Lower-limb amputations can cause a plethora of obstacles that lead to a lower quality of life. Implementing machine learning techniques means advanced prosthetics can contribute to facilitating the lives of those that live with lower-limb amputations. Using the publicly available HuGaDB data set, the current study investigates several classification models (random forest, neural network, and Vowpal Wabbit) to predict the locomotive intentions of individuals using lower-limb prostheses. The results of this study show that the neural network model yielded the highest accuracy, comparable precision, and recall scores to the other models. However, the Vowpal Wabbit model's advantage in speed may allow for other, more practical implementations in practice. These findings provide insight into the advantages of specific classification models over others in predicting the intentions of specific movements during locomotive transitions. These findings present direct comparisons of several machine learning methods, identifying the strengths and weaknesses of each classification model tested.
Dominguez, Joaquin; Kim, Richard; and Slater, Robert
"The Role of Machine Learning in Improved Functionality of Lower Limb Prostheses,"
SMU Data Science Review: Vol. 7:
1, Article 5.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss1/5
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