Machine Learning Model Comparisons of User Independent & Dependent Intent Recognition Systems for Powered Prostheses

Developing intelligent prosthetic controllers to recognize user intent across users is a challenge. Machine learning algorithms present an opportunity to develop methods for predicting user's locomotion mode. Currently, linear discriminant analysis (LDA) offers the standard solution in the state-of-the-art for subject dependent models and has been used in the development of subject independent applications. However, the performance of subject independent models differ radically from their dependent counterpart. Furthermore, most of the studies limit the evaluation to a fixed terrain with individual stair height and ramp inclination. In this study, we investigated the use of the XGBoost algorithm for developing a subject independent model across 8 individuals with transfemoral amputation. We evaluated the performance of XGBoost across different stair heights and inclination angles and found that it generalizes well across preset conditions. Our findings suggest that XGBoost offers a potential benefit for both subject independent and subject dependent algorithms outperforming LDA and NN (DEP SS Error: 2.93% +/- 0.49%, DEP TS Error: 7.03% +/- 0.74%, IND SS Error: 10.12% +/- 3.16%, and IND TS Error: 15.78% +/- 2.39%)(p < 0.05). We were also able to show that with the inclusion of extra sensors the model performance could continually be improved in both user dependent and independent models (p < 0.05). Our study provides valuable information for future intent recognition systems to make them more reliable across different users and common community ambulation modes.