Efficient reconstruction of sequences from their subsequences or supersequences
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Designing a Home of the Future
IEEE Pervasive Computing
An Inertial Measurement Framework for Gesture Recognition and Applications
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
MIThril 2003: Applications and Architecture
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Georgia tech gesture toolkit: supporting experiments in gesture recognition
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Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Gesture spotting using wrist worn microphone and 3-axis accelerometer
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Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
An Agent-Based Framework for Healthcare Support System
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Automatic feature selection for context recognition in mobile devices
Pervasive and Mobile Computing
Toolkit to support intelligibility in context-aware applications
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Identifying the activities supported by locations with community-authored content
Proceedings of the 12th ACM international conference on Ubiquitous computing
Proceedings of the 2nd Augmented Human International Conference
Towards qualitative assessment of weight lifting exercises using body-worn sensors
Proceedings of the 13th international conference on Ubiquitous computing
A novel approach to multi-sensor data synchronization using mobile phones
Proceedings of the Fifth International Conference on Body Area Networks
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Adaptive gym exercise counting for myHealthAssistant: poster abstract
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Detecting leisure activities with dense motif discovery
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Designing to support prescribed home exercises: understanding the needs of physiotherapy patients
Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design
Exercise repetition detection for resistance training based on smartphones
Personal and Ubiquitous Computing
Qualitative activity recognition of weight lifting exercises
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NuActiv: recognizing unseen new activities using semantic attribute-based learning
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
MotionMA: motion modelling and analysis by demonstration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Towards zero-shot learning for human activity recognition using semantic attribute sequence model
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. However, mechanisms for tracking free weight exercises have not yet been explored. In this paper, we study methods that automatically recognize what type of exercise you are doing and how many repetitions you have done so far. We incorporated a three-axis accelerometer into a workout glove to track hand movements and put another accelerometer on a user's waist to track body posture. To recognize types of exercises, we tried two methods: a Naïve Bayes Classifier and Hidden Markov Models. To count repetitions developed and tested two algorithms: a peak counting algorithm and a method using the Viterbi algorithm with a Hidden Markov Model. Our experimental results showed overall recognition accuracy of around 90% over nine different exercises, and overall miscount rate of around 5%. We believe that the promising results will potentially contribute to the vision of a digital personal trainer, create a new experience for exercising, and enable physical and psychological well-being.