The present invention aims to provide a blood flow analysis device capable of accurately identifying a change in the blood flow in a muscle caused by resistance exercise performed by a subject. A blood flow analysis device according to the present invention specifies a resistance exercise period during which a muscle of a subject present at a position to be measured by a motion sensor is performing a resistance exercise, and analyzes a light detection signal in the resistance exercise period, to thereby calculate a feature amount of a change in the blood flow in the muscle of the subject in the resistance exercise period (see FIG. 1).

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 may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan, a patient interface. and a computing device configured to: receive treatment data pertaining to the user who uses the treatment device to perform the treatment plan; identify at least one enhanced component using the treatment data; generate an enhanced environment using the at least one enhanced component and the treatment plan; output at least one aspect of the enhanced environment to at least one of the patient interface and another interface; receive subsequent treatment data pertaining to the user; and selectively modify at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan using the subsequent treatment data.

Systems and methods for positioning one or more sensors on a user. The system has user sensors, apparatus sensors, and treatment sensors. A processing device, executing computer readable instructions stored in a memory, cause the processing device to: generate an enhanced environment representative of an environment; receive apparatus data representative of a location of the apparatus in the environment; generate an apparatus avatar in the enhanced environment; receive user data representative of a location of the user in the environment; generate a user avatar in the enhanced environment; receive treatment data representative of one or more locations of the treatment sensors in the environment; generate, treatment sensor avatars in the enhanced environment; calculate a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user; and generate instruction data representing an instruction for positioning the treatment sensors at the treatment location.

A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan and a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session. The computer-implemented system may also include 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; write to an associated memory, for access by an artificial intelligence engine, the treatment data; receive, from the artificial intelligence engine, at least one prediction; identify a threshold corresponding to the at least one prediction; and, in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.

Methods, systems and devices are provided for motion-activated display of messages on an activity monitoring device. In one embodiment, method for presenting a message on an activity monitoring device is provided, including the following method operations: downloading a plurality of messages to the device; detecting a stationary state of the device; detecting a movement of the device from the stationary state; in response to detecting the movement from the stationary state, selecting one of a plurality of messages, and displaying the selected message on the device.

A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

A method includes a first acquisition step of acquiring a heart rate of a target person who does exercise, a second acquisition step of acquiring an electrocardiographic waveform of the target person who does the exercise, a third acquisition step of acquiring a predetermined feature amount from the acquired electrocardiographic waveform, and an estimation step of estimating a maximum heart rate of the target person based on a relationship between the predetermined feature amount and the acquired heart rate. In the estimation step, the maximum heart rate of the target person is estimated based on a heart rate corresponding to an inflection point in a change of the predetermined feature amount with respect to the acquired heart rate.

A method of monitoring a user's activity level, and improving the user's health with a wearable device designed to be worn all day. The method comprises in one embodiment determining that the user is wearing the wearable device, and monitoring movements of the user using a motion sensing system in the wearable device. The method further determines when the user has not moved in a period of time, and alerts the user to move, when the user has not moved from an ergonomic activity in a preset period of time. The method further counts steps taken by a user, and identifies and tracks aerobic exercise activity.