Artificial intelligence can be used to recognize intentions to move at an early stage - for example via inertial sensors that provide data on acceleration and rotation. By training on typical movement sequences, an AI model learns to deduce the underlying intention during a movement.
Such methods are being researched at the IMT in order to control active orthoses or exoskeletons in a predictive and context-sensitive manner. The aim is to achieve intuitive human-machine interaction in which the technology supports people not only by reacting, but also by anticipating - for example in rehabilitation or in everyday life.
View of the original set-up on the test subject with the IMUs attached to the chest, center of gravity and thighs. The sensors record movement data in real time and are positioned in such a way that they provide precise information on body dynamics.
Schematic structure of the system: IMUs on the chest, center of gravity and both thighs are connected via SPI to the microcontroller, which forwards the data to a PC for AI-based processing.
Exemplary data set of a forward movement after a start signal to recognize and annotate the movement intention of a test person, displayed over different time windows.
Architecture overview: Multiple IMU data streams pass through individual chains of CNN, pooling and dense layers. The obtained features are then aggregated by flattening and dense layers to classify human intention.