@article{Kulkarni:2009:Biomed-Eng-Online:19656402,
  abstract = {Facial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks.Several facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing.The system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases), from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.},
  author = {Kulkarni, S S and Reddy, N P and Hariharan, S I},
  doi = {10.1186/1475-925X-8-16},
  interhash = {9bcd872ea86213a2f7d3271b0e6eb7d1},
  intrahash = {14c48c03f40a1c8bdc22314fcdf292bf},
  journal = {Biomed Eng Online},
  pages = {16-16},
  pmid = {19656402},
  title = {Facial expression (mood) recognition from facial images using committee neural networks},
  url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731770/},
  volume = 8,
  year = 2009
}

@inproceedings{Keally:2011:PTP:2070942.2070968,
  abstract = {The vast array of small wireless sensors is a boon to body sensor network applications, especially in the context awareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendly hardware and software, accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone. We provide an accurate and efficient classification approach through the use of ensemble learning. We explore the properties of different sensors and sensor data to further improve classification efficiency and reduce reliance on user annotated ground truth. We evaluate our PBN system with multiple subjects over a two week period and demonstrate that the system is easy to use, accurate, and appropriate for mobile devices.},
  acmid = {2070968},
  address = {New York, NY, USA},
  author = {Keally, Matthew and Zhou, Gang and Xing, Guoliang and Wu, Jianxin and Pyles, Andrew},
  booktitle = {Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems},
  doi = {10.1145/2070942.2070968},
  interhash = {5e6a13d34026f65338cfa619054822c8},
  intrahash = {61e5e4559d031c4152b3f316c0aa5209},
  isbn = {978-1-4503-0718-5},
  location = {Seattle, Washington},
  numpages = {14},
  pages = {246--259},
  publisher = {ACM},
  series = {SenSys '11},
  title = {PBN: towards practical activity recognition using smartphone-based body sensor networks},
  url = {http://doi.acm.org/10.1145/2070942.2070968},
  year = 2011
}