Interest in measuring human performance has been increasing in popularity and desire, driven by a 17 realization that quantitative validation of one's health is now conveniently and inexpensively possible. 18Advances in semiconductor technology, materials development, and emerging algorithmic platforms 19 (i.e., AI and machine learning) have dramatically increased quality, lowered costs and furthered 20 biometric sensor technology. The impetus for this growing field is a new capability for tangible 21 measurements of individual performance across multidisciplinary and diverse populations of people.
The assessment of human performance to promote improvement can apply to anyone from the 23 everyday person to an elite level athlete and military personnel. Therefore, there are numerous challenges from a variety of perspectives ranging from the data quality 62 required to ensure adequate measurement accuracy to the need to ensure the integrity of transmitted 63 data to respect the user's privacy. In the next year, we will see an incremental advancement in wearable 64 customization, versatility, style, and fabrics, making devices that people will be happy to attach to 65 themselves. We will see new forms of wearables beyond the common smartwatch or ring we are 66 already accustomed to seeing on many users' wrists and fingers. The speedy technological 67 developments are producing drastic reductions in device costs and dimensions and increasing the 68 possibility for measuring new and unexplored variables. The booming on-sensor computational 69 capabilities will push wearables to monitor the human performances of users, workers, and athletes for 70 long periods of time and under challenging conditions (during operation, in new environments even 71 outside of terrestrial conditions). With the new ability to obtain data, previously unavailable under 72 these conditions, will be the increased interest in and importance for databases that are freely available 73 within the relevant communities, yet are simultaneously respectful of the users' privacy. This 74 abundance of real-world data will lead to new advanced algorithms based on artificial intelligence, 75 providing tangible opportunities to identify new biomarkers related to human performance. This will 76 make it possible to overcome the current limitations of data-driven algorithms that contain or promote 77 biases, which are inevitable when the sample of users is small and restricted to a particular geographic 78 area, or when data are taken in the laboratory under certain conditions. In this way we can use data taken with wearable devices not only to provide vital signs physiological quantities and predictive 80 measures but to also strongly support evidence-based clinical and operational choices. 81The articles presented in this special issue highlight the progress that has been made in enabling 82 consumer recreational devices to "crossover" into the areas of medical monitoring, diagnostic 83 improvements and performance enhancements.