MoveNet: A Deep Neural Network for Joint Profile Prediction Across Variable Walking Speeds and Slopes

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 11
• Main Authors: Joshi, Rishabh Bajpaiand Deepak, Joshi, Deepak
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Artificial neural networks; Automation; Coders; Depth profiling; Estimation; Exoskeletons; Flat surfaces; Hip; human locomotion; Inclination angle; Joints (anatomy); Knee; Legged locomotion; Modules; Neural networks; Predictions; Predictive models; Representations; robot control; Slopes; Walking
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3073720

An exoskeleton needs reference joint angle profiles at various speeds and slopes for its application in real-world scenarios. Recording these profiles and their implementation in the control system of an exoskeleton are time-consuming and complex processes. Therefore, there is a need for an artificial system that can predict subject-specific joint angle profiles for various real-world scenarios from a minimum amount of input data. This study aims to propose a predictive neural network (NN) called MoveNet, for joints angle profile prediction across variable walking speeds and slopes. MoveNet consists of three NN modules, namely, encoder, mapper, and decoder. The encoder module is trained to convert the knee joint angle profile into its 6-D latent representation. The mapper module is trained to map the latent representation of angle profile at zero degrees angle of inclination (AOI) to latent representation of angle profile at the target AOI. The decoder module is trained to predict an angle profile from its given latent representation. The proposed model successfully predicted knee joint angle profiles at three walking speeds (0.8, 1, and 1.2 m/s) and nine AOI ranging from −10° to 10°. MoveNet obtained root mean squared error of 3.24 ± 1.19° and mean absolute error of 2.66 ± 1.00° from tenfold cross validation. These results suggest that artificial intelligence can predict subject-specific knee joint angle profiles for variable slopes at a given walking speed from the data of knee joint angle profiles recorded at a flat surface.