3-D Audio in Mobile Communication Devices: Effects of Self-Created and External Sounds on Presence in Auditory Virtual Environments

This article describes a series of experiments which were carried out to measure the sense of presence in auditory virtual environments. Within the study a comparison of self-created signals to signals created by the surrounding environment is drawn. Furthermore, it is investigated if the room characteristics of the simulated environment have consequences on the perception of presence during vocalization or when listening to speech. Finally the experiments give information about the influence of background signals on the sense of presence. In the experiments subjects rated the degree of perceived presence in an auditory virtual environment on a perceptual scale. It is described which parameters have the most influence on the perception of presence and which ones are of minor influence. The results show that on the one hand an external speaker has more influence on the sense of presence than an adequate presentation of one’s own voice. On the other hand both room reflections and adequately presented background signals significantly increase the perceived presence in the virtual environment.

Learning Curves of Temporary Workers in Manual Order Picking Activities

Person-to-stock order picking is highly flexible and requires minimal investment costs in comparison to automated picking solutions. For these reasons, tradi-tional picking is widespread in distribution and production logistics. Due to its typically large proportion of manual activities, picking causes the highest operative personnel costs of all intralogistics process. The required personnel capacity in picking varies short- and mid-term due to capacity requirement fluctuations. These dynamics are often balanced by employing minimal permanent staff and using seasonal help when needed. The resulting high personnel fluctuation necessitates the frequent training of new pickers, which, in combination with in-creasingly complex work contents, highlights the im-portance of learning processes in picking. In industrial settings, learning is often quantified based on diminishing processing time and cost requirements with increasing experience. The best-known industrial learning curve models include those from Wright, de Jong, Baloff and Crossman, which are typically applied to the learning effects of an entire work crew rather than of individuals. These models have been validated in largely static work environments with homogeneous work contents. Little is known of learning effects in picking systems. Here, work contents are heterogeneous and individual work strategies vary among employees. A mix of temporary and steady employees with varying degrees of experience necessitates the observation of individual learning curves. In this paper, the individual picking performance development of temporary employees is analyzed and compared to that of steady employees in the same working environment.