Movement Classification based on inertial sensor data

Transcription

1 Movement Classification based on inertial sensor data Bewegungsklassifikation auf Basis von Inertialsensoren Marcus Bocksch / Jasper Jahn / Jochen Seitz Fraunhofer IIS

2 Motivation What is happening? Movement Classification based on inertial sensor data 2

3 Motivation What is happening? Inertial Sensor System as an addition to the Ko-TAG localization component It helps in the moment of detecting a VRU It provides additional information to improve situation awareness Movement Classification based on inertial sensor data 3

4 Motivation The system is carried by the VRU Two use cases are emphasized: Detection of misuse, e.g. a TAG is thrown on the street. Detection of entering a car and deactivation of the TAG Movement Classification based on inertial sensor data 4

5 Outline Hardware Feature extraction Movement classification Examples running, entering a car, possible misuse Discussion of results Practical application for navigation Movement Classification based on inertial sensor data 5

6 Hardware 3-Axis Gyroscope (2000 deg/s) 3-Axis Accelerometer (4 g) 3-Axis Magnetometer (0.7 Gauss) TI OMAP Processor (opt.) Bluetooth (opt.) WiFi Linux OS Movement Classification based on inertial sensor data 6

7 Feature Extraction as an Alternative Inertial navigation for one minute without motion errors grow quickly short-term stability Movement Classification based on inertial sensor data 7

8 Feature Extraction magnitude of accelerations features Movement Classification based on inertial sensor data 8

9 Classification Movement Classification based on inertial sensor data 9

10 Outline Hardware Feature extraction Movement classification Examples running, entering a car, possible misuse Discussion of results Practical application for navigation Movement Classification based on inertial sensor data 10

11 Example Running Different features are evaluated for classification Running can be classified by analyzing: Magnitude of 3-D accelerations Variance of 3-D accelerations So, standing, walking and cycling can be distinguished from running Movement Classification based on inertial sensor data 11

12 Example Entering a car Car is mainly ferromagnetic Characteristic pattern in signal Forward/Backward movements can be measured Characteristic patterns for entering and sitting down Vertical rotation of the persons body can be measured Characteristic patterns for opening the door and entering the car Movement Classification based on inertial sensor data 12

13 magnitude (accelerations) [m/s²] Example Misuse: Dropping a TAG Dropped TAGs can be a serious problem in certain situations Inertial sensor based drop detection enables the tag to communicate misuse Two main characteristics Zero-g during fall Impact time [s] von Jürgen Howaldt [CC-BY-SA-3.0-de], via Wikimedia Commons Movement Classification based on inertial sensor data 13

14 Results Six activities can be distinguished Using stand-alone inertial and magnetic sensors Mean classification accuracy is 91% Certain activities are hard to classify reliable (entering a car, cycling) Typical 9-DOF sensors as used in smartphones can be applied Movement Classification based on inertial sensor data 14

15 Potential Application besides the goals of Ko-TAG: Seamless Navigation 1. Directly 2. Crosswalk 3. Underground Precise localization Seamless positioning A B Safe guidance Movement Classification based on inertial sensor data 15

16 Potential Application besides the goals of Ko-TAG: Seamless Navigation The growing demand for indoor and seamless localization solutions gives the opportunity to reach a bigger user group than safety solutions and vice versa. But due to sensor errors users are facing very hard limitations regarding quality and the reliable time span. Sensor fusion for seamless navigation Movement Classification based on inertial sensor data 16

17 Thank you for your attention!