A CPR machine (100) is configured to perform, on a patient's (182) chest, compressions that alternate with releases. The CPR machine includes a compression mechanism (148), and a driver system (141) configured to drive the compression mechanism. A force sensing system (149) may sense a compression force, and the driving can be adjusted accordingly if there is a surprise. For instance, driving may have been automatic according to a motion-time profile, which is adjusted if the compression force is not as expected (850). An optional chest-lifting device (152) may lift the chest between the compressions, to assist actively the decompression of the chest. A lifting force may be sensed, and the motion-time profile can be adjusted if the compression force or the lifting force is not as expected.

A percussive therapy system includes a percussive therapy device that includes a housing, an electrical source, a motor positioned in the housing, a switch for activating the motor, a push rod assembly operatively connected to the motor and configured to reciprocate in response to activation of the motor, and an attachment configured to be operatively connected to a distal end of the push rod assembly of the percussive massage device and to provide at least one therapeutic effect to a user. The attachment may include at least one of an actuator configured to provide the at least one therapeutic effect to the user and a sensor configured to obtain at least one of biometric data of the user and information regarding operation of the percussive therapy device.

A computer-implemented system may include a stair-climbing machine configured to be manipulated by a user while the user performs a treatment plan, an interface comprising a display configured to present information associated with the treatment plan, and processing structure configured to receive, from one or more data sources, information associated with the user, wherein the information comprises one or more risk factors associated with a cardiac-related event; generate, using one or more trained machine learning models, the treatment plan for the user, wherein the treatment plan is generated based on the information associated with the user, and the treatment plan comprises one or more exercises associated with managing the one or more risk factors in order to reduce a probability that a cardiac intervention will occur; and transmit the treatment plan to cause the stair-climbing machine to implement the one or more exercises.

A system for assisting with a cardiopulmonary resuscitation (CPR) treatment being administered to a patient. In one aspect, a system for assisting with a cardiopulmonary resuscitation (CPR) treatment being administered to a patient includes a sensor for determining a parameter of the patient (e.g., indicative of a blood flow or pressure waveform), and one or more processors configured for receiving input from the sensor, determining, based on the input from the sensor, whether a rate of chest compressions administered in the CPR treatment should be changed, and providing an indication to a user that the rate of chest compressions should be changed.

Described herein are wearable sensory stimulation and monitoring devices that can be worn around the back of the neck along and in contact with the spine. The wearable device includes cuff, band and head cover members as form factors. Apparatuses described herein stimulate one or more senses including auditory, tactile or olfactory. Apparatuses are also described that allow for sensing and monitoring of physiologic parameters such as heart rate, blood pressure and movement. The device also has features that allow for easy attachment or integration to articles worn on the head such as augmented reality and virtual reality accessories to provide visual stimulation and entertainment.

A computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue, enable the user to perform an exercise at different exertion levels. Next, while the user uses the electromechanical machine to perform the first treatment plan, receiving, at the computing device, data from sensors configured to measure the data associated with the invasive surgical-related health issue and transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, and the invasive surgical-related health issue. The method additionally includes receiving the second treatment plan.

Systems including an elliptical machine and a processing device. The processing device may be configured to receive, before or while a user operates the elliptical machine, one or more messages pertaining to the user or a use of the elliptical machine by the user. The processing device may be also configured to determine whether the one or more messages were received by the processing device. In response to determining that the one or more messages were not received by the processing device, the processing device may be configured to determine, via one or more machine learning models, one or more actions to perform. The one or more actions may include at least one of initiating a telecommunications transmission, stopping operation of the elliptical machine, and modifying one or more parameters associated with the operation of the elliptical machine.

A computer-implemented system may include a rowing machine configured to be manipulated by a user while the user performs a treatment plan, an interface comprising a display configured to present information associated with the treatment plan, and a processing device configured to receive, from one or more data sources, information associated with the user, wherein the information comprises one or more risk factors associated with a cardiac-related event; generate, using one or more trained machine learning models, the treatment plan for the user, wherein the treatment plan is generated based on the information associated with the user, and the treatment plan comprises one or more exercises associated with managing the one or more risk factors in order to reduce a probability that a cardiac intervention will occur; and transmit the treatment plan to cause the rowing machine to implement the one or more exercises.

A computer-implemented system includes a processing device configured to receive a plurality of user and blood vessel characteristics associated with a user, generate a selected set of user and blood vessel characteristics, determine, based on the selected set of the user and blood vessel characteristics, a probability that angiogenesis will occur, and generate, based on the probability and the selected set of the user and blood vessel characteristics, a treatment plan that includes one or more exercises directed to modifying the probability that angiogenesis will occur, and a treatment apparatus configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

A computer-implemented system includes one or more processing devices configured to receive user information associated with a user, generate a selected set of the user information, determine, based on the selected set of the user information, at least one of a first probability of surviving one or more procedures and a second probability indicating an improvement, resulting from the one or more procedures, in quality-of-life metrics for the user, generate, based on the at least one of the first probability and the second probability and on the selected set of the user information, one or more recommendations of whether the user should undergo the one or more procedures, and generate, based on the one or more recommendations, a treatment plan that includes one or more exercises directed to modifying the at least one of the first probability and the second probability.