road Images, Sensors, vol.32, issue.7, p.948, 2016.
DOI : 10.1109/TPAMI.2008.300

Y. Yoshitomi, S. Kim, T. Kawano, and T. Kilazoe, Effect of sensor fusion for recognition of emotional states using voice, face image and thermal image of face, Proceedings 9th IEEE International Workshop on Robot and Human Interactive Communication. IEEE RO-MAN 2000 (Cat. No.00TH8499), pp.178-183, 2000.
DOI : 10.1109/ROMAN.2000.892491

Z. Zeng, J. Tu, M. Liu, S. Thomas, B. Huang et al., Audio-Visual Affect Recognition, IEEE Transactions on Multimedia, vol.9, issue.2, pp.424-428, 2007.
DOI : 10.1109/TMM.2006.886310

J. Zhai and A. Barreto, Stress recognition using non-invasive technology, FLAIRS Conference, pp.395-401, 2006.