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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/23517757
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Clinical Trial
. 2013 Mar 21:10:31.
doi: 10.1186/1743-0003-10-31.

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

Dirk Weenk  1 , Bert-Jan F van Beijnum, Chris T M Baten, Hermie J Hermens, Peter H Veltink
Affiliations
Clinical Trial

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

Dirk Weenk et al. J Neuroeng Rehabil. .

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.

Methods: Walking 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 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method.

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Figures

Figure 1
Figure 1
The three steps used for identifying the inertial sensors. Inputs are the measured 3D acceleration (ss) and angular velocity (ωs), both expressed in sensor coordinate frame. Outputs of the identification process are the classes, in this case the body segments to which the inertial sensors are attached.
Figure 2
Figure 2
Decision tree for segment identification (step 1). Constructed with the J4.8 algorithm of Weka. 31 walking trials of 10 different healthy subjects were used. As testing option a 10-fold cross-validation was used. From the (31·17=)527 inertial sensors, 514 were correctly classified (97.5%). The numbers at the leaves (the rectangles containing the class labels) indicate the number of sensors reaching that leaf and the number of incorrectly classified sensors. For example, 26 sensors reach the sternum leaf, of which one is not a sensor attached to the sternum.
Figure 3
Figure 3
Decision trees for left and right upper arm and upper leg identification (step 2). To identify left and right upper arms, from both upper arm sensors the correlation of the acceleration in z direction with the sternum sensor orientation about the x-axis was used (left). For the upper legs the orientation of the pelvis sensor was used (right). For these segments, all sensors were identified correctly (100% accuracy).
Figure 4
Figure 4
Decision tree for segment identification (step 1), when using a lower body plus trunk configuration. 31 walking trials were used (31·8=248 sensors). 10-fold cross-validation was used for testing the tree, resulting in 248 (100%) correctly classified inertial sensors.
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