An HMM-Based Approach for Gesture Segmentation and Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
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International Journal of Computer Vision
Recognition of Composite Human Activities through Context-Free Grammar Based Representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
Gestures are strings: efficient online gesture spotting and classification using string matching
Proceedings of the ICST 2nd international conference on Body area networks
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ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
A new approach to enable gesture recognition in continuous data streams
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
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ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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AmI'07 Proceedings of the 2007 European conference on Ambient intelligence
Accelerometer based gesture recognition using continuous HMMs
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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ACM Transactions on Intelligent Systems and Technology (TIST)
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EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
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Pervasive and Mobile Computing
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Proceedings of the 4th Augmented Human International Conference
A survey on smartphone-based systems for opportunistic user context recognition
ACM Computing Surveys (CSUR)
A tutorial on human activity recognition using body-worn inertial sensors
ACM Computing Surveys (CSUR)
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We propose a novel model-based approach to activity recognition using high-level primitives that are derived from a human body model estimated from sensor data. Using short but fixed positions of the hands and turning points of hand movements, a continuous data stream is segmented in short segments of interest. Within these segments, joint boosting enables the automatic discovery of important and distinctive features ranging from motion over posture to location. To demonstrate the feasibility of our approach we present the user-dependent and across-user results of a study with 8 participants. The specific scenario that we study is composed of 20 activities in quality inspection of a car production process.