A method and an electronic apparatus is provided. A first UI indicating a start of an activity is displayed when first data indicating that the activity occurs is received from a first sensor. Information is displayed about a preset time or a preset intensity required for the activity to be converted into exercise. The preset time or the preset intensity is set based on user information related to a characteristic of a user. It is detected that the activity is maintained for at least one of the preset time or at the preset intensity. In response to the detecting that the activity is maintained, a second UI is displayed indicating a start of an exercise. A sensor is activated based on the start of the exercise and exercise history of the user. A function is executed for measuring exercise information by using second data received from the activated sensor.

A pelvis exercising system including: a movable seat, sized and shaped to support a user by his pelvis; a vertical supporter which supports the seat above the ground; a flexible connector mounted between the vertical supporter and the seat; wherein, the seat comprises a front portion and a rear portion extending upwardly away from a central arch, each of the rear and front portions comprises a length which is long enough to support a front portion and rear portion of the user's pelvis, while not significantly restricting the abdomen and lower back from bending in relation to a sagittal plane of the seat. A sensor positioned to measure movement of said seat relative to a base, wherein the sensor provides measured values of the seat relative to the base to a processor; and the processor controls a device based on the measured values. Related apparatus and methods are also described.

A method includes receiving a set of treatment plans. Each treatment plan comprising the set of treatment plans may be associated with a user capable of using a treatment device to perform the associated treatment plan. The method also includes receiving healthcare professional profile information. The method also includes identifying treatment device information for each treatment device capable of being used by a cohort of users associated with respective treatment plans. The method also includes using an artificial intelligence engine, wherein the artificial intelligence engine uses at least one machine learning model configured to generate resource deployment predictions, to generate at least one resource deployment prediction. The at least one machine learning model may generate the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information.

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 system and method for using resistance type exercise machines is provided. One embodiment provides a tailored resistance level (TRL) that indicates a resistance level for that particular user and that particular exercise machine. The TRL, calculated for a particular user and a particular exercise machine, is based on three concurrently measured variables: the user biometric (such as heart rate), cadence of the user's effort, and measured resistance value (measured as the degrees of angular rotation of the resistance knob 104 of the exercise machine).

An exercise apparatus having a frame with attachments to multiple independent bidirectional resistance devices used by arms and/or legs of a user to provide resistance against movement of the user's appendages in two substantially opposed directions. The resistance devices may have a mechanism to control bi-directional resistance. The movement of the user's appendages in bi-directional resistance offers near full body exercise of agonist/antagonist muscles using flexion and extension action of larger muscle groups in a gait pattern, simultaneously. Hand engaging members attach to arm resistance devices and foot engaging members attach to leg resistance devices. The exercise apparatus may include an attached inclined backboard, mat, bench, or cushion to fully or partially support the user. The exercise apparatus may have separate height and/or length adjustment mechanisms for adjusting a range of motion of the user's appendages.

A breathing training, monitoring and/or assistance device is provided for home use. The device is for providing support and guidance during the training of breathing exercises. The device takes account of external data which relates to one or more of: the environmental conditions in which the device is being used; activity information in respect of the user; physiological sensor data about the user which is not related to breathing characteristics.

The invention relates to a device for neurovascular stimulation, at least comprising: at least one brain activity sensor, at least one cardiovascular sensor, at least one computing unit and at least one output unit. The computing unit comprises at least one task algorithm, wherein signals of at least the brain activity sensor and the cardiovascular sensor can be received by the computing unit, and wherein a task, which is in correlation with at least the signals from at least the brain activity sensor and the signals of the cardiovascular sensor, can be determined by means of the task algorithm and can be output by means of the output unit.

Exemplary embodiments of the present disclosure are directed to systems, methods, and computer-readable media configured to autonomously track a round of golf and/or autonomously generate personalized recommendations for a user before, during, or after a round of golf. The systems and methods can utilize course data, environmental data, user data, and/or equipment data in conjunctions with one or more machine learning algorithms to autonomously generate the personalized recommendations.

A computer-implemented method for (1) receiving one or more characteristics of a user, wherein the one or more characteristics comprise personal information, performance information, measurement information, or some combination thereof, (2) generating, using one or more trained machine learning models, a treatment plan for the user, wherein the treatment plan is generated based on the one or more characteristics of the user, and the treatment plan comprises: (i) a dietary plan that is tailored to manage one or more medical conditions associated with the user, and (ii) an exercise plan comprises one or more exercises associated with the one or more medical conditions, and (3) presenting, via a display device, at least a portion of the treatment plan comprising the dietary plan.