C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recognizing Mimicked Autistic Self-Stimulatory Behaviors Using HMMs
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Verbosity: a game for collecting common-sense facts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Pervasive Computing and Autism: Assisting Caregivers of Children with Special Needs
IEEE Pervasive Computing
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Proceedings of the 11th international conference on Ubiquitous computing
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The design of a portable kit of wireless sensors for naturalistic data collection
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Detection of motion disorders of patients with autism spectrum disorders
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
A monitoring system enhanced by means of situation-awareness for cognitive impaired people
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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Individuals with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. Automatically detecting these movements using comfortable, miniature wireless sensors could advance autism research and enable new intervention tools for the classroom that help children and their caregivers monitor, understand, and cope with this potentially problematic class of behavior. We present activity recognition results for stereotypical hand flapping and body rocking using accelerometer data collected wirelessly from six children with ASD repeatedly observed by experts in real classroom settings. An overall recognition accuracy of 88.6% (TP: 0.85; FP: 0.08) was achieved using three sensors. We also present pilot work in which non-experts use software on mobile phones to annotate stereotypical motor movements for classifier training. Preliminary results indicate that non-expert annotations for training can be as effective as expert annotations. Challenges encountered when applying machine learning to this domain, as well as implications for the development of real-time classroom interventions and research tools are discussed.