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 daytoday and even hourtohour 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 spatiotemporal 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 predefined movements and rotation matrices to ensure accurate spatial analysis regardless of how the BSN nodes are placed onbody. 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 zerophase, 3rd order, Butterworth lowpass 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 HeelStrike event is removed after the filtering, illuminating the footonground identified by the peak detection algorithm. Then the time point of footonground event is recorded and the original gyroscope signal is kept for later integration.

Refined Gait Model
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:
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 outofplane motion, more refined gait models will be developed based on biomechanical knowledge.