Wearable health monitors and methods of monitoring health

An apparatus, comprising:

• a strap configured to be worn on a first limb of a user;
• a pulse oximeter disposed on at least one printed circuit board (PCB) which is coupled to the strap, the pulse oximeter comprising:
• at least one light emitting diode (LED) configured to be oriented towards the first limb of the user when the strap is worn by the user and to emit, through openings of the strap and into the first limb of the user, a first type of light having a first peak wavelength and a second type of light having a second peak wavelength;
• and
• a light sensor configured to be oriented towards the first limb of the user when the strap is worn by the user and comprising respective components configured to sense, from the first limb of the user, the first type of light and the second type of light;
• an electrocardiogram sensor coupled to the strap, the electrocardiogram sensor comprising:
• a first electrode configured to make electrical contact with the first limb of the user when the strap is worn by the user;
• and
• a second electrode configured to be oriented such that the user may make electrical contact with the second electrode using a second limb of the user when the strap is worn by the user;
• and
• a computing system, configured to:
• receive data from the pulse oximeter and the electrocardiogram sensor;
• determine, using data from the pulse oximeter, an amount of oxygen in blood of the user based on a comparison of:
• a first reading sensed by the light sensor corresponding to an amount of the first type of light absorbed by the blood;
• and
• a second reading sensed by the light sensor corresponding to an amount of the second type of light absorbed by the blood;
• determine, using data from the pulse oximeter, a pulse onset timing of the user;
• determine, using data from the electrocardiogram sensor, an R-wave timing of an electrocardiogram signal of the user;
• determine, using the pulse onset timing and the R-wave timing, a pulse wave velocity of the user;
• determine, using the pulse wave velocity, a pulse transit time of the user;
• input the pulse transit time and the amount of oxygen in the blood into a machine learning model trained, using supervised learning based on a dataset including pulse transit time data and blood oxygenation data, to produce an output indicative of blood pressure;
• receive, from the machine learning model, estimates of:
• systolic blood pressure of the user;
• and
• diastolic blood pressure of the user;
• and
• output the estimates to the user.