[TMP-099] Prediction of static and perturbed reach goals from movement kinematics

A research project on movement kinematics, where the goal could change its position during action execution in unpredictable way.

Micro-project in Humane-AI-Net

Short description

In this microproject, the action goal could change its spatial position during action execution and in unpredictable way. Using a double neural network approach, the present results contribute to the objectives of the Task 2.2 of WP2 at two levels.

In the first level, we described the temporal structure of action goal recognition in static and perturbed condition of reaching from movement kinematics of the index and wrist in the 3-dimensional space. This first achievement contributes to the recognition of the action goal in a context that is known (static targets) or not, a priori (perturbed targets).

In the second level, we predicted future trajectory of the movement given previous action path. This second achievement contributes to the creation of the bases for the design of a system able to monitor activity in a natural human workspace and extract prediction of future actions in situations that could require human-AI interaction.

 

Additional information

We measured the kinematics of reaching movement in 12 participants towards visual targets located in the 3D-space. The targets could remain static or be perturbed at the movement onset. Experiment 1: by a supervised recurrent neural network, we tested at what point, during the movement, it was possible to accurately detect the reaching endpoints given the instantaneous x, y, z coordinates of the index and wrist. The classifier successfully predicted static and perturbed reaching endpoints with progressive increasing accuracy across movement execution (mean accuracy = 0.560.19, chance level = 0.16). Experiment 2: using the same network architecture, we trained a regressor to predict the future x, y, z position of the index and wrist given the actual x, y, z positions.  X, y and z components of index and wrist showed an average Rsquared higher than 0.9 suggesting an optimal reconstruction of future trajectory given the actual one.

Tangible outputs

  • a model descriptive of reaching movement in static and dynamic conditions
  • a research paper submitted on a relevant journal of the sector

This Humane-AI-Net micro-project was carried out by Università di Bologna (UNIBO, Patrizia Fattori), German Research Centre for Artificial Intelligence (DFKI, Elsa Kirchner)

 

Technical categories
Planning and scheduling
Technology Readiness Level
TRL 1-2 (Basic research)

Lead institution

Last updated
Wed, 11/20/2024 - 13:54
Contact details
Matteo Filippini (matteo.filippini7@unibo.it)

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