A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation

Abstract

Background: Virtual reality (VR) technology along with treadmill training (TT) can effectively provide goal-oriented practice and promote improved motor learning in patients with neurological disorders. Moreover, the VR + TT scheme may enhance cognitive engagement for more effective gait rehabilitation and greater transfer to over ground walking. For this purpose, we developed an individualized treadmill controller with a novel speed estimation scheme using swing foot velocity, which can enable user-driven treadmill walking (UDW) to more closely simulate over ground walking (OGW) during treadmill training. OGW involves a cyclic acceleration-deceleration profile of pelvic velocity that contrasts with typical treadmill-driven walking (TDW), which constrains a person to walk at a preset constant speed. In this study, we investigated the effects of the proposed speed adaptation controller by analyzing the gait kinematics of UDW and TDW, which were compared to those of OGW at three pre-determined velocities.

Methods: Ten healthy subjects were asked to walk in each mode (TDW, UDW, and OGW) at three pre-determined speeds (0.5 m/s, 1.0 m/s, and 1.5 m/s) with real time feedback provided through visual displays. Temporal-spatial gait data and 3D pelvic kinematics were analyzed and comparisons were made between UDW on a treadmill, TDW, and OGW.

Results: The observed step length, cadence, and walk ratio defined as the ratio of stride length to cadence were not significantly different between UDW and TDW. Additionally, the average magnitude of pelvic acceleration peak values along the anterior-posterior direction for each step and the associated standard deviations (variability) were not significantly different between the two modalities. The differences between OGW and UDW and TDW were mainly in swing time and cadence, as have been reported previously. Also, step lengths between OGW and TDW were different for 0.5 m/s and 1.5 m/s gait velocities, and walk ratio between OGS and UDW was different for 1.0 m/s gait velocities.

Conclusions: Our treadmill control scheme implements similar gait biomechanics of TDW, which has been used for repetitive gait training in a small and constrained space as well as controlled and safe environments. These results reveal that users can walk as stably during UDW as TDW and employ similar strategies to maintain walking speed in both UDW and TDW. Furthermore, since UDW can allow a user to actively participate in the virtual reality (VR) applications with variable walking velocity, it can induce more cognitive activities during the training with VR, which may enhance motor learning effects.

Figure 1

Foot velocities along the forward walking direction during overground and treadmill walking. The swing foot velocities are similar during overground and treadmill walking. Foot velocity during stance phase is zero in overground walking (Figure 1(a)) and negative on a treadmill, since it is caused by the treadmill speed (Figure 1(b)). It should be noted that magnitudes of sinusoidal trajectories in both cases are equal. (a) Foot and pelvis velocities (Overground) (b) Foot and pelvic velocities (Treadmill).

Figure 2

Calculation of the average velocity by MSFV. Swing foot velocity can be modelled as a sine function with period T_sw and magnitude V_max; average walking speed during the swing foot period can then be derived as $V_{sw,avg}=\frac{2V_{\mathit{\text{mag}}}}{\pi }$. (a) Swing foot velocity trajectory (b) Average velocity of the swing foot.

Figure 3

Estimation of the pelvis velocity. The average pelvic velocity should be half the swing foot velocity.

Figure 4

Swing foot velocity on a treadmill. The walking speed V_{treadmill,est} on a treadmill can be estimated as $-\frac{V_{sw,max}}{\pi -1}$ by shifting the swing foot velocity trajectory V_sw.

Figure 5

2nd order interpolation for walking velocity estimation. 2^nd order interpolation results for walking velocity estimation with one exemplary subject. (a) Foot and pelvis positions (b) Foot velocity vs. walking velocity (c) Second-order interpolation (d) Foot velocity after interpolation for error compensation vs. walking velocity.

Figure 6

Block diagram of the treadmill speed control algorithm.

Figure 7

Measurement setup. The user’s pelvic and foot motions were captured by the VICON motion capture system at a sampling rate of 120 Hz.

Figure 8

Speed estimation error before/after interpolation. After the proposed second-order interpolation, the speed estimation errors dropped to 0.0663 m/s and the standard deviations of the average estimation errors were also reduced to 0.01 (box-whisker plot).

Figure 9

Effect of walking speed on temporal-spatial gait parameters. Temporal-spatial gait parameters between UDW and TDW are not significantly different for all gait speeds, while there was significant difference between OGW and UDW/TDW. (a) Step length (b) Walk ratio (c) Cadence (d) Swing time. OGW vs. UDW: cadence at 1.0 m/s (p = 0.02) and 1.5 m/s (p = 0.01), swing time at 0.5 m/s (p = 0.02), 1.0 m/s (p = 0.001), and 1.5 m/s (p = 0.001), and walk ratio at 1.0 m/s (p = 0.03). OGW vs. TDW: cadence at 1.5 m/s (p = 0.04), swing time at 1.0 m/s (p = 0.03) and 1.5 m/s (p = 0.001), step length at 0.5 m/s (p = 0.001) and 1.5 m/s (p = 0.03).

Figure 10

Effect of walking speed on pelvis motion. For the overall comparisons of the three modalities, the average total magnitude and variability for the pelvic acceleration did not differ. (a) Average peak magnitude of pelvic acceleration (b) Variability of pelvic acceleration.