Daily Locomotion Recognition and Prediction: A Kinematic Data-Based Machine Learning Approach

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Abstract
More versatile, user-independent tools for recognizing and predicting locomotion modes (LMs) and LM transitions (LMTs) in natural gaits are still needed. This study tackles these challenges by proposing an automatic, user-independent recognition and prediction tool using easily wearable kinematic motion sensors for innovatively classifying several LMs (walking direction, level-ground walking, ascend and descend stairs, and ascend and descend ramps) and respective LMTs. We compared diverse state-of-the-art feature processing and dimensionality reduction methods and machine-learning classifiers to find an effective tool for recognition and prediction of LMs and LMTs. The comparison included kinematic patterns from 10 able-bodied subjects. The more accurate tools were achieved using min-max scaling [-1;1]interval and “mRMR plus forward selection” algorithm for feature normalization and dimensionality reduction, respectively, and Gaussian support vector machine classifier. The developed tool was accurate in the recognition (accuracy > 99% and > 96%) and prediction (accuracy > 99% and > 93%) of daily LMs and LMTs, respectively, using exclusively kinematic data. The use of kinematic data yielded an effective recognition and prediction tool, predicting the LMs and LMTs one-step-ahead. This timely prediction is relevant for assistive devices providing personalized assistance in daily scenarios. The kinematic data-based machine learning tool innovatively addresses several LMs and LMTs while allowing the user to self-select the leading limb to perform LMTs, ensuring a natural gait.
Funding Information
  • Fundação para a Ciência e a Tecnologia (SFRH/BD/108309/2015, SFRH/BD/147878/2019)
  • FEDER Funds through the Programa Operacional Regional do Norte
  • European Regional Development Fund (NORTE-01-0145-FEDER-030386)
  • European Regional Development Fund (POCI-01-0145-FEDER-006941)

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