An inflatable belt 100 for use in a BFR system with an outer belt material 102 hermetically sealed to an inner belt material 101 along a perimeter, thereby forming at least one inflatable chamber 103, the inflatable chamber having an input port 104 for accepting a gas into the chamber, the inflatable belt further comprising a first fastening means 110 in communication with the outer belt material, for attaching to a second fastening means 111 in communication with the outer belt material, thereby locking a circumference of the inflatable belt, when wrapped around a user's limb, efficacy feedback means 200 for gathering efficacy feedback data 205, for use in prescribing, monitoring and adjusting one or more training parameters of a BFR training session and/or program based on evaluation of the efficacy feedback data.

A method of determining blood pressure measurements includes inflating a cuff, receiving an indication of pressure inside the cuff during inflation, determining a blood pressure based at least in part on the received indication, assigning a confidence level to the blood pressure, and determining whether the confidence level satisfies a threshold confidence level. Based at least on a determination that the confidence level satisfies a threshold confidence level, the method can include causing a display to display the blood pressure. Based at least on a determination that the confidence level does not satisfy a threshold confidence level, the method can include deflating the cuff, receiving an indication of pressure inside the cuff during deflation, determining another blood pressure, and causing a display to display a blood pressure.

Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate metabolic equivalent of task (MET) measures for a plurality of moments of interest. Based on the MET measures and a time period for which the MET measures exceed a threshold value, it is determined whether the user is in a sedentary state. If the user is in the sedentary state, the user is provided a notification to encourage the user to perform a non-sedentary activity.

The current invention pertains to an apparatus and method for the determination of excess post-exercise oxygen consumption (EPOC) and the estimation of blood lactate levels. While these exercise parameters are traditionally determined using indirect calorimetry and blood sampling, this invention provides a method for the determination of these parameters using heart rate data. A wearable photoplethysmography device for measuring heart rate is included as an exemplary embodiment, however, the method of the current inventions can also be used with heart rate data from any heart rate monitor. In an embodiment of the present invention a supply demand differential equation is used to continuously monitor EPOC in real-time. Furthermore, blood lactate levels can also be estimated as a function of EPOC. Importantly, the process of determining EPOC and blood lactate can be linked to a biomathematical model of human physiology to access additional parameters such as hormonal changes, body composition changes or other physiological fluctuations or transient physiological behavior.

A virtual stress test may be performed for a patient by creating an electronic model of a region of interest of the patient's anatomy, such as one or more coronary arteries, determining pressure drops through the region of interest, based on computational fluid dynamics, at two different activity levels of the patient, and determining a range of pressure drops for a range of activity levels based on flow rates associated with the two activity levels. Based on the range of pressure drops, it can be determined if the patient has a clinically-significant pressure drop, indicative of an obstructive stenosis, at an activity level relevant to the patient's metabolic requirements.

A computer-implemented system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan, a patient interface, and a computing device configured to: receive treatment; write to an associated memory, configured to be accessed by an artificial intelligence engine, treatment data, the artificial intelligence engine being configured to use at least one machine learning model to, using the treatment data, generate at least one of a treatment scheduling output prediction and an appointment output; receive, from the artificial intelligence engine, the at least one of the treatment scheduling output prediction and the appointment output; and selectively modify, using the at least one of the treatment scheduling output prediction and the appointment output, the at least one aspect of the treatment plan.

A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan, a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session, and a first computing device configured to: receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan; identify at least one aspect of the at least one measurement pertaining to the user associated with a first treatment device mode of the treatment device; determine whether the at least one aspect of the measurement correlates with a secondary condition of the user; and, in response to a determination that the at least one aspect of the at least one measurement is correlated with the at least one secondary condition of the user, generate secondary condition information indicating at least the secondary condition.

A computer-implemented system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan, a patient interface, and a computing device configured to: receive treatment; write to an associated memory, configured to be accessed by an artificial intelligence engine, treatment data, the artificial intelligence engine being configured to use at least one machine learning model to, using the treatment data, generate at least one of a treatment scheduling output prediction and an appointment output; receive, from the artificial intelligence engine, the at least one of the treatment scheduling output prediction and the appointment output; and selectively modify, using the at least one of the treatment scheduling output prediction and the appointment output, the at least one aspect of the treatment plan.

A method includes causing display, during a first time period and via a first set of multiple smart mirrors, of live video depicting at least one user associated with the first set of multiple smart mirrors, without displaying a workout video. The method also includes causing display, during a second time period following and mutually exclusive of the first time period, and via a second set of multiple smart mirrors, of a workout video and a representation of at least one user associated with the second set of smart mirrors. The method also includes causing display, during a third time period following and mutually exclusive of the second time period, and via a third set of multiple smart mirrors, of live video depicting at least one user associated with the third set of multiple smart mirrors.

During a first time period and for a first user, a second user is automatically selected based on competitive data of the first user and competitive data of the second user, and a workout selection is sent to cause a video of a workout to be displayed during a second time period on a smart mirror of the first user and a smart mirror of the second user. During the second time period, a live stream of the first user exercising is displayed at the smart mirror of the second user, and a live stream of the second user exercising is received and displayed at the smart mirror of the first user. During the second time period, a performance score of the first user and a performance score of the second user is displayed at the smart mirrors of the first user and the second user.