Detection and Recognition of Periodic, Nonrigid Motion
International Journal of Computer Vision
View-Based Interpretation of Real-Time Optical Flow for Gesture Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Recognizing User Context via Wearable Sensors
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Repetitive Motion Analysis: Segmentation and Event Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Articulated Motion Modeling for Activity Analysis
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Human activity recognition with action primitives
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Social life networks: a multimedia problem?
Proceedings of the 21st ACM international conference on Multimedia
Building health persona from personal data streams
Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia
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In this article, we present a wearable intelligence device for activity monitoring applications. We developed and evaluated algorithms to recognize physical activities from data acquired using a 3-axis accelerometer with a single camera worn on a body. The recognition process is performed in two steps: at first the features for defining a human activity are measured by the 3-axis accelerometer sensor and the image sensor embedded in a wearable device. Then, the physical activity corresponding to the measured features is determined by applying the SVM classifier. The 3-axis accelerometer sensor computes the correlation between axes and the magnitude of the FFT for other features of an activity. Acceleration data is classified into nine activity labels. Through the image sensor, multiple optical flow vectors computed on each grid image patch are extracted as features for defining an activity. In the experiments, we showed that an overall accuracy rate of activity recognition based our method was 92.78%.