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Human Activity Recognition with Inertial Sensors

General information about Human Motion Related Activity Recognition

Knowledge about the current motion related activity (e.g. Sitting, Standing, Walking, Running, Jumping, Falling and Lying) of a person is information that is required or useful for a number of applications. Technical advances in recent years have reduced prices for sensors capable of providing the necessary motion and pose related signals, in particular MEMS based inertial measurement units (IMUs). In addition to low cost sensors, unobtrusiveness is a requirement for an activity recognition system: We achieve this by mounting one IMU on the belt of the user. Our signal processing approach is a multi-tier one. We first compute features from the raw accelerations and turn rates and use these for classification with Bayesian Network techniques trained from a semi naturalistic, labelled data set.

Results

These results stem from a four-fold cross-validation of classification with a grid based filter, using a BN with learnt parameters and structure and a manually defined hidden Markov model. Features are computed at 4 Hz, with sliding windows and a recognition delay of 0.5 s taken into account. The data were recorded under semi-naturalistic conditions.

Implementation

The setup installed at DLR (as shown in the Video below) is composed of a wired sensor and some computing unit, computing the Bayesian inference. The single components and versions are described shortly in the following:

• Sensor:
  The wired IMU used was the xsens MTx IMU.
  Initially, a C-written driver was used to feed the data to a socket, the Java implemented listened to.
  The current version has implemented the same driver library in Java.
• Java OSGI Implementation:
  This implementation was realised during the EU ICT-Persist project.
• Android Implementation:
  The next step has been the portation to smaller devices, like the Android Galaxy S2.
  The adaptation of the Java code above is straightforward.

More Information on Activity Recognition:

Publications                   Video                     Poster               Data Set

Links

• Stephen Intille from MIT helped us with an accelerometer data set for activity recognition.
• XSens MTx: the Inertial Measurement Unit (IMU) used for our project

Acknowledgments

• The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement no. 215098 of the Persist (PERsonal Self-Improving SmarT spaces) Collaborative Project and from the Irish HEA through the PRTLI cycle 4 project ”Serving Society: Management of Future Communication Networks and Services”.
  We also want to thank all persons helping to record the data set.