A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Technological opportunities for supporting people with dementia who are living at home
International Journal of Human-Computer Studies
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Recognizing daily activities with RFID-based sensors
Proceedings of the 11th international conference on Ubiquitous computing
Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Using wearable activity type detection to improve physical activity energy expenditure estimation
Proceedings of the 12th ACM international conference on Ubiquitous computing
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
Eigenspace-based fall detection and activity recognition from motion templates and machine learning
Expert Systems with Applications: An International Journal
A smartphone-based fall detection system
Pervasive and Mobile Computing
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We present a real-time fall detection and activity recognition system (FDAR) that can be easily deployed using Wii Remotes worn on human body. Features extracted from continuous accelerometer data streams are used for training pattern recognition models, then the models are used for detecting falls and recognizing 14 fine grained activities including unknown activities in realtime. An experiment on 12 subjects was conducted to rigorously evaluate the system performance. With the recognition rates as high as 91% precision and recall for 10-fold cross validation and as high as 82% precision and recall for leave one subject out evaluations, the results demonstrated that the development of real-time fall detection and activity recognition systems using low-cost sensors is feasible.