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.
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.
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:
More Information on Activity Recognition:
Publications Video Poster Data Set
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.