• IMUSEF allows to acquire data from inertial sensors in order to estimate a segment orientation or a joint angle. From this, an event can be defined to be used to control an electrical stimulator.

  • RT_Stim

    • Hybrid simulation architecture gathering in a single framework and consistent time scales both the numerical integration of the continuous model of a bio-mechanical system (bones, joints and muscles) and a model of the hardware and software control architecture, including control tasks, communication protocols and real-time schedulers.
      Simulation run in real-time when possible, and otherwise consistent time scales are generated. The framework is intended to seamlessly evolve from purely software models to hardware-in-the-loop simulation.

  • NeuroPrehens

    • Python application using a framework of input as EMG, IMU, Foot switchs, Camera etc. for controlling a Functional Electrical Stimulation neuroprosthesis in real time closed loop for prehension restoration in hemiplegic stroke patients.


    • ORCCAD is a software environment that allows the design and implementation of the discrete and continuous control of complex robot systems. It also allows the specification and validation of missions to be realized by this system. It is mainly intended for critical real-time applications in robotics, in which automatic control aspects (servoloops, control) have to interact narrowly with the handling of discrete events (exception handling). ORCCAD offers a complete and coherent vertical solution, ranging from the high level specification to real-time code generation.

  • ID-IMU

    • Users can wear movement sensors (IMUs) on different parts of their bodies and train the system to identify specific and arbitrary movements. These can then be used to activate or trigger other devices, such as neuroprostheses, or robots.
      As is, this software can read and classify movement data stored in files according to the AGILIS standards by the time of its publication. The real time reading of sensor data and device actuation is out of scope.


    • Based on the ANT+ communication protocol's Python library, this project unlocks the low frequency limits (4Hz) from the default ANT+ protocol to allow acquisition of the ROTOR "Fast Mode" data streaming (50Hz) through ANT+. This enables data acquisition and monitoring of crank angle, cadence, average power, torque applied on the left and right side independently and more, at frequencies sufficient to analyse the pedalling dynamics inside of a turn rather than having a simple overview of it.


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