This paper reports the experimental studies we have performed to evaluate Explore!, an m-learning system that supports middle school students during a visit to an archaeological park. It exploits a learning technique called excursion-game, whose aim is to help students to acquire historical notions while playing and to make archaeological visits more effective and exciting. In order to understand the potentials and limitations of Explore!, our studies compare the experience of playing the excursion-game with and without technological support. The design and evaluation of Explore! have provided knowledge on the advantages and pitfalls of m-learning that may be instrumental in informing the current debate on e-learning.

Within a smart home environment the information processing is supposed to be thoroughly integrated into everyday objects. This introduces the need to keep track of the everyday objects and their state changes produced based on the userpsilas interaction with them. Such information is useful in recognizing the userpsilas activities, situations, etc. In this paper we present a ZigBee communication protocol based wireless sensor networking of 42 everyday objects (embedded with 81 simple state change sensors of 8 sensor types) in a living laboratory smart home environment. The system was evaluated in a realistic setup with background noise. The sensing module has shown promising results with an overall system precision of 91.2% and a recall of 98.8% in keeping track of the state changes to everyday objects. The signal strength measure above the acceptable limit of >10 dB to obtain reliable data communication was found to be 97.5% checked at 8 different locations in a home environment. Finally the transmission-reception range was evaluated to be 33 m with a single wall obstruction and 19 m with multiple wall obstruction in an indoor environment.

A novel method for video-based head gesture recognition using eye information by an eye tracker has been proposed. The method uses a combination of gaze and eye movement to infer head gestures. Compared to other gesture-based methods a major advantage of the method is that the user keeps the gaze on the interaction object while interacting. This method has been implemented on a head-mounted eye tracker for detecting a set of predefined head gestures. The accuracy of the gesture classifier is evaluated and verified for gaze-based interaction in applications intended for both large public displays and small mobile phone screens. The user study shows that the method detects a set of defined gestures reliably.

While the new generation of eyewear computers have increased expectations of a wearable computer, providing input to these devices is still challenging. Hand-held devices, voice commands, and hand gestures have already been explored to provide input to the wearable devices. In this paper, we examined using head and eye movements to point on a graphical user interface of a wearable computer. The performance of users in head and eye pointing has been compared with mouse pointing as a baseline method. The result of our experiment showed that the eye pointing is significantly faster than head or mouse pointing, however, our participants thought that the head pointing is more accurate and convenient.