A computer-implemented method for providing training guidance in connection with a physical exercise includes: measuring, by using a heart activity sensor, heart activity of a user during a physical exercise; measuring, by using a bioimpedance measurement sensor, bioimpedance of the user during the physical exercise; computing, by a processing circuitry, a stroke volume from the bioimpedance synchronized to a cardiac cycle of the user by using the measured heart activity; computing, by the processing circuitry, training intensity by using at least the computed the stroke volume; comparing, by the processing circuitry, the training intensity with at least one threshold; and outputting at least one training guidance instruction on the basis of the comparison.

Motion activity data is collected from at least one sensor. An initial motion activity classifier function is applied to the motion activity data to produce an initial motion activity posteriorgram. Pre-processing and segmenting the motion activity data into windows produces segmented motion activity data from which sensor specific features are extracted. An updated motion activity classifier function is generated from the extracted sensor specific features. Subsequent motion activity data is also collected from the at least one sensor, and the updated motion activity classifier function is applied to the subsequent motion activity data to produce an updated motion activity posteriorgram.

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.

A method of monitoring user effectiveness during cyclic operation of an exercise machine (10) having a pair of human limb-operable drive members (31,32) coupled for cyclic movement. The method comprises the steps of: (i) measuring and monitoring the drive force applied to the drive members (31,32) during cyclic movement of the drive members (31,32) and generating one or more force signals indicative thereof; (ii) using the one or more force signals to derive for each cycle of movement of the drive members (31,32) at least one maximum user effectiveness coefficient signal indicative of variation of force relative to a maximum force value measured for that cycle of movement and at least one minimum user effectiveness coefficient signal indicative of variation in force relative to a minimum force value measured for that cycle of movement; (iii) using the maximum and minimum user effectiveness coefficient signals derived in step (ii) to generate a user effectiveness score signal that is indicative of user effectiveness in driving cyclic movement of the drive members; and (iv) using the user effectiveness score signal in recording, displaying, printing, storing, downloading, uploading or transmitting one or more indicia representative of the user effectiveness score signal.

According to one embodiment, an apparatus comprising a portable monitoring device to be affixed to a user. The portable monitoring device including: 1) a set of one or more sensors to generate sensor data indicative of physical activity of a user when the portable monitoring device is affixed to the user; and 2) processing circuitry coupled with the set of sensors, to detect that the user has been sedentary for a period of time, and cause the portable monitoring device to alert the user responsive to the detection to encourage the user to move.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

A method and apparatus for improving recognition of anaerobic sections of exercise and to providing feedback on training load or training effect reflecting energy systems used and trained during exercise. The method determines an ‘oxygen debt’ like cumulative physiological sum (usually training effect TE as EPOC value) brought on by a change in a body homeostasis and its aerobic and anaerobic values. Particularly anaerobic value may be determined by a procedure, where a total EPOC (or TRIMP) is determined as a total sum and an aerobic part calculated in a known manner, is deducted from the total sum.

The present invention concerns an ergometer (10) comprising at least one training assembly, each assembly comprising: a training element (11), -at least one hydraulic cylinder (12) comprising a pressure chamber (32), said training element (11) being linked to said at least one hydraulic cylinder (12) such that a hydraulic pressure value applied in the pressure chamber (32) of said or at least one of said corresponding cylinders determines a force opposing the movement of said training element (11) by a user, and at least one control unit (13, 14) for supplying the pressure chamber (32) of said or one corresponding hydraulic cylinder (12) with pressurised hydraulic fluid such that said pressure chamber (32) has a chosen pressure value during at least a part of the muscle exercises carried out by the user, said control unit (13, 14) being linked to said pressure chamber (32) of the corresponding hydraulic cylinder (12) by a hydraulic fluid supply circuit.

Methods, devices, and computer programs are presented for consolidating overlapping data provided by multiple sources. One method includes operations for associating a plurality of devices to a user, each device operable to capture data associated with the user, and for receiving, from the plurality of devices, captured data about a first activity associated with a first period of time. Additionally, the method includes operations for detecting that the received captured data from two or more devices provide overlapping information about the first activity, and for evaluating one or more rules for consolidating the overlapping information to produce consolidated data. The consolidated data provides a unified view of the first activity during the first period of time. The consolidated data is stored in permanent storage and made available for presentation to the user.