In this study, the hypothesis being explored is that VO2Max and other CPET parameters can be accurately estimated from biosignals (namely, motion from accelerometers and cardiopulmonary variables from EKG) collected during activities of daily living using wearable biosensors worn by study participants. This study will aim to collect development and validation data for a machine learning algorithm and to evaluate the performance of the algorithm. A total of 1000 participants will be enrolled including: (Normal) 100 participants, self-reported healthy male and female participants aged 18 to 80 and (Standard of Care) 900 participants.

Inclusion Criteria:

1. Willing and able to comply with protocol procedures and available for the duration of the study.
2. Willing to sign and date informed consent document for study participation.
3. Participant is undergoing the Cardiopulmonary Exercise Test (CPET) as Standard of Care

Exclusion Criteria:

1. Participant is pregnant, lactating or ≤30 days post-partum.

2. Participant has limited or no intrinsic sinus node function (i.e. chronic atrial pacing).

   • If participant has an indwelling cardiac device and programming cannot be sufficiently ascertained to assure sinus node competence and lack of atrial pacing, the patient should be excluded.

3. Barostim (™) or similar noncardiac electrical pulse generating device in situ.

4. Complex congenital heart disease (even repaired or palliated) with the following exception:

   • Biventricular physiology without severe valvar dysfunction (e.g. free pulmonary insufficiency) at the discretion of the investigator.

5. Any history of allergy to adhesive

6. Any cognitive or physical limitations that, in the opinion of the investigator, limits the participant's ability to fully follow study procedures and/or reach a respiratory exchange ratio of 1.0.

Participant is undergoing the Cardiopulmonary Exercise Test (CPET) as Standard of Care