Version: 1.8.1


This section outlines how to interact with our movement analysis service. Firstly, VayAnalyser has to be created, where a specific exercise can be configured. The API is written based on an event VayListener that can be added to the analyser. Throughout an exercise, our movement analysis service provides information about the user's session state that is further explained in a seperate page. When an event is triggered by the API, relevant information is received within the event data. In the following section, the most relevant event data are highlighted briefly


The most basic output from the VAY service is the human pose, which is estimated by our AI algorithm. The positions are stored for each body point within the PoseEvent and are provided by the onPose callback in the VayListener. These points are useful for visualization and custom analysis.

Metric Value

For every metric that is tracked during the configured exercise, the API returns a detailed analysis with the onMetricValues callback containing the current value and the metric that the value belongs to. For a ROM metric, the value can e.g. be used to obtain the user's ROM of a movement.


The onFeedback callback provides feedback immediately after our AI algorithm has detected an undesired body position. For example, if the user squats too low, the corrective feedback is sent before the user is again in an upright position where the repetition is completed. This can occur each time the user reaches this undesired position.

Activity Summary

After an exercise is completed, a summary with all feedback that were collected during the activity are sent with the onActivity callback. The activity performed by the user is classified into either a VayRepetition, where the exercise is completed with a full range, or a VayAttempt, where the user's range of motion or form deviated too much from the target exercise. Hence the purpose of a VayAttempt is to inform the user that the movement was recognized and what is missing to be counted as repetition.

If a counter is used to show the number of repetitions, we recommend to only consider repetitions in the counter and not attempts.

Example: Squat

Assume you are using the Swift API to analyse a squat. An example of proper alignment of a squat is keeping the back upright during execution to prevent injuries. Therefore, the AI checks if the metric, specifically the angle between the back and the vertical axis, is in the desired range. With the onMetricValues callback, the API returns an understandable value for this metric that is checked. Furthermore, one can see if the user does not keep the back in an upright position by listening to the onFeedback callback or the onActivity callback. The onActivity callback summarises all feedback after the activity is complete, and classifies the activity into a VayRepetition or a VayAttempt. Finally, the corrective feedback would be "Keep your torso upright".

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