Estimation of accelerometer orientation for activity recognition.
Tri-axial accelerometers have been widely used for human activity recognition and classification. A main challenge in accelerometer-based activity recognition is the system dependence on the orientation of the accelerometer. This paper presents an approach for overcoming this challenge by calibrating the accelerometer orientation using pre-defined activities alongside automated correction algorithms. This method includes manipulation of data via rotation matrices estimated from the pre-defined activities. The system is subsequently tested with real data where sensors were placed in the wrong orientation. A control set of correctly oriented sensors were also placed for validation purposes. We show that our approach improves the accuracy from 38% to 92% for the wrongly oriented sensors, when the control sensors achieve 95%. A GUI was also created in order to make the tool easily available to other researchers.
Center of Embedded Networked Sensing, UCLA, USA. AscherFriedman@gmail.com
Ascher
A
Friedman
Nabil
N
Hajj Chehade
Chieh
C
Chien
Greg
G
Pottie
D061725:Accelerometry; D056044:Actigraphy / Q000295:instrumentation* / Q000379:methods; D002138:Calibration; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D008954:Models, Biological; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009068:Movement / Q000502:physiology*; D009949:Orientation / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity