Scientific Publications

Abdallah, S., Wilkinson-Maitland, C., Waskiw-Ford, M., Abdallah, I., Lui, A., Smith, B., Bourbeau, J., & Jensen, D. (2017). Late Breaking Abstract - Validation of Hexoskin biometric technology to monitor ventilatory responses at rest and during exercise in COPD. European Respiratory Journal, 50(Suppl. 61), PA1359. https://doi.org/10.1183/1393003.congress-2017.PA1359

Schneider, B. A. (2022). Building an understanding of human activities in first person video using fuzzy inference [Doctoral dissertation, Wright State University]. https://etd.ohiolink.edu/acprod/odb_etd/r/etd/search/10?p10_accession_num=wright1647342586221469&clear=10&session=113631612178441

Abed, M., & Ibrikci, T. (2023). Sleep apnea events detection using deep learning techniques. Journal of Sleep Disorders: Treatment and Care, 12(1), 101. https://doi.org/10.4172/2325-9639.23.12.101

Breen, M., Reed, T., Breen, H. M., Osborne, C. T., & Breen, M. S. (2022). Integrating wearable sensors and video to determine microlocation-specific physiologic and motion biometrics-method development for competitive climbing. Sensors, 22, Article 6271. https://doi.org/10.3390/s22166271

Abid, M., Khabou, A., Ouakrim, Y., Watel, H., Chemcki, S., Mitiche, A., Benazza-Benyahia, A., & Mezghani, N. (2021). Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers. Sensors, 21(14), 4713. https://doi.org/10.3390/s21144713

Hopenfeld, B. (2022). Peak space motion artifact cancellation applied to textile electrode waist electrocardiograms recorded during outdoors walking and jogging [Preprint]. bioRxiv. https://doi.org/10.1101/2022.01.07.475456

Ahmed, S., Deneke, W., Mai, V., Veneruso, A., Stepita, M., Dawson, A., Hoefel, B., Claeys, G., Lam, N., & Sharmin, M. (2020). InterViewR: A Mixed-Reality Based Interview Training Simulation Platform for Individuals with Autism. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 439-448). IEEE. https://doi.org/10.1109/COMPSAC48688.2020.0-211

Rogers, B. (2020). A personalized low intensity exercise prescription based on an index of non-linear heart rate variability: A case report. Journal of Athletic Enhancement, 9(1). https://doi.org/10.37532/jae.2020.9(1).327

Ahn, H., & Ko, J. (2022). The Effect of Olfactory Inhalation on KPGA Golfers’ Putting Performance, Postural Stability and Heart Rate. International Journal of Environmental Research and Public Health, 19(19), 12666. https://doi.org/10.3390/ijerph191912666

Brunet, J., Wurz, A., Hussien, J., Pitman, A., Conte, E., Ennis, J. K., Herry, C. L., Seely, A. J. E., & Seely, D. (2022). Exploring the effects of yoga therapy on heart rate variability and patient-reported outcomes after cancer treatment: A study protocol. Integrative Cancer Therapies, 21, 1–13. https://doi.org/10.1177/15347354221075576

Mujaddid, A. (2023). Responsiveness of the Hexoskin Smart Shirt to detect pharmacological lung volume reduction at rest and during exercise in people with chronic obstructive pulmonary disease (COPD) [Master's thesis, McGill University]. eScholarship@McGill. https://doi.org/10.82308/3155

Brunet, J., Hussien, J., Pitman, A., Wurz, A., Conte, E., Polskaia, N., & Seely, D. (2024). Yoga therapy as an intervention to improve patient-reported outcomes among adults after treatment for cancer: Preliminary findings from a trial using single-subject experimental design. Integrative Cancer Therapies, 23, 1–14. https://doi.org/10.1177/15347354241233517

Bull, R., Wong, S., MacQuarrie, A., Katrakazos, E., Cumming, T., & McFarland, L. (2026). Early childhood educators’ well-being and physiological stress in the workplace: An exploratory study utilizing wearable technology. International Journal of Stress Management, 33(1), 55–66. https://doi.org/10.1037/str0000381

Alalool, A., Dubeau, S., Shu, Y., Roy, J.-F., Fournier, P.-A., Crystal, E., & Cheung, C. (2024). Comparative Analysis of a Wearable Electrocardiograph Reporting Technology and Holter Monitor in Cardiac Monitoring: A Prospective Study. Canadian Journal of Cardiology, 40, S67. https://doi.org/10.1016/j.cjca.2024.08.104

Buregeya, J. M., Apparicio, P., & Gelb, J. (2020). Short-term impact of traffic-related particulate matter and noise exposure on cardiac function. International Journal of Environmental Research and Public Health, 17(4), Article 1220. https://doi.org/10.3390/ijerph17041220

MacQuarrie, A., Sidhu, J., Deetlefs, C., Whitfield, S., & Stainer, M. (2024). Establishing the Validity and Reliability of the Astroskin® Biometric Shirt. Topics in Exercise Science and Kinesiology, 5(1), Article 4. https://doi.org/10.9741/2766-2187.1078

Burnett, R. A., Matheson, D. J., Kapron, C. R., Blackburn, B. E., Gililland, J. M., Anderson, L. A., Pelt, C. E., Peters, C. L., & Archibeck, M. J. (2025). Conversion total knee and hip arthroplasty requires increased energy expenditure compared to primary knee arthroplasty. The Journal of Arthroplasty. Advance online publication. https://doi.org/10.1016/j.arth.2025.11.003

Sherman, A. R. (2015). System Safety Analysis of the Opelika Fire Department's Crawling Simulator [Master's thesis, Auburn University]. Auburn University Electronic Theses and Dissertations. https://etd.auburn.edu/handle/10415/4901

Burr, L., Šula, J., Mayrhauser, J., & Meschtscherjakov, A. (2023). BREATHTURES: A first step towards breathing gestures as distinct input modality. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Article 60, pp. 1–6). ACM. https://doi.org/10.1145/3544549.3585787

Alizadeh, M., Shaker, G., De Almeida, J. C. M., Morita, P. P., & Safavi-Naeini, S. (2019). Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar. IEEE Access, 7, 54958-54968. https://doi.org/10.1109/ACCESS.2019.2912956

Landry, C. (2019). The association of uncertainty tolerance and task appraisal on law enforcement student stress and performance [Bachelor of Arts (Honours) thesis, University of Regina]. https://uregina.scholaris.ca/items/6a65cdf5-af44-4342-b963-dbc61194c893

Alizadeh, M., Shaker, G., & Safavi-Naeini, S. (2020). Remote Health Monitoring System for Bedbound Patients. In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 801-806). IEEE. https://doi.org/10.1109/BIBE50027.2020.00136

Calbert, L., & Hashemi Tonekaboni, N. (2024). Temporal dynamics of classroom stress: Insights from wearable sensors and machine learning. In 2024 International Conference on Machine Learning and Applications (ICMLA) (pp. 377–384). IEEE. https://doi.org/10.1109/ICMLA61862.2024.00057

Alkurdi, A., He, M., Cerna, J., Clore, J., Sowers, R., Hsiao-Wecksler, E. T., & Hernandez, M. E. (2025). Extending Anxiety Detection from Multimodal Wearables in Controlled Conditions to Real-World Environments. Sensors, 25(4), 1241. https://doi.org/10.3390/s25041241

MacQuarrie, A. J. (2019). Fit for duty: Context and correlates of paramedic health status and job performance [Doctoral dissertation, Charles Sturt University]. Charles Sturt University Research Output. https://researchoutput.csu.edu.au/en/publications/fit-for-duty-context-and-correlates-of-paramedic-health-status-an

Simons, A. (2021). Trust in Human Activity Recognition Deep Learning Models [Master's thesis, McMaster University]. MacSphere. https://macsphere.mcmaster.ca/handle/11375/27039

Campbell, K., Neul, J. L., Lieberman, D. N., Berry‑Kravis, E., Benke, T. A., Fu, C., Percy, A., Suter, B., Morris, D., Carpenter, R. L., Marsh, E. D., & von Hehn, J. (2025). A randomized, placebo‑controlled, cross‑over trial of ketamine in Rett syndrome. Journal of Neurodevelopmental Disorders, 17, Article 4. https://doi.org/10.1186/s11689-025-09591-y

Amelard, R., Hedge, E. T., & Hughson, R. L. (2021). Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities. NPJ Digital Medicine, 4(1), 156. https://doi.org/10.1038/s41746-021-00531-3

Carlson, L. A., Lawrence, M. A., & Kenefick, R. W. (2018). Hydration status and thermoregulatory responses in drivers during competitive racing. Journal of Strength and Conditioning Research, 32(7), 2061–2065. https://doi.org/10.1519/jsc.0000000000002268

Côté Bélisle, A. (2021). Rétroaction biologique et développement de la régulation émotionnelle et de la conscience émotionnelle chez les jeunes adultes ayant un TDAH [Doctoral dissertation, Université de Sherbrooke]. https://usherbrooke.scholaris.ca/items/2701469d-0f18-43ed-b88c-793cbf23c085

Caroll, T., Hernandez, G., Koutitas, G., Wierschem, D., Mendez, F., Vallez, D., Aslan, S., Koldenhoven, R., & Jimenez, J. (2020). Comparison of inverse kinematics algorithms for digital twin industry 4.0 applications. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3319–3326). IEEE. https://doi.org/10.1109/smc42975.2020.9283253

Andreev, E., Radeva, V., & Nikolova, M. (2019). Innovative biomonitoring systems in the aerospace industry. N. Y. Vaptsarov Naval Academy, Varna, Bulgaria.

McGregor, C. (2021). A platform for real-time space health analytics as a service utilizing space data relays. In 2021 IEEE Aerospace Conference (50100) (pp. 1–14). IEEE. https://doi.org/10.1109/AERO50100.2021.9438475

Merrill, A. M. (2018). A transdiagnostic investigation of amygdala-vmPFC resting state functional connectivity and emotional distress in daily lives [Doctoral dissertation, University of Missouri-Columbia]. https://doi.org/10.32469/10355/66774

Landry, C. (2021). Cuffless blood pressure monitoring: Estimation of the waveform and its prediction interval [Doctoral dissertation, University of Waterloo]. UWSpace. https://dspacemainprd01.lib.uwaterloo.ca/server/api/core/bitstreams/314aa0fc-f09e-4423-acfb-8825ed7fc8be/content

Antoniadou, E., Belo, D., D'Silva, K., Wang, B., Russell, B., Soboczenski, F., Martin, A., Mackintosh, G., & Shaw, T. (2020). A generative machine learning framework for synthesizing symptomatic ECG astronaut health data [Conference presentation]. NASA Human Research Program Investigators' Workshop, Galveston, TX, United States. https://www.researchgate.net/profile/Hrp-Workshop/publication/341267174_A_GENERATIVE_MACHINE_LEARNING_FRAMEWORK_FOR_SYNTHESIZING_SYMPTOMATIC_ECG_ASTRONAUT_HEALTH_DATA_NASA_HRP_Investigators'_Workshop/links/5eb6a355a6fdcc1f1dcb0738/A-GENERATIVE-MACHINE-LEARNING-FRAMEWORK-FOR-SYNTHESIZING-SYMPTOMATIC-ECG-ASTRONAUT-HEALTH-DATA-NASA-HRP-Investigators-Workshop.pdf

Chaari, M., Abid, M., Ouakrim, Y., Lahami, M., & Mezghani, N. (2020). A mobile application for physical activity recognition using acceleration data from wearable sensors for cardiac rehabilitation. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (pp. 625–632). SCITEPRESS. https://doi.org/10.5220/0009118706250632

Saini, A. (2024). Enhancing Human Activity Recognition via Analysis of Hexoskin Sensor Data and Deep Learning Techniques [Master's thesis, Université de Montréal]. Papyrus. https://umontreal.scholaris.ca/server/api/core/bitstreams/376242d4-cd0e-4c4c-b411-a9317f5cc530/content

Al Sayed, C. (2018). Développement d’une veste de refroidissement individuelle adaptée au travail en mines profondes [Thèse de doctorat, École de technologie supérieure, Université du Québec].

Apparicio, P., Gelb, J., Jarry, V., & Lesage-Mann, É. (2021). Cycling in one of the most polluted cities in the world: Exposure to noise and air pollution and potential adverse health impacts in Delhi. International Journal of Health Geographics, 20(1), 18. https://doi.org/10.1186/s12942-021-00272-2

Chang, T. P., Beshay, Y., Hollinger, T., & Sherman, J. M. (2019). Comparisons of stress physiology of providers in real-life resuscitations and virtual reality–simulated resuscitations. Simulation in Healthcare, 14(2), 104–112. https://doi.org/10.1097/SIH.0000000000000356

Pelgröm, A. (2021). Breathing patterns of asthma patients during cycling ergometry [Master's thesis, University of Twente]. UT Student Theses. https://essay.utwente.nl/89148/

Chang, T. P., Azen, C., & Sherman, J. M. (2020). Physiological stress markers following resuscitations remain elevated throughout physician shift hours. Academic Emergency Medicine, 27(6), 510–513. https://doi.org/10.1111/acem.13982

Cherif, N., Ouakrim, Y., Benazza-Benyahia, A., & Mezghani, N. (2018). Physical activity classification using a smart textile. In 2018 IEEE Life Sciences Conference (LSC) (pp. 175–178). IEEE. https://doi.org/10.1109/LSC.2018.8572280

Aucoin, R., Nguyen, D., Ross, B., Bourbeau, J., Lewthwaite, H., Ekström, M., von Leupoldt, A., & Jensen, D. (2026). Effect of fan-to-face therapy on exertional breathlessness in adults with chronic lung disease. ERJ Open Research, 12(4). https://doi.org/10.1183/23120541.01636-2025

Cherif, N. H., Mezghani, N., Gaudreault, N., Ouakrim, Y., Mouzoune, I., & Boulay, P. (2018). Physiological data validation of the Hexoskin smart textile. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) (Vol. 1, pp. 150–156). SCITEPRESS. https://doi.org/10.5220/0006588001500156

Cheriyan, D., Khamraev, K., & Choi, J.-H. (2021). Varying health risks of respirable and fine particles from construction works. Sustainable Cities and Society, 72, Article 103016. https://doi.org/10.1016/j.scs.2021.103016

Auger, D., Almond, A., Wright, R., Breuer, J., Fletcher, F., & Jones, K. E. (2025). Bridging Spirit and Science: Physiological and Spiritual Effects of Wîwîp’son Healing Swing Sessions. Research Square. https://doi.org/10.21203/rs.3.rs-8399311/v1

Choi, J.-H., Khamraev, K., & Cheriyan, D. (2022). Hybrid health risk assessment model using real-time particulate matter, biometrics, and benchmark device. Journal of Cleaner Production, 350, Article 131443. https://doi.org/10.1016/j.jclepro.2022.131443

Bıçakcı, H. S., Santopietro, M., & Guest, R. (2023). Activity-based electrocardiogram biometric verification using wearable devices. IET Biometrics, 12(1), 38-51. https://doi.org/10.1049/bme2.12105

Chowdhury, T. A., Gratz-Kelly, S., Wagner, E., Motzki, P., & Lehser, M. (2024). Ensemble learning approach for advanced predictive modeling of biometric data and action states with smart sensing. IEEE Access, 12, 139998–140008. https://doi.org/10.1109/ACCESS.2024.3466528

Bıçakcı Yeşilkaya, H. S., & Guest, R. (2025). Activity-aware electrocardiogram biometric verification utilising deep learning on wearable devices. EURASIP Journal on Information Security, 2025(1), Article 7. https://doi.org/10.1186/s13635-025-00193-8

Chowdhury, T. A., Gratz-Kelly, S., Wagner, E., Motzki, P., & Lehser, M. (2026). A multimodal intelligent system for human digital twin simulation with continuous kinematic data tracking, biometric prognosis, and cognitive state feedback in industrial environments. Advanced Intelligent Discovery, Article e202500217. https://doi.org/10.1002/aidi.202500217

Babakhani, S., Remy, D., & Roitberg, A. (2025). Deep Learning for Metabolic Rate Estimation from Biosignals: A Comparative Study of Architectures and Signal Selection. arXiv. https://doi.org/10.48550/arXiv.2511.09276

Claiborne, A., Williams, A., Jolly, C., Isler, C., Newton, E., May, L., & George, S. (2023). Methods for analyzing infant heart rate variability: A preliminary study. Birth Defects Research, 115(10), 998–1006. https://doi.org/10.1002/bdr2.2177

Baig, M., Mirza, F., GholamHosseini, H., Gutierrez, J., & Ullah, E. (2018). Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, 4456-4459. https://doi.org/10.1109/EMBC.2018.8513343

Coden, G., Greenwell, P., Niu, R., Fang, C., Talmo, C., & Smith, E. L. (2024). Energy expenditure of femoral broaching in direct anterior total hip replacements—Comparison between manual and automated techniques. The International Journal of Medical Robotics and Computer Assisted Surgery, 20(1), Article e2592. https://doi.org/10.1002/rcs.2592

Baig, M. M., GholamHosseini, H., Gutierrez, J., Ullah, E., & Lindén, M. (2021). Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications. Applied Clinical Informatics, 12(1), 1-9. https://doi.org/10.1055/s-0040-1719043

Banerjee, T., Anantharam, P., Romine, W. L., Lawhorne, L., & Sheth, A. (2015). Banerjee, T., Anantharam, P., Romine, W. L., Lawhorne, L. W., & Sheth, A. (2015, July 27-30). Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia [Conference presentation]. International Conference on Health Informatics and Medical Systems, Las Vegas, NV, United States. https://corescholar.libraries.wright.edu/knoesis/1063

Couture, L., Passalacqua, M., Joblot, L., Magnani, F., Pellerin, R., & Léger, P.-M. (2024). Adaptive system to enhance operator engagement during smart manufacturing work. Sensors & Transducers, 265(2), 106–119. http://www.sensorsportal.com/p_3339.htm

Banerjee, T., Peterson, M., Oliver, Q., Froehle, A., & Lawhorne, L. (2018). Validating a Commercial Device for Continuous Activity Measurement in the Older Adult Population for Dementia Management. Smart Health, 5-6, 51-62. https://doi.org/10.1016/j.smhl.2017.11.001

Couture, L., Passalacqua, M., Joblot, L., Magnani, F., Pellerin, R., & Léger, P.-M. (2024). Enhancing operator engagement during AI-assisted manufacturing work using optimal state deviation feedback system. In Proceedings of the 4th IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0/5.0 (ARCI 2024) (pp. 232–237). https://sam.ensam.eu/handle/10985/24788

Barker, L., & Mercer, J. (2017). Measuring Acceleration of a Trampoline Circus Act During Training and In-Show Using Wearable Technology [Conference abstract]. Medicine & Science in Sports & Exercise. https://doi.org/10.1249/01.mss.0000519030.33160.bb

Cozzarelli, N. F., Ashkenazi, I., Khan, I. A., Lonner, J. H., Lajam, C., Schwarzkopf, R., & Rozell, J. C. (2024). Surgeons Experience Greater Physiologic Stress and Strain in the Direct Anterior Approach Than the Posterior Approach for Total Hip Arthroplasty. The Journal of Arthroplasty, 39(9 Suppl 2), S322–S326. https://doi.org/10.1016/j.arth.2024.05.063

Barker, L., Burnstein, B., & Mercer, J. (2020). Acceleration profile of an acrobatic act during training and shows using wearable technology. Sports Biomechanics, 19(2), 201-211. https://doi.org/10.1080/14763141.2018.1460394

D’Souza, A. W., Notley, S. R., Brown, E. K., Poirier, M. P., & Kenny, G. P. (2019). The Hexoskin physiological monitoring shirt does not impair whole-body heat loss during exercise in hot-dry conditions. Applied Physiology, Nutrition, and Metabolism, 44(3), 332–335. https://doi.org/10.1139/apnm-2018-0370

Baylon, G., Soghier, L., Gordish, H., & Wolfe, A. (2025). A Pilot Study Measuring Pediatric Clinician Daily Physiologic Stress Using Heart Rate Variability [Conference abstract]. Critical Care Medicine. https://doi.org/10.1097/01.ccm.0001099128.48142.cb

Raffoul, D. (2019). Validation and responsiveness of wearable biometric technology to monitor cardiac and ventilatory parameters at rest and during exercise in COPD [Master’s thesis, McGill University]. eScholarship@McGill. https://doi.org/10.82308/47051

Darnell, M. E., Quinn, T. D., Carnahan, S. P., Carpenter, T., Meglino, N., Yorio, P. L., & Doperak, J. M. (2022). Effect of cloth masks and N95 respirators on maximal exercise performance in collegiate athletes. International Journal of Environmental Research and Public Health, 19(13), 7586. https://doi.org/10.3390/ijerph19137586

Beltrame, T., Amelard, R., Wong, A., & Hughson, R. L. (2017). Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models. Journal of Applied Physiology, 124(2), 473-481. https://doi.org/10.1152/japplphysiol.00299.2017

MacQuarrie, A., & Whitfield, S. (2019). Fit for duty: shedding light on paramedics’ health via research methodologies. International Paramedic Practice, 9(2), 47–49. https://doi.org/10.12968/ippr.2019.9.2.47

Beltrame, T., Amelard, R., Wong, A., & Hughson, R. L. (2017). Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living. Scientific Reports, 7, 45738. https://doi.org/10.1038/srep45738

Davidson, J. M., Pulford-Thorpe, A., Callaghan, J. P., & Dominelli, P. B. (2025). An active sitting chair can increase energy expenditure while performing standardized data entry work. Work: A Journal of Prevention, Assessment and Rehabilitation, 81(1), 2129–2138. https://doi.org/10.1177/10519815241303339

Beltrão, G., Alaee-Kerahroodi, M., Schroeder, U., Tatarinov, D., & Bhavani Shankar, M. R. (2022). Nonlinear Least Squares Estimation for Breathing Monitoring Using FMCW Radars. In 2021 18th European Radar Conference (EuRAD) (pp. 241-244). IEEE. https://doi.org/10.23919/EuRAD50154.2022.9784561

DeLeon, R., Cowings, P., & Toscano, B. (2016, August 15). Wearable biosensor monitor to support autonomous crew health and performance [Poster presentation]. University of Houston-Clear Lake Student Symposia, Clear Lake, TX, United States. NASA Technical Reports Server. https://ntrs.nasa.gov/citations/20190000238

Bennett, S. L., Goubran, R., & Knoefel, F. (2016). Adaptive eulerian video magnification methods to extract heart rate from thermal video. In 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-5). IEEE. https://doi.org/10.1109/MeMeA.2016.7533818

Mannée – Boele, D. (2022). Current and novel methods to measure dynamic hyperinflation in patients with COPD: Accuracy and usability in telemonitoring [Doctoral thesis, Radboud University Nijmegen]. Radboud Repository. https://repository.ubn.ru.nl/handle/2066/250220

Bennett, M., Čironis, L., Sousa, A., Ahmad, S. L., Hassan, T., Yuen, K., Douris, P., Zwibel, H., DiFrancisco-Donoghue, J., & Koshy, A. (2022). Continuous monitoring of HRV in esports players. International Journal of Esports, 2(2). https://www.ijesports.org/article/60/html

Denning, M., Smith, M., Zagrodnik, J., & Ruden, T. (2016). Movement intensity and caloric expenditure differences between walking and pickleball [Abstract]. Medicine & Science in Sports & Exercise, 48(5 Suppl), 209. https://doi.org/10.1249/01.mss.0000485629.74799.e1

Betson, J., Fein, E. C., Long, D., & Horrocks, P. (2024). Quantifying Threat or Challenge Response of Undergraduate Paramedicine Students During High-Stress Clinical Scenarios: A Narrative Review. International Journal of Paramedicine, (8), 173-189. https://doi.org/10.56068/QHQM3379

Denning, W. M., Zagrodnik, J., Smith, M., & Ruden, T. (2022). Physical activity differences between walking and playing pickleball doubles. Science & Sports, 37, 513.e1-513.e4. https://doi.org/10.1016/j.scispo.2021.06.009

Birdee, G., Nelson, K., Wallston, K., Nian, H., Diedrich, A., Paranjape, S., Abraham, R., & Gamboa, A. (2023). Slow breathing for reducing stress: The effect of extending exhale. Complementary Therapies in Medicine, 73, 102937. https://doi.org/10.1016/j.ctim.2023.102937

Dhillon, P., Tam, K., & Juneau, E. (2022). Dress for success: Embedded health sensors in the future soldier. Journal of Military, Veteran and Family Health, 8(2), 109-115. https://doi.org/10.3138/jmvfh-2021-0095

Bishnoi, A., & Hernandez, M. E. (2025). Heart rate reserve effects on brain activation and cognitive-motor performance while doing an instrumented trail walking task in middle-aged to older adults. Archives of Gerontology and Geriatrics Plus, 2, 100115. https://doi.org/10.1016/j.aggp.2024.100115

Ding, T. Y., Gagliano, L., Beauregard, L., Robert, M., Toffa, D., Nguyen, D., & Bou Assi, É. (2021). Sleep macrostructure of nights preceding epileptic seizures: A preliminary study using the Hexoskin smart shirt (Abstract No. 1.409) [Conference presentation abstract]. American Epilepsy Society Annual Meeting. https://aesnet.org/abstractslisting/sleep-macrostructure-of-nights-preceding-epileptic-seizures--a-preliminary-study-using-the-hexoskin-smart-shirt

Bishop, A., MacNeil, E., & Izzetoglu, K. (2021). Cognitive Workload Quantified by Physiological Sensors in Realistic Immersive Settings. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Augmented Cognition (Lecture Notes in Computer Science, Vol. 12776, pp. 119-133). Springer. https://doi.org/10.1007/978-3-030-78114-9_9

Ding, T. Y., Gagliano, L., Jahani, A., Toffa, D. H., Nguyen, D. K., & Bou Assi, E. (2024). Epileptic seizure forecasting with wearable-based nocturnal sleep features. Epilepsia Open, 9(5), 1793-1805. https://doi.org/10.1002/epi4.13008

Bläsing, D., & Schicha, M. (2017). Wirkung von Herzkohärenztraining auf Real-Life-Stressoren. In Gesellschaft für Arbeitswissenschaft e.V. (Ed.), Soziotechnische Gestaltung des digitalen Wandels - kreativ, innovativ, sinnhaft: 63. Kongress der Gesellschaft für Arbeitswissenschaft (Beitrag A.3.1). GfA-Press. https://gfa2017.gesellschaft-fuer-arbeitswissenschaft.de/inhalt/A.3.1.pdf

Distler, T. (2025). Breath in motion: Understanding runners' perception, adaptation and embodiment to respiratory visualizations [Master's thesis, University of Salzburg]. https://eplus.uni-salzburg.at/Abschlussarbeiten/content/titleinfo/12715805

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