Gait Speed Monitoring for Aging Conditions

 Gait speed is a particularly important parameter in geriatrics, as it is the number one predictor of mortality in adults over 65 years old, with differences of just a couple tenths of a meter per second predicting statistically significant outcome differences. The most common method for gait speed estimation in medical research and clinical practice is to simply use a stopwatch and a tape measure. This typically provides good accuracy but is insufficient for applications that require more continuous and longitudinal data, especially given that speed and many other gait parameters can vary significantly day-to-day and even hour-to-hour in geriatric and gait impaired populations. It is therefore highly desirable to be able to estimate gait speed using inertial BSNs and to do so with a resolution of better than 0.1 m/s.

However, for inertial BSNs, while simple temporal gait parameters such as step time and double stance time can be easily extracted from accelerometer and gyroscope data, parameters that depend on both temporal and spatial information are much more challenging to accurately assess due to integration drift (e.g., acceleration to velocity and position, rotational rate to angular displacement) and node placement uncertainty. And gait speed is one such spatio-temporal parameter, as it includes both stride length and stride time. With the abovementioned challenges, inertial BSNs have been prevented significant progress towards accurate gait speed estimation.

Methodology:

To tackle this problem, mounting calibration using simple pre-defined movements and rotation matrices to ensure accurate spatial analysis regardless of how the BSN nodes are placed on-body. In addition, application specific methods are developed and applied that leverage knowledge of biomechanics and human gait – including temporal knowledge of gait phases – in order to minimize integration drift and to better model stride length. 

  • Mounting Calibration:

  • Gait Cycle Segmentation and Drift Elimination:

    Gait cycle extraction is critical to extract parameters such as gait phase, step time, and stride length, all of which are important for gait speed estimation. Based on the assumption that during the foot on ground event, the angular velocity should be near zero, a local maximum peak detection algorithm is selected for gait cycle extraction. (This portion of the gait cycle was chosen because it also supports integration drift cancelation) To suppress the ripples in the gyroscope signal, a zero-phase, 3rd order, Butterworth low-pass filter with a cutoff frequency of 3Hz is used. The cutoff frequency is determined empirically by inspecting the spectrum of the gyroscope signal, in which the main frequency components lie below 3Hz. The spike representing the Heel-Strike event is removed after the filtering, illuminating the foot-on-ground identified by the peak detection algorithm. Then the time point of foot-on-ground event is recorded and the original gyroscope signal is kept for later integration.

  • Refined Gait Model
To better examine the human gait model, the gait cycle is divided into 8 phases as shown in Figure 5. Research has shown that the angular velocity of the shank reaches its maximum when the leg is fully extended, and the angle of the shank reaches its maximum after this when the leg is flexed. These two events do not overlap in time as illustrated in Figure 5. and verified by the data in Figure 6. Thus using leg length and the maximum shank angle for computing step length during backward swing (the simplified pendulum model in Figure 2. ) is imprecise. This discrepancy suggests a more refined compound pendulum model to compute step length as shown in Figure 7.

       

As shown in Figure 7. , the step length calculation of our model differs from the model in the reference. One stride’s length is defined as the sum of the step length of the right leg and the step length of the left leg in one gait cycle. The total distance travelled is the sum of the stride lengths of all cycles. Finally, the average gait speed is the distance travelled divided by the total time elapsed.

Results:

The RMSE is computed comparing treadmill speed, with a resolution of 0.2 MPH (0.09 m/s) from 1MPH to 3MPH, to the calculated gait speed. The accuracy of the proposed model was significantly higher than that of the reference model, which commonly overestimates gait speed.  The largest RMSE was only 0.095m/s after mounting calibration as shown in . However, at very low and high speeds, the thigh angle can be critical for controlling the step length. At very low speeds, the thigh tends to swing forward ahead of plumb line so as to maintain a very short step length on the treadmill, resulting in a step length that is shorter than predicted, and vice versa at high speeds. Thus, correction factors are needed to further reduce errors at very slow or fast walking speeds.

Future Work:

Work is underway to evaluate the estimation accuracy among various gaits, including both healthy and pathological gait at a greater range of speeds (including running), through experiments with more subjects. For healthy gait, a training set of data can be used to calibrate the algorithm for each individual subject. For certain types of pathological gait, including those with shuffling, a wide base, and out-of-plane motion, more refined gait models will be developed based on biomechanical knowledge.

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