@conference {
	title = {Learning Hand Movements from Markerless Demonstrations for Humanoid Tasks},
	author = {Mao, Ren., Yang, Yezhou., Fermuller, Cornelia., Aloimones, Yiannis., Baras, John S.},
	year = {2014},
	month = {11/2014},
	abstract = {We present a framework for generating trajectories of the hand movement during manipulation actions from demonstrations so the robot can perform similar actions in new situations. Our contribution is threefold: 1) we extract and transform hand movement trajectories using a state-of-the-art markerles full hand model tracker from Kinect sensor data; 2)we develop a new bio-inspired trajectory segmentation method that automatically segments complex movements into action units, and 3) we develop a generative method to learn task specific control using Dynamic Movement Primitives (DMPs).Experiments conducted both on synthetic data and real data using the Baxter research robot platform validate our approach.},
	address = {Madrid, Spain},
	booktitle = {2014 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2014)},
	pages = {pp. 938-943}
}
@conference {
	title = {Maximizing Output Power of a CFPG Micro Energy-Harvester for Wearable Medical Sensors},
	author = {Dadfarnia, Mehdi., Sayrafian, Kamran., Mitcheson, Paul., Baras, John S.},
	year = {2014},
	month = {11/2014},
	abstract = {Energy Harvesting refers to the process ofcapturing and storing energy from the ambientenvironment. Kinetic energy harvested from the human bodymotion seems to be one of the most convenient and attractivesolutions for wearable wireless sensors in healthcareapplications. Due to their small size, such sensors are oftenpowered by small batteries which might necessitate frequentrecharge or even sensor replacement. Energy harvesting canprolong the battery lifetime of these sensors. This could directlyimpact their everyday use and significantly help theircommercial applications such as remote monitoring. In thispaper, our aim is to develop a Simulink model of the CFPGdevice that can be used to study temporal behavior of thegenerated power. Having such a dynamic model, not only helpsto have a more accurate estimation of the amount of powergenerated from various human movements, but also allows us tofurther optimize the design parameters of the micro-harvester(e.g. size/dimension, electrostatic holding force, etc.) with thecharacteristics of the input acceleration (i.e. human activity).},
	address = {Athens, Greece},
	booktitle = {4th International Conference on Wireless Mobile Communication and Healthcare},
	pages = {pp. 218-221}
}
