Fig. 3

Graphical overview of the reviewed literature, grouped by the same four topics used in the manuscript. The references are grouped by application (knee, ankle or both), the EMG-driven control working principle and type of neuro-control (type of movement restored). Some of the most relevant works are detailed additionally by listing the employed additional inputs, number of EMG channels and controller delays, if known. Five classes of additional data inputs were recognized (apart from EMG signals). Motion Capture (MoCap) and sensorized treadmills with force platforms are used to acquire body kinematic and dynamic data; IMU sensors are usually integrated to obtain orientation of the lower limb segments; footswitch and loadcells are installed to acquire the force exchanged with the environment; encoders and goniometers are used to measure joint angles; finally, visual or haptic feedback is sometimes provided to the user to encourage a correct employment of the device. On the lower part of the graph, the most used measurements for each control class validation is displayed. NS not stated; ^†referenced work belong to a hybrid model-based and pattern recognition control class, see “EMG-driven working principles” section