Participants (patients and volunteers) will be recruited to have their blood pressure measured by standard blood pressure assessment methods while having their face video recorded. The data collected will help improve the blood pressure measurement accuracy of Transdermal Optical Imaging, which relies on machine learning to extract physiological information from videos recorded.

There are many ways to measure blood pressure (monitors, mercury sphygmomanometer, aneroid devices), with most relying on a cuff-inflation. Transdermal Optical Imaging measures blood pressure using a video captured by any conventional camera (e.g., those on a phone, tablet, laptop) and advanced machine learning algorithms.

The current study aims to improve the accuracy of Transdermal Optical Imaging algorithms for measuring blood pressure. The investigators will recruit participants (patients with medical problems and healthy volunteers) to have their blood pressure measured in various ways (e.g.,by registered nurses with sphygmomanometer and stethoscope, continuous blood pressure monitor, etc.). Further, participants will have their faces video-recorded intermittently between standard measurements or at the same time as standard measures.

Inclusion Criteria:

• Able and willing to provide written informed consent to participate (including by parent or legal guardian if under 16 years old).

Exclusion Criteria:

• No exclusion criteria

Only individuals, of any sex, over the age of 13 will be included in the study.

No IPD will be shared with other researchers outside the team of researchers involved in the current study. Findings of the study based on group results will be shared in publications.