Musculoskeletal analysis physically estimates human movement and, beyond traditional posture estimation, can simulate down to the internal muscle level of an individual. This reduces the complexities associated with traditional motion capture setups, multiple cameras, and the need for calibration, allowing for usage with just single-view image and video data or even live footage.
Developers can use the data output from the MYoACT Engine—joint angles, ground reaction forces, joint torques, and muscle activities—to create applications related to medical or sports fields where human movement is integral. Furthermore, the engine is equipped with convenient data analysis libraries, including middleware for medical calculations based on estimated data, as well as for enhancing sports training programs.

Applications incorporating the MYoACT ENGINE are designed to simultaneously utilize multiple data types in a time series, including time, muscle activation levels, joint torque, external force, and the coordinates of objects being held. By ensuring interactivity among these data types, it becomes possible to synchronize and use multiple sets of data simultaneously. The MYoACT Engine automatically processes the temporal synchronization of the available data and also offers libraries for data preprocessing and postprocessing, along with interfaces for customization.

The point cloud data provided by the MYoACT Engine is three-dimensional and also represents vector data indicating the direction of force. A 3D viewer is provided as a standard feature to represent this data. In the 3D viewer, you can playback the analysis data in a time series and freely move the viewpoint 360 degrees.
Additionally, the 3D viewer supports various formats, allowing modifications to the 3D human model data, representations of just the muscles, or models of just the skeletal structure.

The data obtained from the MYoACT Engine consists of three-dimensional coordinate data, the direction of force, estimates of muscle activation, and external force. This raw data does not have significant meaning by itself. To utilize this data in applications, it is necessary to imbue this data set with meaning.
For example, if the movement of the knee is estimated to be poor, the data could be used to predict knee diseases. In this way, the system provides libraries that estimate potential issues from the data or make estimates that lead to better improvements.
Being able to analyze multiple sets of analysis results simultaneously allows for further extraction of issues by comparing oneself with others, the past with the present, or the anticipated future, enabling a deeper understanding and identification of potential problems.

The data generated by the MYoACT Engine is vast, producing data for each joint, including coordinates, torque, direction, and more. The MYoACT Engine offers functionalities to graph these data sets for visualization. It allows for the simultaneous overlay of multiple data sets or the concurrent analysis of separate data.
To facilitate data analysis, the engine also provides interfaces for easily managing the parameters set for analysis, storage for metadata, and assets.

The data generated by the MYoACT Engine achieves accuracy equivalent to expensive motion capture equipment, electromyography sensors, and force plates. This level of precision is suitable for academic evaluation by researchers in academic positions and can be utilized not only for applications intended for end-users but also for product development and presentations in basic research.
Furthermore, it includes data analysis libraries commonly used in statistics for data conversion, estimation, and testing, necessary for data analysis.