Method for the Determination of Relative Joint Axes for Wearable Inertial Sensor Applications

Wearable IMU sensing systems have widely been used in the study of human motion. For example, gait analysis using wearable inertial sensor systems is a tool used by clinicians to discriminate between typical and pathological walking. Similarly, key descriptors can be identified in spontaneous kicking to distinguish between typical and atypical motor development in infants. Oftentimes in human applications, precise placement of inertial sensors is difficult due to the irregular shape of human limbs. Without precise placement and alignment of the inertial sensors, meaningful joint kinematic data are difficult to extract as the orientation of the joint axes are unknown in the sensor's local frame. So, for applications where precise alignment may not be possible, a necessary first step is to identify the joint axes with respect to the local frame. In this work, we propose a method for the identification of joint axes for multiple degree of freedom (multi-DOF) joints in a kinematic chain using acceleration and angular rate data. This method couples a thresholding activity detection algorithm with a principal component analysis (PCA) dimensionality reduction technique. Furthermore, this method is validated on mimicked kicking data from a NAO robot. This method can determine joint axes of a kinematic chain from simultaneous movement data within an error ratio of 0.09.