Digital Healthcare
Our paper presents RespiroDynamics: A Comprehensive Multimodal Respiratory Dataset, compiled from 60 participants, recorded in two sessions labelled ’rest’ and ’exercise’. This dataset incorporates a variety of data types, including Red-Green-Blue (RGB) and Thermal videos, Heart Rate (HR), ECG readings and metadata, all synchronized with observed respiratory activities. Additionally, these data are enriched with reference values from the NHANES III (Hankinson- 1999) distribution.
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IoT sensors offer a wide range of sensing capabilities, many of which have the potential for health or medical applications. Existing solutions for IoT in healthcare have notable limitations, such as limited I/O protocols, limited cloud platform support, and limited extensibility. Therefore, the development of an open-source Internet of Medical Things (IoMT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility would be highly desirable.
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The dataset comprises raw data to validate methods for reliable data collection. We proposed the data collection methods in a path to assess digital healthcare apps. To validate the methods, we conducted experiments in Amazon Mechanical Turk (MTurk), and then we showed that the methods have a significant meaning based on statistical tests.
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