Characteristics of a Dual Force Plate System Embedded in a Six Degree of Freedom Motion Platform Emily H. Sinitski *,***
Edward D. Lemaire *,**
The Ottawa Hospital Research Institute CRRD 505 Smyth Road, Ottawa, ON K1H 8M2 firstname.lastname@example.org
University of Ottawa Faculty of Medicine 451 Smyth Road Ottawa, ON K1H 8M5
Abstract—Motek Medical’s CAREN-Extended system is a virtual environment primarily used in physical rehabilitation and biomechanical research. This virtual environment integrates a motion capture system and a six degree of freedom motion platform equipped with a dual-belt treadmill and two force plates. This research describes performance characteristics associated with a “treadmill – motion platform” configuration that should be considered for appropriate measurement, and effective design of research protocols and rehabilitation CAREN applications. Keywords—CAREN; virtual reality; force plate; 6 DOF motion platform; treadmill
The Computer Aided Rehabilitation Environment (CAREN – Motek Medical, Amsterdam, Netherlands) is a virtual environment and a rehabilitation aid used in research and clinical settings. This system enables clinicians to utilize innovative rehabilitation techniques and to obtain comprehensive evaluation measurements that provide insights into a patient’s recovery process . In addition to clinical use, researchers employ the CAREN for scientific inquiry, furthering knowledge of walking stability , traumatic brain injury , and neurorehabilitation . The Ottawa Hospital Rehabilitation Centre is equipped with a CAREN-Extended system. This CAREN configuration integrates a motion capture system, six degree of freedom motion platform, instrumented treadmill, and a virtual scene. The CAREN system allows researchers to address a number of novel research questions by enabling manipulation of the standing or walking surface, virtual scene, and by developing interactive environments. The CAREN system’s discrete technologies are not new, but aggregating these systems for rehabilitation is currently unique. Understanding the performance characteristics and limitations of this system is crucial for effective design of research protocols or formulation of clinical treatment plans. One challenge presented by the CAREN-Extended This project was partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). 978-1-4673-5197-3/13/$31.00 ©2013 IEEE
University of Ottawa Department of Mechanical Engineering 161 Louis Pasteur Ottawa, ON K1N 6N5
framework is including force plates within the motion platform. In a typical motion analysis laboratory, force plates are secured in a level walkway and are isolated from electrical interference and environmental vibrations. However, CAREN-Extended force plates are embedded in a treadmill and actuated platform where platform and treadmill operation affect ground reaction force (GRF) signals. This purpose of this research was to examine how force measurements are affected by CAREN-Extended system operation. The performance tests included force plate noise characteristics during platform motion, ambulation, treadmill operation, and baseline drift. Understanding these characteristics enables researchers and clinicians to fully utilize the system’s potential. II.
A. Equipment Description The CAREN-Extended system at The Ottawa Hospital Rehabilitation Centre (Fig. 1) incorporates a 12-camera (MX T20S) Vicon motion capture system (Vicon, Oxford, UK), 3m diameter Sarnicola hydraulic platform capable of six degrees of motion (Sarnicola Simulation Systems, Inc., Conklin, NY), Bertec 1x2m dual-belt treadmill instrumented with two force plates (Bertec Corp., Columbus, OH), 180° projection screen, and four F10 AS3D projectors (projectiondesign, Fredrikstad, Norway). The Sarnicola motion platform consists of six hydraulic actuators connected in a Stewart configuration. The actuators are controlled independently to enable motion in six degrees of freedom: sway or medial-lateral translation (ML), surge or anterior-posterior translation (AP), heave or vertical translation (VT), pitch, yaw, and roll. B. Data Collection The Vicon Nexus 1.8.2 motion capture system (Vicon, Oxford UK) was used to digitize reflective marker positions at 100 Hz and record force plate data at 1000 Hz.
velocity, angular acceleration, and angular deceleration were calculated for rotation trials. Transition time was the time between initial platform movement and when the platform stopped moving. Maximum force was calculated after subtracting an offset due to platform mass. The force offset was determined by filtering the original force signal using a 1 Hz 4th order Butterworth low pass filter. After subtracting the offset, the force signal was zero when the platform was stationary. Force signals when the platform was moving represented the force due to platform acceleration.
(a) Motion capture system Projection screen
(b) Platform Treadmill components Force plates
Figure 1. CAREN-Extended system (a) and motion platform embedded with a dual-belt treadmill system and two force plates (b).
Three reflective markers were secured to the motion platform. All marker data were reconstructed and labeled using Vicon Nexus. Force plate baseline was reinitialized prior to each performance test. The D-Flow 3.10.0 Platform Module (Motek Medical, Amsterdam, NL) was used to control platform orientation and position. The D-Flow safety filter parameter was set to 1, which is equivalent to a 1 Hz 2nd order Butterworth low pass filter applied to all platform movements in real-time. Visual3D 3.96.4 (C-Motion Inc., Rockville, MD) and Matlab 2010a (The Mathworks, Matwick, MA) were used to analyze each performance test. Visual3D was used to model the platform as a kinematic object (segment) and to calculate platform translation and rotation. 1) Platform Acceleration The platform was translated or rotated to examine the effects of platform acceleration on force plate signals. At the start of each trial, the platform was positioned at the origin and then translated or rotated (Table I). After a few seconds, the platform was translated or rotated back to the starting position. Platform velocity, acceleration, and deceleration were calculated for translation trials, and platform angular TABLE I. Translation (cm) Rotation (deg)
PLATFORM TRANSLATION AND ROTATION RANGES. ML and AP
5, 10, 15, 20, 25
5, 10, 15, 20, 25, 30
Pitch and Roll
5, 10, 15, 20
5, 10, 15
2) Platform Transition Since large platform accelerations may not be appropriate for people with mobility disabilities, this test examined characteristics as the platform transition quickly and gradually. A step input signal (range of 0–7° pitch) was filtered at 0.8, 0.4, 0.3, and 0.1 Hz using a 2nd order Butterworth low pass filter (D-Flow Filter Module) to generate four platform orientation curves with different slopes. Visual3D was used to calculate the maximum angular velocity, acceleration, deceleration, transition time, and platform overshoot. The effect of platform motion on force plate output was also examined after the platform mass offset was subtracted. Transition time was calculated as described in the previous section. Peak transition time was also calculated from the time the platform velocity was greater than zero to the time the platform reached peak position. Data windows were selected when the platform was stationary (1 s window) and when the platform was moving (1–6 s window) to calculate force plate noise. Mean and two standard deviations were calculated from the force data for each window. 3) Noise with Increasing Treadmill Speed A single trial was collected while the treadmill speed was increased from 0–5 m/s, in 0.5 m/s increments. The increment duration was approximately 10 s. Mean and two standard deviations over 5000 samples of force data were calculated for each speed. The power spectral density of the force plate signal was also examined for the ML, AP, and VT directions. 4) Noise During Ambulation Force plate data were recorded while a person walked at 1.1 m/s on one force plate to determine the effect of impact forces applied to the platform at heel strike on the other force plate. The mean and two standard deviations of force data over five heel strikes were calculated to examine the effect of heel contact on the unloaded force plate. A power spectral density of the unloaded force plate signal was also examined. 5) Baseline Drift Force plate drift was examined with and without a person walking on the platform. After the amplifiers were on for four hours, the force plates were reinitialized and a trial was recorded to determine a baseline signal. A 5 s trial was recorded every five minutes for a 40 minute period without a person walking on the platform. During another independent test session, a person walked on the treadmill for five trials where each trial was five minutes in length. Five seconds of
platform was moving. The most gradual platform orientation curve (0.1 Hz) increased the force plate noise compared to a stationary platform, but the noise was less than 10 N. Steeper platform orientation curves (0.2, 0.4, and 0.8 Hz) also increased force plate noise, particularly in the AP and VT directions.
PEAK PLATFORM ACCELERATION AND DECELERATION.
Translation 0-5 cm 0-10 cm 0-15 cm 0-25 cm 0-30 cm
Peak Acceleration (m/s2) ML AP VT 0.63 0.64 0.66 1.30 1.33 1.35 1.96 2.1 2.01 2.77 2.7 2.58 3.36 3.24 2.66
Peak Deceleration (m/s2) ML AP VT 0.39 0.42 0.43 0.84 0.89 0.89 1.32 1.34 1.38 1.91 1.87 1.82 2.54 2.56 2.45
Rotation 0-5° 0-10° 0-15° 0-20°
Peak Acceleration (deg/s2) Pitch Roll Yaw 93 115 73 179 140 150 207 241 198 256 297 -
Peak Deceleration (deg/s2) Pitch Roll Yaw 50 102 54 118 103 113 147 181 145 205 229 -
The peak transition time was 6 s for the most gradual platform orientation curve (0.1 Hz) and 1.2 s for the steepest orientation curve (0.8 Hz). A 0.2–0.3° platform angle overshoot was also observed for all platform orientation curves tested. C. Noise Characteristics With Increasing Treadmill Speed Force plate noise was less than 3 N when the treadmill belts were stationary (0 m/s) and increased with increasing treadmill belt speed. Force plate noise was less than 10 N for 0.5–2.0 m/s treadmill speeds and was between 10 and 40 N for speeds of 2.5–5.0 m/s. Power spectral density showed that most signal noise due to the treadmill components was larger than 20 Hz and can be filtered using a low pass filter with a 20 Hz cut-off frequency.
force data were recorded between each trial, when the participant was off the force plates. The mean force data between trials were examined. III.
RESULTS D. Noise Characteristics During Ambulation When a person walked on the platform, the platform tracking markers exhibited more vertical translation than marker variation observed without a person walking on the platform. While a person walked on one force plate, the force signal artifact observed on the unloaded force plate was equivalent to 3 ± 40 N, averaged over all force channels (Fig. 2). The power spectral density of the force signal artifact resulted in frequency components between 0–40 Hz, which overlaps the frequency components of ground reaction forces  and cannot be easily removing by filtering techniques. In Fig. 2, low pass filtering reduced the force signal artifact to less than 20 N. This is under the threshold commonly used to remove background noise during post-processing for biomechanical gait analysis.
A. Platform Acceleration Maximum platform velocity, acceleration, and deceleration increased as the distance the platform travelled increased (Table II). Maximum deceleration was smaller than maximum acceleration for the same distance travelled (Table II). Peak force also increased as platform acceleration increased and was largest in the direction of platform motion. Peak force ranged from -99 to -559 N for in the ML for ML platform translations, -136 to -509N in the VT for VT platform translations, and -100 to -562 N in the AP for AP platform translations. The other force channels were less affected by platform translation, with peak forces ranging from 9–58 N. Peak forces were largest in the AP for pitch (-81 to -205 N), ML and VT for roll (56 to 246 N), and AP for yaw (-71 to -231 N). The time between platform movement initiation and completion was approximately 1 s for all platform conditions tested.
E. Drift Without a person walking on the platform, ground reaction forces drifted less than 3 N over a 40 minute period and the moments drifted approximately 2 Nm. When a person walked on the platform, the AP and ML ground reaction forces drifted less than 5 N over a 25 minute period, but VT drifted 5 N every five minutes. The moments drifted approximately 5 Nm every five minutes.
B. Platform Transition As expected, more abrupt orientation curves (larger cutoff frequencies) resulted in larger platform velocity and acceleration (Table III). Force plate output increased as platform acceleration increased, with the largest value in the AP direction. When the force plate was stationary, forces were less than 3 N. Force signals increased when the TABLE III.
PLATFORM VELOCITY, ACCELERATION, TRANSITION TIME, OVERSHOOT, AND FORCES USING FOUR LOW PASS FILTER CUT-OFF FREQUENCIES TO TRANSITION THE PLATFORM FROM A LEVEL SURFACE TO A 7° INCLINE.
ML Mean±2SD (N) -0.01±6.4
AP Mean±2SD (N) 0.00±9.8
VT Mean±2SD (N) -0.03±8.4
Cut-off frequency (Hz) 0.1
Peak platform velocity (deg/s) 2.5
Peak platform acceleration (deg/s2) 11
Time to peak platform position (s) 6.1
Time to final platform position (s) 10.6
filter parameter allows. The method of filtering a step input signal successfully generated platform transitions that varied in intensity. Generally, a 0.2 Hz cut-off frequency produced a comfortable, 3 s transition time. For people with mobility disabilities, transition speeds ideally vary in a virtual application; for example, slower transition speeds may be required for transitioning from a level platform to steeper slopes. The platform transitioned quickly to peak orientation and took longer to reach final platform orientation. Although a small overshoot was observed, the platform moved very slowly during the final transition period (approximately 5% of peak velocity). Additionally, force plate noise during this period was smaller compared to the noise and velocity during the time to reach peak platform orientation. Treadmill operation also affected force signals. In this study, the noise due to the treadmill components was small, particularly for speeds less than 2.0 m/s. These data were comparable to the instrumented treadmill results reported by Paolini  and can be removed by low pass filtering.
Figure 2. Unfiltered force signal (a) and filtered force signal (b) from the unloaded force plate while a person walked on the other force plate (medial-lateral, anterior-posterior, and vertical axes). Each peak in the unfiltered force signal represents a foot strike. Force data were filtered using a 4th order Butterworth low pass filter with a cut-off frequency of 20 Hz (b). The red line at 0 N on all graphs represents the force signal without a person walking on the force plates.
This research examined a number of contributing factors that affect output from force plates that are embedded in a dual-belt treadmill and motion platform. Understanding what factors affect force plate signals and how to minimize these inferences is important for clinicians and researchers when developing rehabilitation applications or formulating research protocols. Moving the platform progressively larger distances using the D-Flow Platform Module resulted in increasingly larger platform accelerations and the transition time remained similar for all trials. Force plate output also increased as platform acceleration increased and was largest in the predominant direction of platform movement. Larger platform accelerations may be useful to create more challenging environments for postural balance or walking stability applications. Depending on the application, minimizing forces due to platform acceleration or gradual platform transitions may be required for patient safety and to minimize force signal noise. The D-Flow Platform Module safety filter parameter can be used to change the transition by filtering the input platform orientation signal; however, this setting affects all platform movement in an application. Furthermore, an application may require different platform transitions during a trial or slower transitions than the Platform Module safety
The artifact observed on the unloaded plate due to walking could not be easily removed with standard filtering techniques. This artifact was measured when a person walked on the other force plate and may reflect platform movement and structural vibrations during foot strike. Additionally, it is important to note both force plates are secured to the same base, so any vibrations through the structure will affect both force plates. While the majority of this artifact could be removed with a 20 Hz low pass filter, filtering will have less effect for people with larger impact forces due to mass, walking velocity, or pathological gait. Another consideration is force plate signal baseline drift. Force and moment signals did not drift appreciably when the force plates were unloaded. However, there was a consistent drift of 5 N and 5 Nm every five minutes when the force plates were in continuous use. This is important for long data capture sessions. If reinitializing the force plates throughout a session is impractical, post processing techniques could be used to subtract a dynamic offset due to drift (e.g. using force data during the swing phase). V.
This research highlighted a number of considerations when utilizing force measurements from force plates embedded in a motion platform. These considerations are important for outcome validity and to avoid misinterpretation of kinetic data. With a platform and dual-belt instrumented treadmill configuration, motion and signal artifact are observed even when the platform was stationary. Although it is known that platform acceleration affects force signals, there is no accepted method in biomechanics to account for these effects. Future research is necessary to account for platform acceleration and obtain valid kinetic data for the new generation of virtual rehabilitation systems.
The authors would like to thank Courtney Bridgewater, Andrew Smith, and Joao Tomas for their assistance with the data collection sessions. VII. REFERENCES  B. J. Darter and J. M. Wilken, "Gait training with virtual reality-based
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Published on Jul 3, 2013
Published on Jul 3, 2013
2013 IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA)