So this research is motivated by two things, one is to overcome the challenges in signal processing for getting accurate spatial information, Another is simply because gait speed itself is an important parameter in gait analysis. Esp. now it’s been identified as no.1 predictor in frailty assessment in geriatrics.Predicting frailty with a couple of tens meters/s.And it’s an application require high accuracy and high resolutionAnd would benefit a lot from longitudinal continuous monitoring
Before we jump into this research, let’s look at the current devices on gait speed estimation.Nike+ provides a pedometer solution to assess cadence, and FitBit uses accelerometer for cadence.Both of the two solutions require a predefined calibration to get step length, And the accuracy remains questionable.Garmin Forerunner provides a GPS solution, and gives an RMS of 0.05m/s in velocity assessment,But it’s limited to outdoor use only.And don’t forget, in clinic, we can always use stopwatch and tape if you don’t have other fancy devices,Needless to say it’ll be limited to clinic use
Then we compute stride length by this reference model, Where they assume human gait can be considered as symmetric single pendulum model.For example, when the shank swing backwards to the maximum, We obtain d1rs.Forward maximum, we get d2rs.By summing left shank step length and right shank step length we can get the stride length based on the trignometry provided here.
By taking this 8 frames/second picture of many gaits, we found when the shank reaches the maximum, the leg isn’t straight.
Based on this observation, we propose this gait model, using only shank length as the hypotenuse at the backward swing, the forward swing remains the same.
Extracting Spatio-Temporal Information from Inertial Body Sensor Networks for Gait Speed Estimation
Extracting Spatio-Temporal Information from Inertial Body Sensor Networks for Gait Speed Estimation Shanshan Chen, Christopher L. Cunningham, Bradford C. Bennett, John Lach UVA Center for Wireless Health University of Virginia BSN, 20111
Research Statement Signal processing challenge to obtain accurate spatial information from inertial BSNs Gait speed as an example to extract accurate spatio-temporal information Gait speed is the No. 1 predictor in frailty assessment require high gait speed accuracy desire for continuous, longitudinal gait speed monitoring2
Inertial BSN for Gait Speed Estimation TEMPO 3.1 inertial BSN platform developed at the University of Virginia4
Contributions Refined human gait model by leveraging biomechanics knowledge Improve accuracy without increasing signal processing complexity Mounting calibration procedure to correct mounting error Practical in experiments Improved gait speed estimation accuracy by combining the two methods5
Outline Current Gait Speed Estimation Method Gait Cycle Extraction and Integration Drift Cancelation Stride Length Computation by Reference Model Refined Human Gait Model Mounting Calibration Experiment & Results6
Gait Cycle & Integration Drift Cancelation Gyroscope signals on the sagittal plane Use foot on ground to find gait cycle boundaries Numerically easy to pick up – local maximum Helpful for canceling integration drift Shank angle is near zero and does not contribute to the stride length calculation when foot is on ground Assume linear drift7
Stride Length Computation Reference Model S. Miyazaki, “Long-Term Unrestrained Measurement of Stride Length and Walking Velocity Utilizing a Piezoelectric Gyroscope”8
Outline Current Gait Speed Estimation Method Gait Cycle Extraction & Integration Drift Cancelation Stride Length Computation by Reference Model Refined Human Gait Model Mounting Calibration Experiments and Results9
Validation of Mounting Calibration Algorithm Mounting Measured by Measurement Position Rotated Proposed Algorithm Error of Angle Around Y-axis 0° -0.072° 0.072° 15° 16.286° 1.286° 30° 27.896° 2.104° 45° 43.954° 1.046° 60° 58.078° 1.922° 75° 74.737° 0.263° Pendulum Model to simulate 90° 90.461° 0.461° node rotation on shank Rotate around z-axis with Measurement Error of Angle 2.5 controlled degree 2 Determine the rotation by 1.5 Mounting Calibration Algorithm 1 0.5 Achieve an average error of ~1° 0 0° 15° 30° 45° 60° 75° 90° Measurement Error of Angle16
Outline Current Gait Speed Estimation Method Gait Cycle Extraction and Integration Drift Cancelation Stride Length Computation by reference model Refined Human Gait Model Mounting Calibration Experiment & Results17
Treadmill Control of Speed Is gait on treadmill different from on ground? Gyroscope signals collected on treadmill show no significant difference from those collected on ground18
Experiments on Treadmill Subject with poorly mounted Inertial BSN nodes performing mounting calibration on treadmill Two subjects, a taller male subject and a shorter female subject Two trials were conducted for each subject, one with well-mounted nodes and another with poorly-mounted nodes to validate mounting calibration Speeds ranging from 1 to 3 MPH with a 0.2 MPH (0.1m/s) increment for 45 seconds at each speed19
Before/After Mounting Calibration Before Mounting Calibration After Mounting Calibration • Badly mounted nodes causes underestimation of gait speed – attenuation of signal due to bad mounting • Mounting Calibration has correct the significant estimation error21
Results of Two Subjects • Significantly reduced RMSE compared to the reference model • Overestimate at lower speeds and underestimate at higher speeds • Overestimate taller subject’s speeds more than the shorter subject22
Gait Model at Different Speeds The thigh angle can be critical for controlling the step length Elimination of thigh angle results in underestimation of stride length at high speed Vice versa at low speed High Speed Use thigh nodes to increase accuracy if invasiveness is not a concern How accurate is accurate enough? Depends on application requirement23
Results of Two Approaches Double Pendulum at Initial Swing Single Pendulum at Toe-Off Single Pendulum Model at Toe-off • Better than the reference model • Still overestimate the gait speed24
Future Work Need more subjects, more gait types, and more gait speeds For certain types of pathological gait, include those with shuffling, a wide base, and out-of-plane motion More refined gait models will be developed based on biomechanical knowledge Evaluate if a training set of data can be used to calibrate the algorithm for each individual subject25
Conclusion Achieving an RMSE of 0.09m/s accuracy with a resolution of 0.1m/s Proposed model shows significant improvement in accuracy compared to the reference model Mounting calibration corrected the estimation error Leveraging biomechanical domain knowledge simplifies signal processing26