Rapid evolution of pervasive computing and body-sensing technologies propels the Internet of Health, encouraging clinical researchers to use these methods in more ecological and real-life experiments. Important applications include neuropharmacological research, behavioural neuroscience, and preventive healthcare. However, there are several challenges to overcome. We lack data management standard to integrate data from multiple devices used by one subject, thus the extent of data collection is limited to single specialized biosensor platforms. We also lack freely available, standardized and device-independent visualization and processing tools. Therefore, data harmonization, cross-modal data integration and data visualization are prohibitively cumbersome. These heterogeneities limit the scale, equity and inclusivity of the data collection process--keeping such systems available to few major industry players or wealthier academic institutes. This problem has been addressed since early 1990s by the neuroimaging community, that has taken advantage of functional and anatomical brain imaging in population neuroscience studies. Today, a wealth of knowledge exists about best practices to undertake epidemiological or lifestyle study in Alzheimer's disease or Autism, using magnetic resonance imaging (MRI) and, recently, electro- and magnetoencephalography (EEG &MEG). To integrate myriad large-scale longitudinal data generated across different modalities (e.g. positron emission tomography, EEG, MRI), on scanners from different manufacturers, and across the globe, is now common practice. Researchers are now interested incorporating ecological data from body-worn sensors, apps or interactive and immersive games. We described Sensomatrix framework as a response to this need, by inspiration from existing data management tools available in neuroimaging. Here, we provide an example of its application in serious game development, where we studied the relation between cognition, and psychophysiological stress caused by computer games in their prospective users, older adults. By drawing parallels between neuroimaging and biosensor data, we provide provide ideas for standardization of such technologies for use in large-scale studies.