Recognizing emotional states is becoming a majorpart of a user's context for wearable computing applications. The system should be able to acquire a user's emotional states by using physiological sensors. We want to develop a personal emotional states recognition system that is practical, reliable, and can be used for health-care related applications. We propose to use the eHealth platform [1] which is a ready-made, light weight, small and easy to use device for recognizing a few emotional states like 'Sad', Dislike', 'Joy', 'Stress', 'Normal', 'No-Idea', 'Positive' and Negative' using decision tree (J48)and IBK classifiers. In this paper, we present an approach tobuild a system that exhibits this property and provides evidence based on data for 8 emotional states collected from 24 different subjects. Our results indicate that the system has an accuracy rate of approximately 97%.In our work, we used three physiological sensors (i.e. 'BloodVolume Pulse', 'Galvanic Skin Response' and 'SkinTemperature') in order to recognize emotional states (i.e.stress, joy/happy, sad, normal/neutral, dislike, no-idea, positive and negative).

Evaluations of task guidance systems often focus on evaluations of new technologies rather than comparing the nuances of interaction across the various systems. One common domain for task guidance systems is warehouse order picking. We present a method involving an easily reproducible ecologically motivated order picking environment for quantitative user studies designed to reveal differences in interactions. Using this environment, we perform a 12 participant within-subjects experiment demonstrating the advantages of a head-mounted display based picking chart over a traditional text-based pick list, a paper-based graphical pick chart, and a mobile pick-by-voice system. The test environment proved sufficiently sensitive, showing statistically significant results along several metrics with the head-mounted display system performing the best. We also provide a detailed analysis of the strategies adopted by our participants.

We present the results of an empirical study investigating the effect of visual feedback and body postures on gesture interaction techniques in a dual task setup found, for example, in wearable computing. The conducted experiment uses a novel apparatus called "Hot Wire" that allows retaining the properties of wearable computing even in laboratory environments. Visual feedback was found to impair user performance and caused users to be caught in an attention demanding closed feedback loop once presented in a head-mounted display. Even though continuous feedback was not necessary for gesture interaction, users were unable to ignore it and remain focused on the primary task. The design of an alternative gesture recognition method using a body-centric frame of reference instead of a conventional static one to improve usability, is shown to have an opposed impact both on the performance and subjective perception of users. The presence of novel devices in gesture interaction, such as data gloves, is found to be a major source of erroneous gesture recognition due to unpredictable user behavior. Our detailed result discussion provides guidelines for designing better gesture interaction.

Wearable computing means a paradigm shift: instead of working at the computer users are supported by computing systems in their primary tasks. Currently wearable computing is still a technology of niches and in a laboratory stage. However, with [email protected] a project dedicated to applications was launched by the European Commission (EC IP 004216). The first 30 months of this project are over and industrial demonstrators, evaluations and results are available. In this paper we present results from industrial case studies in two of the four application domains, namely production and maintenance, with a newly designed wearable user interface.