Details

Autor(en) / Beteiligte

• McQuire, Jamie
• Watson, Paul
• Wright, Nick
• Hiden, Hugo
• Catt, Michael

Titel

Uneven and Irregular Surface Condition Prediction from Human Walking Data using both Centralized and Decentralized Machine Learning Approaches

Ist Teil von

• 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, p.1449-1452

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEL

Beschreibungen/Notizen

• Gait data collected using wearable sensors offers non-intrusive, affordable, real-time monitoring of human motion. Recognizing surface conditions from wearable sensor data has the potential to help systems discriminate between 'poor quality' walking data. This research investigates the predictive capabilities of machine learning models, trained on both centralized and decentralized datasets, at categorizing uneven and irregular surface conditions. The results showed that machine learning classification algorithms, trained with data originating from a single sensor positioned on the left-shank, were able to accurately discriminate between different types of surface conditions. We found the Support Vector Machine, when trained with the data centralized, had a test-set accuracy of 94%. Federated Learning offers a way to increase privacy and security for healthcare applications by avoiding the centralization of data. Our simulated federated Deep Neural Network converged to a test-accuracy of 85%, which was 8% less than the centralized counterpart.