Boosting a weak learning algorithm by majority
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Smart Technology for Aging, Disability and Independence: The State of the Science
Smart Technology for Aging, Disability and Independence: The State of the Science
Naive Bayes models for probability estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
Sensing Muscle Activities with Body-Worn Sensors
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Body Sensor Networks
A survey of advances in vision-based human motion capture and analysis
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A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Motion- and location-based online human daily activity recognition
Pervasive and Mobile Computing
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Pervasive and Mobile Computing
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Journal of Data and Information Quality (JDIQ)
Activity recognition based on a multi-sensor meta-classifier
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Classification of daily life activities by decision level fusion of inertial sensor data
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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In this paper, we propose a human daily activity recognition method by fusing the data from two wearable inertial sensors attached on one foot and the waist of the subject, respectively. We developed a multi-sensor fusion scheme for activity recognition. First, data from these two sensors are fused for coarse-grained classification in order to determine the type of the activity: zero displacement activity, transitional activity, and strong displacement activity. Second, a fine-grained classification module based on heuristic discrimination or hidden Markov models (HMMs) is applied to further distinguish the activities. We conducted experiments using a prototype wearable sensor system and the obtained results prove the effectiveness and accuracy of our algorithm.