Enhancing Locomotion-Mode Recognition and Transition Prediction with (Bio)Mechanical Sensor Fusion for Intelligent Prosthetic Knees.

The ability to continuously recognize locomotion modes and accurately predict transition intentions is essential for intelligent prosthetic knees. In this study, an innovative framework for locomotion recognition and transition prediction was introduced based on fusing mechanical (inertial measurement unit (IMU)) and biomechanical (force myography (FMG)) signals. This framework integrated an FMG-IMU dual-modal sensing system implemented on a prosthetic knee, enabling simultaneous acquisition of FMG-IMU fusion signals from transfemoral amputees during dynamic walking. A novel feature-driven CNN-BiLSTM model was developed and trained as the classifier, enhancing the accuracy and efficiency of locomotion mode prediction. The RelifF-MI algorithm was employed to optimize FMG-IMU features, ensuring efficient data processing by effectively eliminating feature redundancy. The framework was evaluated using data collected from eight transfemoral amputees. The results demonstrated that the fusion of FMG-IMU dual-modal gait data with the feature-driven classifier significantly improved classification performance, achieving an overall average recognition accuracy of 98.51% and an average prediction time of 274 ms (21.82% of the gait cycle) across five locomotion modes-level walking (LW), stair ascent/descent (SA/SD), and ramp ascent/descent (RA/RD)-and eight transitions between these modes. These promising results highlighted the considerable potential of the proposed method for application in prosthetic knee control.