1. 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, 2011
1

2. 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 monitoring
2

3. Prevailing Technology
--for Gait Speed Estimation
 Nike+®  Fit-Bit®:
Pedometer, cadence Accelerometer, cadence
 Garmin Forerunner ®301
Wearable wrist GPS, velocity  Stopwatch and Tape
3

4. Inertial BSN for Gait Speed Estimation

TEMPO 3.1 inertial BSN platform
developed at the University of Virginia
4

5. 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 methods
5

6. 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 & Results
6

7. 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 drift
7

8. Stride Length Computation
Reference Model
S. Miyazaki, “Long-Term Unrestrained Measurement of Stride Length
and Walking Velocity Utilizing a Piezoelectric Gyroscope”
8

9. 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 Results
9

10. Inspection of Gait Phase
10

11. 11

12. Refined Compound Model
Reference Model
12

13. 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 & Results
13

14. Mounting Calibration
 Nodes could be rotated 20°~30° from ideal orientation
 Attenuate the signal of interest on the sensitive axis
Ideal Mounting
14 Non-ideal Mounting

15. Mounting Calibration Methods

15

16. 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 Angle
16

17. 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 & Results
17

18. 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
ground
18

19. 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 speed
19

20. Results

21. 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 error
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22. 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 subject
22

23. 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 requirement
23

24. 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 speed
24

25. 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 subject
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26. 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 processing
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27. Thanks!
Q&A