The challenge of an accurate medical diagnosis lies in dealing with heterogeneous data with different granularity and reliability degrees, missing data, and subjective or unquantifiable data. Healthcare researchers should analyze unstructured information from patients with similar symptoms to draw general principles that, when validated, could contribute to create new knowledge. Wearable technologies, based on ECG and PPG acquisition, allow the quantification of HR changes throughout the entire day as the subject moves freely. As such, this information can be used for screening and monitoring arrythmias and other cardiovascular diseases, but also metabolic, respiratory and even mental disorders.While more substantial evidence is needed, it is clear that emotional state has an influence on physical state and vice versa. It is essential to incorporate the emotional state into the diagnostic process and the prescription of treatments. However, assessing emotional information is challenging, as it relies heavily on the observer, and quantifying emotion accurately is complex. Recent advances using machine and deep learning on electrodermal activity combined with HRV data have shown promising potential for quantitative assessment of emotional information. Anticipating the rise of personalized medical diagnosis, technology is adapting to the new possibilities and wearable devices are evolving in this direction.Today, almost all wearables offer HR features extracted from photoplethysmographic signals (PPG), that can provide information about stress and mood. PPG combined with ECG can also provide estimates of arterial pressure although they have not been proven to be accurate enough to meet medical quality standards. These physiological and mental parameters might still offer the potential for personalized medical diagnosis or, at least, triggering alerts and professional reviews.In the near future, the technology will incorporate new sensors, better algorithms aided by advances in artificial intelligence having easy access to vast databases. All these novelties will undoubtedly improve the reliability of most physiological and emotional variables in use what, in turn, could significantly enhance diagnostic accuracy and provide valuable support to healthcare professionals. Moreover, wearable devices and smartphones are likely to converge in a digital environment, allowing continuous monitoring of various aspects of an individual's health in a transparent manner.On this specific topic, several papers have been published addressing the various medical applications mentioned above and focusing on the intersection of technology and healthcare. Semin Ryu et al., explored the use of telemedicine in combination with wearable devices to facilitate remote medical diagnosis in space or rural areas by introducing remote auscultation, percussion, and palpation.Another study from Eemu-Samuli Väliaho et al., demonstrated that precise analysis of a good quality PPG signal registered during 24 hours enables the detection of asymptomatic atrial fibrillation, a significant risk factor for stroke in the elderly population.Additionally, in Psychosomatic response to acute emotional stress in healthy students, Esther Garcia et al. have quantitatively assessed induced acute stress in a healthy students population by using electrophysiological variables and developed mathematical models to enhance stress evaluation. Models are then validated against gold standard test.Moreover, wearable sensors have proven reliable in quantifying fatigue and sleep, providing objective data for disease surveillance and evaluating treatment outcomes as it is published by Emmi Antikainen et al. assessing fatigue and sleep in chronic diseases using wearables. Further, wearables have enabled the assessment of physical rehabilitation outcomes through unobtrusive monitoring such as an unintentional walk test in free-living activities as it is shown by Daivaras Sokas et al.Spyridon Kontaxis et al. demonstrates that autonomic function, as measured through HRV by using a wearable device, is a useful way to assess clinical outcomes in multiple sclerosis patients, and also to monitor the severity of the disorder. In conclusion, the combination of knowledge, technology, and creativity promises to significantly enhance medical diagnosis and therapies. The increasing popularity of wearable technology such as connected watches and rings, with over 100 million units sold worldwide each year, equipped with advanced sensors that generate vast amounts of data, coupled with advancements in artificial intelligence, has the potential to change the healthcare paradigm. These devices will offer continuous monitoring and objective assessment of numerous physiological and emotional variables that will process to promote physical exercise and sports as preventive and therapeutic measures against various health conditions such as obesity, diabetes, high blood pressure, cardiovascular diseases, and cancer. Additionally, they can play a preventive and curative role in addressing stress, anxiety disorders, depressive disorders, and Alzheimer's disease.The implication of this technological advancement is huge from a public health perspective. The increased accuracy in diagnosis and improved therapies will benefit both healthcare professionals and patients alike. It represents a promising step forward in the field of healthcare, empowering individuals to take a proactive approach to their well-being and potentially leading to better overall health outcomes.
15 Authors
- EG
Esther García Pagès
Universitat Autònoma de Barcelona, Spain
- AA
Adriana Arza
École Polytechnique Fédérale de Lausanne, Switzerland
- JL
Jesús Lazaro
Universidad de Zaragoza, Spain
- CP
Carlos Puig
Universitat Autònoma de Barcelona, Spain
- TC
Thais Castro
Universitat Autònoma de Barcelona, Spain
- MO
Manuel Ottaviano
Life Supporting Technologies, Spain
- MT
Maria Teresa Arredondo
Life Supporting Technologies, Spain
- ML
Maria Luisa Bernal
Universidad de Zaragoza, Spain
- RL
Raúl López-Antón
Universidad de Zaragoza, Spain
- CD
Concepción De La Cámara
Universidad de Zaragoza, Spain
- EG
Eduardo Gil
Universidad de Zaragoza, Spain
- PL
Pablo Laguna
Universidad de Zaragoza, Spain
- RB
Raquel Bailón
Universidad de Zaragoza, Spain
- JA
Jordi Aguiló
Universidad de Zaragoza, Spain
- JM
Jorge Mario Garzón-Rey
Omya Clariana S.L.U., Spain
6,539 views
16 citations
25 Authors
- SK
Spyridon Kontaxis
Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), Spain
- EL
Estela Laporta
Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), Spain
- EG
Esther Garcia
Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
- AI
Ana Isabel Guerrero
Multiple Sclerosis Centre of Catalonia (CEMCAT), Spain
- AZ
Ana Zabalza
Multiple Sclerosis Centre of Catalonia (CEMCAT), Spain
- MM
Martinis Matteo
Faculty of Medicine and Surgery, Vita Salute San Raffaele University, Italy
- RL
Roselli Lucia
Faculty of Medicine and Surgery, Vita Salute San Raffaele University, Italy
- SS
Sara Simblett
Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
- JW
Janice Weyer
RADAR-CNS Patient Advisory Board, United Kingdom
- MH
Matthew Hotopf
Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
- VA
Vaibhav A. Narayan
Research and Development Information Technology, United Kingdom
- ZR
Zulqarnain Rashid
Department of Biostatistics and Health Informatics, United Kingdom
- AA
Amos A. Folarin
Department of Biostatistics and Health Informatics, United Kingdom
- RJ
Richard J. B. Dobson
Department of Biostatistics and Health Informatics, United Kingdom
- MD
Mathias Due Buron
Danish Multiple Sclerosis Centre, Denmark
- LL
Letizia Leocani
Faculty of Medicine and Surgery, Vita Salute San Raffaele University, Italy
- NC
Nicholas Cummins
Department of Biostatistics and Health Informatics, United Kingdom
- SV
Srinivasan Vairavan
Research and Development Information Technology, United Kingdom
- GD
Gloria Dalla Costa
Faculty of Medicine and Surgery, Vita Salute San Raffaele University, Italy
- MM
Melinda Magyari
Danish Multiple Sclerosis Centre, Denmark
- PS
Per Soelberg Sørensen
Danish Multiple Sclerosis Centre, Denmark
- CN
Carlos Nos
Multiple Sclerosis Centre of Catalonia (CEMCAT), Spain
- RB
Raquel Bailón
Laboratory of Biomedical Signal Interpretation and Computational Simulation (BSICoS), Spain
- GC
Giancarlo Comi
Faculty of Medicine and Surgery, Vita Salute San Raffaele University, Italy
- TR
the RADAR-CNS Consortium
The RADAR-CNS Consortium, United Kingdom
3,120 views
2 citations

