Methodologies for determining minimal grasping requirements and sensor locations for sEMG-based assistive hand orthosis for SCI patients.

In this paper, we address two of the most important challenges in development and control of assistive hand orthosis. First, supported by experimental results, we present a method to determine an optimal set of grasping poses, essential for grasping daily objects. Second, we present a method for determining the minimal number of surface EMG sensors and their locations to carry out EMG-based intention recognition and to control the assistive device by differentiating between the hand poses.

Statistical method for prediction of gait kinematics with Gaussian process regression.

We propose a novel methodology for predicting human gait pattern kinematics based on a statistical and stochastic approach using a method called Gaussian process regression (GPR). We selected 14 body parameters that significantly affect the gait pattern and 14 joint motions that represent gait kinematics. The body parameter and gait kinematics data were recorded from 113 subjects by anthropometric measurements and a motion capture system. We generated a regression model with GPR for gait pattern prediction and built a stochastic function mapping from body parameters to gait kinematics based on the database and GPR, and validated the model with a cross validation method. The function can not only produce trajectories for the joint motions associated with gait kinematics, but can also estimate the associated uncertainties. Our approach results in a novel, low-cost and subject-specific method for predicting gait kinematics with only the subject's body parameters as the necessary input, and also enables a comprehensive understanding of the correlation and uncertainty between body parameters and gait kinematics.