Paper Info
Title
Kalman-Filter-Based Walking Distance Estimation for a Smart-Watch
Abstract
A novel walking distance estimation algorithm using the inertial sensors of the smart-watch is proposed. Firstly, the peaks of the norm of the accelerometer and gyroscope signals are detected. Due to arm swing, walking step detection using these peaks are not reliable. A Kalman filter is used to combine with the peak detection algorithm applied on the accelerometer and gyroscope norm peaks and robustly detect walking steps even if there is large arm swing. Walking distance is estimated using walking step time and walking length relationship. The proposed algorithm was tested on 25 subjects: each subject walked 50 m six times with different walking speed and different arm swing speed. The standard deviation of walking distance estimation error is 3.9 m (without person dependent calibration) and 1.9 m (with person dependent calibration) for a 50m distance.
Year
DOI
Venue
2016
10.1109/CHASE.2016.21
2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
Keywords
Field
DocType
Kalman filter,walking distance estimation,smart-watch,inertial sensors,accelerometer,gyroscope signals,arm swing,walking step detection,peak detection algorithm,walking step time,walking length,walking speed
Gyroscope,Control theory,Accelerometer,Kalman filter,Step detection,Inertial measurement unit,Engineering,Standard deviation,Preferred walking speed,Swing
Conference
ISBN
Citations 
PageRank 
978-1-5090-0944-2
0
0.34
References 
Authors
11
3
Name
Order
Citations
PageRank
Young Soo Suh118320.62
Ebrahim Nemati28415.30
Majid Sarrafzadeh33103317.63