Abstract: A ML model arrangement configured for evaluating motion patterns in a sequence of image data structures is described. The ML model arrangement comprises a first ML model configured for predicting a set of key data elements for each image data structure of the sequence of image data structures, a key data element indicating a respective position of a landmark in the image data structure. The ML model arrangement further comprises at least one second ML model, each second ML model being a ML model configured for evaluating a corresponding specific motion pattern. Each second ML model is configured for determining, based on input data comprising at least one of the key data elements predicted for at least one image data structure or data derived therefrom, class labels for each image data structure, said class labels identifying at least one of: at least one motion phase of the specific motion pattern, at least one evaluation point of the specific motion pattern.

Abstract: An editor application configured for setting up at least one evaluation data structure, each evaluation data structure being configured for evaluating a corresponding specific motion pattern in a sequence of image data structures Each evaluation data structure includes a machine learning (ML) model artifact of an exercise specific ML model configured for evaluating the particular physical exercise. Further, the ML model is trained based on a plurality of sequences of image data structures showing different variants of the specific motion pattern for the particular physical exercise, and for each image data structure, a set of key data elements is provided, a key data element indicating a respective position of a landmark in the image data structure, said training being further based on class labels provided for each image data structure.

Abstract: A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person.

Abstract: A method for upgrading a training state of a machine learning model is described, the machine learning model being configured for supporting a model update. The method comprises predicting a set of target data elements based on the input data structure using the machine learning model, a target data element corresponding to a respective characteristic of the input data structure, and determining, for at least one of the predicted target data elements, whether or not a respective target data element is presumably erroneous. The method further comprises determining, for each presumably erroneous target data element detected in the previous step, an estimated corrected target data element, and performing, based on at least one estimated corrected target data element, a step of updating the training state of the machine learning model.

Abstract: A ML model arrangement configured for evaluating motion patterns in a sequence of image data structures is described. The ML model arrangement comprises a first ML model configured for predicting a set of key data elements for each image data structure of the sequence of image data structures, a key data element indicating a respective position of a landmark in the image data structure. The ML model arrangement further comprises at least one second ML model, each second ML model being a ML model configured for evaluating a corresponding specific motion pattern. Each second ML model is configured for determining, based on input data comprising at least one of the key data elements predicted for at least one image data structure or data derived therefrom, class labels for each image data structure, said class labels identifying at least one of: at least one motion phase of the specific motion pattern, at least one evaluation point of the specific motion pattern.

Abstract: An editor application configured for setting up at least one evaluation data structure, each evaluation data structure being configured for evaluating a corresponding specific motion pattern in a sequence of image data structures Each evaluation data structure includes a machine learning (ML) model artifact of an exercise specific ML model configured for evaluating the particular physical exercise. Further, the ML model is trained based on a plurality of sequences of image data structures showing different variants of the specific motion pattern for the particular physical exercise, and for each image data structure, a set of key data elements is provided, a key data element indicating a respective position of a landmark in the image data structure, said training being further based on class labels provided for each image data structure.

Abstract: A ML model arrangement configured for evaluating motion patterns in a sequence of image data structures is described. The ML model arrangement comprises a first ML model configured for predicting a set of key data elements for each image data structure of the sequence of image data structures, a key data element indicating a respective position of a landmark in the image data structure. The ML model arrangement further comprises at least one second ML model, each second ML model being a ML model configured for evaluating a corresponding specific motion pattern. Each second ML model is configured for determining, based on input data comprising at least one of the key data elements predicted for at least one image data structure or data derived therefrom, class labels for each image data structure, said class labels identifying at least one of: at least one motion phase of the specific motion pattern, at least one evaluation point of the specific motion pattern.

Abstract: An editor application configured for setting up at least one evaluation data structure is described, wherein each evaluation data structure is configured for evaluating a corresponding specific motion pattern in a sequence of image data structures. Each evaluation data structure comprises a ML model artifact configured for determining, based on input data comprising key data elements provided for at least one image data structure, class labels for each image data structure, said class labels identifying at least one of: at least one motion phase of the specific motion pattern, at least one evaluation point of the specific motion pattern. A key data element indicates a respective position of a landmark in the image data structure.

Abstract: A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person.

Abstract: A method for monitoring a person performing a physical exercise based on a sequence of image frames showing the person's exercise activity is described. The method comprises the steps of extracting, based on the sequence of image frames, for each image frame a set of body key points using a neural network, the set of body key points being indicative of the person's posture in the image frame, and deriving, based on a subset of the body key points in each image frame, at least one characteristic parameter indicating the progression of the person's movement. The method further comprises detecting a start loop condition by evaluating the time progression of at least one of the characteristic parameters, said start loop condition indicating a transition from a start posture of the person to the person's movement when performing the physical exercise.

Abstract: A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person.

Abstract: A ML model arrangement configured for evaluating motion patterns in a sequence of image data structures is described. The ML model arrangement comprises a first ML model configured for predicting a set of key data elements for each image data structure of the sequence of image data structures, a key data element indicating a respective position of a landmark in the image data structure. The ML model arrangement further comprises at least one second ML model, each second ML model being a ML model configured for evaluating a corresponding specific motion pattern. Each second ML model is configured for determining, based on input data comprising at least one of the key data elements predicted for at least one image data structure or data derived therefrom, class labels for each image data structure, said class labels identifying at least one of: at least one motion phase of the specific motion pattern, at least one evaluation point of the specific motion pattern.

Abstract: An editor application configured for setting up at least one evaluation data structure is described, wherein each evaluation data structure is configured for evaluating a corresponding specific motion pattern in a sequence of image data structures. Each evaluation data structure comprises a ML model artifact configured for determining, based on input data comprising key data elements provided for at least one image data structure, class labels for each image data structure, said class labels identifying at least one of: at least one motion phase of the specific motion pattern, at least one evaluation point of the specific motion pattern. A key data element indicates a respective position of a landmark in the image data structure.

Abstract: A method for upgrading a training state of a machine learning model is described, the machine learning model being configured for supporting a model update. The method comprises predicting a set of target data elements based on the input data structure using the machine learning model, a target data element corresponding to a respective characteristic of the input data structure, and determining, for at least one of the predicted target data elements, whether or not a respective target data element is presumably erroneous. The method further comprises determining, for each presumably erroneous target data element detected in the previous step, an estimated corrected target data element, and performing, based on at least one estimated corrected target data element, a step of updating the training state of the machine learning model.

Abstract: A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person.

Abstract: A method for monitoring a person performing a physical exercise based on a sequence of image frames showing the person's exercise activity is described. The method comprises the steps of extracting, based on the sequence of image frames, for each image frame a set of body key points using a neural network, the set of body key points being indicative of the person's posture in the image frame, and deriving, based on a subset of the body key points in each image frame, at least one characteristic parameter indicating the progression of the person's movement. The method further comprises detecting a start loop condition by evaluating the time progression of at least one of the characteristic parameters, said start loop condition indicating a transition from a start posture of the person to the person's movement when performing the physical exercise.