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Dementia research has frequently documented high rates of caregiver depression and distress in spouses providing care for a partner suffering from dementia. However, the role of marital communication in understanding caregiver distress has not been examined sufficiently. Studies with healthy couples demonstrated an association between marital communication and the partners' psychological well-being, depressiveness, respectively (e.g., Heene, Buysee, & Van Oost, 2005). The current study investigates the relationship between caregiver depression and communication in 37 couples in which the wives care for their partners with dementia. Nonsequential and sequential analyses revealed significant correlations between caregiver depression and marital communication quality. Caregivers whose husbands used more positive communication reported less depression and distress. Additionally, caregiver depression was negatively correlated with rates of positive reciprocal communication indicating dependence between the couples' interaction patterns. This study is one of the first to illustrate the relevance of spousal communication in understanding caregiver distress and depression.

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Virtual training systems deliver training within a virtual environment (VE) using virtual reality (VR) or augmented reality (AR) technologies. However, to be fully accepted as a valid tool for training within the automotive industry, evidence is required on the ability of these systems to deliver effective and efficient training to the relevant users. This paper aims to investigate the effectiveness and efficiency of the first prototype of the virtual training system (VTS) developed within the VISTRA (Virtual Simulation and Training of Assembly and Service Processes in Digital Factories) project (FP7‐ICT‐285176), using real end users from the OPEL automotive plant in Rüsselsheim, Germany. Two separate and independent studies were employed that used objective and subjective methods of investigation to establish performance and usability measures. The objective results show that virtual training was effective in reducing error during task performance when compared to traditional training. The subjective results concluded that the opinions of the participants were mainly positive concerning the overall use of the VTS for assembly operation training; however, a number of issues were highlighted and reported to the developers for further advancement of the system.

Today, the workflows that are involved in industrial assembly and production activities are becoming increasingly complex. To efficiently and safely perform these workflows is demanding on the workers, in particular when it comes to infrequent or repetitive tasks. This burden on the workers can be eased by introducing smart assistance systems. This article presents a scalable concept and an integrated system demonstrator designed for this purpose. The basic idea is to learn workflows from observing multiple expert operators and then transfer the learnt workflow models to novice users. Being entirely learning-based, the proposed system can be applied to various tasks and domains. The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind. The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user’s pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD). A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed. The feasibility of the chosen approach for the complete action-perception-feedback loop is demonstrated on three increasingly complex datasets representing manual industrial tasks. These limited size datasets indicate and highlight the potential of the chosen technology as a combined entity as well as point out limitations of the system.