Systems and methods for using AI/ML and for cardiac and pulmonary treatment via an electromechanical machine related to urologic disorders and antecedents and sequelae of certain urologic surgeries

A computer-implemented system for controlling a treatment apparatus, the computer-implemented system comprising:

• a treatment apparatus configured to be manipulated by a user while performing a first treatment plan;
• an interface comprising a display configured to present information pertaining to the first treatment plan;
• and
• a processing device of a first computing device, wherein the processing device is configured to:
• receive a first treatment plan designed to treat a sexual performance health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the sexual performance health issue of the user, enable the user to perform an exercise at different exertion levels by using the treatment apparatus;
• receive data from one or more sensors configured to measure, while the user performs the first treatment plan by using the treatment apparatus, the data associated with the sexual performance health issue of the user;
• transmit the data to a second computing device executing one or more machine learning models to generate a second treatment plan by predicting, based on a standardized measure of perceived exertion indicated by the data and the sexual performance health issue of the user, exercises that will result in a desired exertion level, wherein the second treatment plan modifies at least one exertion level to achieve the desired exertion level, and wherein the standardized measure of perceived exertion includes at least one of a metabolic equivalent of tasks and a Borg rating of perceived exertion, and wherein the one or more machine learning models are trained with labeled inputs comprising first data pertaining to the standardized measure of perceived exertion and other data associated with other users' sexual performance health issues, and labeled outputs comprising other users' exertion levels that led to desired exertion levels;
• receive the second treatment plan;
• and
• implement the second treatment plan at the treatment apparatus, wherein implementing the second treatment plan includes controlling one or more operating parameters of an electromechanical machine of the treatment apparatus in accordance with the second treatment plan.

A tangible, non-transitory computer-readable medium stores instructions that, when executed, cause a processing device to:

• receive, at a first computing device, a first treatment plan designed to treat a sexual performance health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the sexual performance health issue of the user, enable the user to perform an exercise at different exertion levels by using a treatment apparatus;
• receive data from one or more sensors configured to measure, while the user performs the first treatment plan by using the treatment apparatus, the data associated with the sexual performance health issue of the user;
• transmit the data to a second computing device executing one or more machine learning models to generate a second treatment plan by predicting, based on a standardized measure of perceived exertion indicated by the data and the sexual performance health issue of the user, exercises that will result in a desired exertion level, wherein the second treatment plan modifies at least one exertion level to achieve the desired exertion level, and wherein the standardized measure of perceived exertion includes at least one of a metabolic equivalent of tasks and a Borg rating of perceived exertion, and wherein the one or more machine learning models are trained with labeled inputs comprising first data pertaining to the standardized measure of perceived exertion and other data associated with other users' sexual performance health issues, and labeled outputs comprising other users' exertion levels that led to desired exertion levels;
• receive the second treatment plan;
• and
• implement the second treatment plan at the treatment apparatus, wherein implementing the second treatment plan includes controlling one or more operating parameters of an electromechanical machine of the treatment apparatus in accordance with the second treatment plan.

A computer-implemented method for controlling a treatment apparatus, the computer-implemented method comprising:

• receiving, at a first computing device, a first treatment plan designed to treat erectile dysfunction of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the erectile dysfunction of the user, enable the user to perform an exercise at different exertion levels by using the treatment apparatus;
• receiving data from one or more sensors configured to measure, while the user performs the first treatment plan by using the treatment apparatus, the data associated with the erectile dysfunction of the user;
• transmitting the data to a second computing device executing one or more machine learning models to generate a second treatment plan by predicting, based on a standardized measure of perceived exertion indicated by the data and the sexual performance health issue of the user, exercises that will result in a desired exertion level, wherein the second treatment plan modifies at least one exertion level to achieve the desired exertion level, wherein the standardized measure of perceived exertion includes at least one of a metabolic equivalent of tasks and a Borg rating of perceived exertion, and wherein the one or more machine learning models are trained with labeled inputs comprising first data pertaining to the standardized measure of perceived exertion and other data associated with other users' sexual performance health issues, and labeled outputs comprising other users' exertion levels that led to desired exertion levels;
• receiving the second treatment plan;
• and
• implementing the second treatment plan at the treatment apparatus, wherein implementing the second treatment plan includes controlling one or more operating parameters of an electromechanical machine of the treatment apparatus in accordance with the second treatment plan.