Erik Maartens

Continuous three dimensional analysis of running mechanics during a marathon by means of inertial magnetic measurement units to objectify changes in running mechanics

Recent developments in wearable and wireless sensor technology allow for a continuous three dimensional analysis of running mechanics in the sport specific setting. The present study is the first to demonstrate the possibility of analyzing three dimensional (3D) running mechanics continuously, by means of inertial magnetic measurement units, to objectify changes in mechanics over the course of a marathon. Three well trained male distance runners ran a marathon while equipped with inertial magnetic measurement units on trunk, pelvis, upper legs, lower legs and feet to obtain a 3D view of running mechanics and to asses changes in running mechanics over the course of a marathon. Data were continuously recorded during the entire 42.2km (26.2Miles) of the Marathon. Data from the individual sensors were transmitted wirelessly to a receiver, mounted on the handlebar of an accompanying cyclist. Anatomical calibration was performed using both static and dynamic procedures and sensor orientations were thus converted to body segment orientations by means of transformation matrices obtained from the segment calibration. Joint angle (hip, knee and ankle) trajectories as well as center of mass (COM) trajectory and acceleration were derived from the sensor data after segment calibration. Data were collected and repeated measures one way ANOVA׳s, with Tukey post-hoc test, were used to statistically analyze differences between the defined kinematic parameters (max hip angle, peak knee flexion at mid-stance and at mid-swing, ankle angle at initial contact and COM vertical displacement and acceleration), averaged over 100 strides, between the first and the last stages (8 and 40km) of the marathon. Significant changes in running mechanics were witnessed between the first and the last stage of the marathon. This study showed the possibility of performing a 3D kinematic analysis of the running technique, in the sport specific setting, by using inertial magnetic measurement units. For the three runners analyzed, significant changes were observed in running mechanics over the course of a marathon. The present measurement technique therefore allows for more in-depth study of running mechanics outside the laboratory setting.

Appearance of breast cysts in planar geometry photoacoustic mammography using 1064-nm excitation

Abstract. In the search for improved imaging modalities for detection and diagnosis of breast cancer, a high negative prediction value is also important. Photoacoustic (optoacoustic) imaging is a relatively new technique that has high potential for visualizing breast malignancies, but little is known about the photoacoustic appearance of benign lesions. In this work, we investigate the visibility of benign breast cysts in forward-mode photoacoustic mammography using 1064-nm light, as currently applied in the Twente photoacoustic mammoscope. Results from (Monte Carlo and k-wave) simulations and phantom measurements were used to interpret results from patient measurements. There was a strong agreement among the results from simulations, phantom, and patient measurements. Depending on the absorption contrast between cyst and breast tissue, cysts were visible as either one or two confined high-contrast areas representing the front and the back of the cyst, respectively. This edge enhancement is most likely the consequence of the local sudden change in the absorbed energy density and Grüneisen coefficients. Although the current forward-mode single-wavelength photoacoustic mammoscope cannot always unambiguously discriminate cysts from malignancies, this study reveals specific features of cysts compared to malignancies, which can be exploited for discrimination of the two abnormalities in future modifications of the imager.

Estimation of vertical ground reaction forces and sagittal knee kinematics during running using three inertial sensors

Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture systems, it becomes possible to measure kinematics in any environment. However, kinetic information could not be provided with such technology. Furthermore, numerous body-worn sensors are required for a full-body motion analysis. The aim of this study is to examine the validity of a method to estimate sagittal knee joint angles and vertical ground reaction forces during running using an ambulatory minimal body-worn sensor setup. Two concatenated artificial neural networks were trained (using data from eight healthy subjects) to estimate the kinematics and kinetics of the runners. The first artificial neural network maps the information (orientation and acceleration) of three inertial sensors (placed at the lower legs and pelvis) to lower-body joint angles. The estimated joint angles in combination with measured vertical accelerations are input to a second artificial neural network that estimates vertical ground reaction forces. To validate our approach, estimated joint angles were compared to both inertial and optical references, while kinetic output was compared to measured vertical ground reaction forces from an instrumented treadmill. Performance was evaluated using two scenarios: training and evaluating on a single subject and training on multiple subjects and evaluating on a different subject. The estimated kinematics and kinetics of most subjects show excellent agreement (ρ>0.99) with the reference, for single subject training. Knee flexion/extension angles are estimated with a mean RMSE <5°. Ground reaction forces are estimated with a mean RMSE < 0.27 BW. Additionaly, peak vertical ground reaction force, loading rate and maximal knee flexion during stance were compared, however, no significant differences were found. With multiple subject training the accuracy of estimating discrete and continuous outcomes decreases, however, good agreement (ρ > 0.9) is still achieved for seven of the eight different evaluated subjects. The performance of multiple subject learning depends on the diversity in the training dataset, as differences in accuracy were found for the different evaluated subjects.