Effects of belt speed on the body's center of mass motion relative to the center of pressure during treadmill walking.

Treadmills are often used in clinical settings to improve walking balance control in patients with gait impairments. However, knowledge of the effects of belt speed on balance control remains incomplete. The current study determined such effects in terms of inclination angles (IA) and the rate of change (RCIA) of the center of mass (COM) motion relative to the center of pressure (COP) in twelve healthy adults at five belt speeds, including the subjects' preferred walking speed (PWS), as measured using a motion capture system and an instrumented treadmill. The values of IAs and RCIAs at key gait events and their average values over single-limb support (DLS) and double-limb support (DLS) were compared between speeds using one-way repeated measures analysis of variances. While the COM-COP controls were different between SLS and DLS, they were inter-related to form an integrated whole. Among the belt speeds, the range of frontal IA during SLS was smallest at the PWS (p<0.05). With increasing speed, most variables of the sagittal IAs and RCIAs, and of the frontal RCIAs during DLS showed a linearly increasing trend (p<0.001). A linearly decreasing trend was found in the frontal IA at toe-off and in the average frontal RCIA during SLS (p<0.05). The PWS appeared to be the best compromise between frontal stability during SLS and smooth weight-transfer during DLS. The current results provide useful baseline data for selecting speeds according to training needs, and may be helpful for developing protocols for gait retraining for patients with gait impairment.

Evaluation of three force-position hybrid control methods for a robot-based biological joint-testing system.

BACKGROUND: Robot-based joint-testing systems (RJTS) can be used to perform unconstrained laxity tests, measuring the stiffness of a degree of freedom (DOF) of the joint at a fixed flexion angle while allowing the other DOFs unconstrained movement. Previous studies using the force-position hybrid (FPH) control method proposed by Fujie et al. (J Biomech Eng 115(3):211-7, 1993) focused on anterior/posterior tests. Its convergence and applicability on other clinically relevant DOFs such as valgus/varus have not been demonstrated. The current s1tudy aimed to develop a 6-DOF RJTS using an industrial robot, to propose two new force-position hybrid control methods, and to evaluate the performance of the methods and FPH in controlling the RJTS for anterior/posterior and valgus/varus laxity tests of the knee joint. METHODS: An RJTS was developed using an industrial 6-DOF robot with a 6-component load-cell attached at the effector. The performances of FPH and two new control methods, namely force-position alternate control (FPA) and force-position hybrid control with force-moment control (FPHFM), for unconstrained anterior/posterior and valgus/varus laxity tests were evaluated and compared with traditional constrained tests (CT) in terms of the number of control iterations, total time and the constraining forces and moments. RESULTS: As opposed to CT, the other three control methods successfully reduced the constraining forces and moments for both anterior/posterior and valgus/varus tests, FPHFM being the best followed in order by FPA and FPH. FPHFM had root-mean-squared constraining forces and moments of less than 2.2 N and 0.09 Nm, respectively at 0° flexion, and 2.3 N and 0.14 Nm at 30° flexion. The corresponding values for FPH were 8.5 N and 0.33 Nm, and 11.5 N and 0.45 Nm, respectively. Given the same control parameters including the compliance matrix, FPHFM and FPA reduced the constraining loads of FPH at the expense of additional control iterations, and thus increased total time, FPA taking about 10 % longer than FPHFM. CONCLUSIONS: The FPHFM would be the best choice among the methods considered when longer total time is acceptable in the intended clinical applications. The current results will be useful for selecting a force-position hybrid control method for unconstrained laxity tests using an RJTS.

Calibration of an instrumented treadmill using a precision-controlled device with artificial neural network-based error corrections.

Instrumented treadmills (ITs) are used to measure reaction forces (RF) and center of pressure (COP) movements for gait and balance assessment. Regular in situ calibration is essential to ensure their accuracy and to identify conditions when a factory re-calibration is needed. The current study aimed to develop and calibrate in situ an IT using a portable, precision-controlled calibration device with an artificial neural network (ANN)-based correction method. The calibration device was used to apply static and dynamic calibrating loads to the surface of the IT at 189 and 25 grid-points, respectively, at four belt speeds (0, 4, 6 and 8 km/h) without the need of a preset template. Part of the applied and measured RF and COP were used to train a threelayered, back-propagation ANN model while the rest of the data were used to evaluate the performance of the ANN. The percent errors of Fz and errors of the Px and Py were significantly decreased from a maximum of -1.15%, -1.64 mm and -0.73 mm to 0.02%, 0.02 mm and 0.03 mm during static calibration, respectively. During dynamic calibration, the corresponding values were decreasing from -3.65%, 2.58 mm and -4.92 mm to 0.30%, -0.14 mm and -0.47 mm, respectively. The results suggest that the calibration device and associated ANN will be useful for correcting measurement errors in vertical loads and COP for ITs.

A method for estimating subject-specific body segment inertial parameters in human movement analysis.

An optimization-based, non-invasive, radiation-free method was developed for estimating subject-specific body segment inertial properties (BSIPs) using a motion capture system and two forceplates. The method works with accurate descriptions of the geometry of the body segments, subject-specific center of pressure (COP) and kinematic data captured during stationary standing, and an optimization procedure. Twelve healthy subjects performed stationary standing in different postures, level walking and squatting while kinematic and forceplate data were measured. The performance of the current method was compared to three commonly used predictive methods in terms of the errors of the calculated ground reaction force, COP and joint moments using the corresponding predicted BSIPs. The current method was found to be capable of producing estimates of subject-specific BSIPs that predicted accurately the important variables in human motion analysis during static and dynamic activities. With the differences in the BSIPs from the current method, the mean COP errors were less than 5 mm during stationary standing postures, while those from the existing comparative methods ranged from 11 to 25 mm. During dynamic activities, the existing methods gave COP errors three times as large as the proposed method, with up to 2.5 times RMSE in joint moments during walking. Being non-invasive and using standard motion laboratory equipment, the current method will be useful for routine clinical gait analysis and relevant clinical applications, particularly in patient populations that are not targeted by the existing predictive methods.

A new device for in situ static and dynamic calibration of force platforms.

In human motion analysis, in situ calibration of the force plate is necessary to improve the accuracy of the measured ground reaction force (GRF) and center of pressure (COP). Few existing devices are capable of both static and dynamic calibration of the usually non-linear GRF and COP errors, while are also easy to move and/or set up without damaging the building. The current study developed a small device (160 cm × 88 cm × 43 cm) with a mass of 50 kg, equipped with auxiliary wheels and fixing suction pads for rapid deployment and easy set-up. A PC-based controller enabled quick movement and accurate positioning of the applied force to the calibration point. Static calibration at 100 validation points and dynamic calibration of a force plate were performed using the device. After correction by an artificial neural network (ANN) trained with the static data from another 121 points, the mean errors for the GRF were all reduced from a maximum of 0.64% to less than 0.01%, while those for the COP were all reduced from a maximum of about 1.37 mm to less than 0.04 mm. For dynamic calibration, the mean errors for the GRF were reduced from a maximum of 0.46% to less than 0.28%, while those for the COP were reduced from a maximum of 0.95 mm to less than 0.11 mm. The results suggest that the calibration device with the ANN method will be useful for obtaining more accurate GRF and COP measurements in human motion analysis.