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Fig. 2 | Journal of NeuroEngineering and Rehabilitation

Fig. 2

From: Neuro-cognitive assessment of intentional control methods for a soft elbow exosuit using error-related potentials

Fig. 2

Control system for the soft pneumatic elbow exosuit. \(\mu _{tri}\) and \(\mu _{bi}\) indicate muscle activations determined from the Delsys Trigno EMG system in the triceps and biceps muscles respectively. \(Q_{UA}\) and \(Q_{FA}\) indicate the quaternion data collected from the IMU sensors for the upper arm and forearm respectively. \(P_{exo}\) is the exosuit pressure reading from the pressure sensor housed within the control box. These sensor readings feed into the high-level control scheme that determines the required torque \(\tau _r\) (gravity: \(\tau _g\) or myoprocessor: \(\tau _m\)), as well as the exosuit torque \(\tau _{exo}\). The output of the high-level controller is an interaction torque \(\tau _i\) that acts as an input to the low-level controller. At this stage, the low-level controller aims to minimize the interaction between the user and exosuit assistance by minimizing the interaction torque via a PID controller. The output of the low-level controller is the opening percentages of the inlet valve \(\phi _{in}\) and outlet valve \(\phi _{out}\) respectively which get relayed to the servomotors controlling the inlet and outlet valves for air flow

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