Real-Time Locomotion Transitions Detection: Maximizing Performances with Minimal Resources

Assistive devices, such as exoskeletons and pros- theses, have revolutionized the field of rehabilitation and mobil- ity assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and en- hancing user experience. We present an approach for real- time transition detection, aimed at optimizing the processing- time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method im- proves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The effi- cacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, rein- forcing its potential for integration into practical applications.