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Estimating the elbow angle using shoulder data is very important and valuable in Functional Electrical Stimulation (FES) systems which can be useful in assisting C5/C6 SCI patients. Much research has been conducted based on the elbow-shoulder synergies. The aim of this study was the online estimation of elbow flexion/extension angle from the upper arm acceleration signals during ADLs. For this, a three-level hierarchical structure was proposed based on a new approach, i.e. ‘the movement phases’. These levels include Clustering, Recognition using HMMs and Angle estimation using neural networks. ADLs were partitioned to the movement phases in order to obtain a structured and efficient method. It was an online structure that was very useful in the FES control systems. Different initial locations for the objects were considered in recording the data to increase the richness of the database and to improve the neural networks generalization. The cross correlation coefficient (K) and Normalized Root Mean Squared Error (NRMSE) between the estimated and actual angles, were obtained at 90.25% and 13.64%, respectively. A post-processing method was proposed to modify the discontinuity intervals of the estimated angles. Using the post-processing, K and NRMSE were obtained at 91.19% and 12.83%, respectively.

Online Estimation of Elbow Joint Angle Using Upper Arm Acceleration: A Movement Partitioning Approach