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Automatic identification of inertial sensor placement on human body segments during walking

OPEN Journal of neuroengineering and rehabilitation | 23 Mar 2013

D Weenk, BJ van Beijnum, CT Baten, HJ Hermens and PH Veltink
Abstract
Background Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically.MethodsWalking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis).Results and conclusions After testing the algorithm with 10-fold cross-validation using 31 walkingtrials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for alower body plus trunk configuration (8 inertial sensors) was trained andtested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree wasalso tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligamentreconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of themethod.
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Concepts
Classical mechanics, Decision tree learning, Motion capture, Human body, Walking
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