Virtual reality (VR) is a technology that allows users to experience multisensory and interactive environments that simulate real or imaginary scenarios. The effect of different VR immersive technology on mental workload (MWL), i.e., the amount of resources required to perform a task, is still debated; however the potential role of EEG in this context was never exploited. This paper aims to investigate the effects on MWL of performing a cognitive task in a VR environment in two conditions characterized by different degrees of immersion using a multimodal approach which combines well-assessed subjective evaluations of MWL with physiological EEG measures. A cognitive task based on the n-back test was proposed to compare the performance and MWL of participants who used either a head-mounted display (HMD) or a desktop computer to present the stimuli. The task had four different complexity levels (n = 1 or 2 with either visual or visual and audio stimuli). Twenty-seven healthy participants were enrolled in this study and performed the tasks in both conditions. EEG data and NASA Task Load indeX (NASA-TLX) were used to assess changes in objective and subjective MWL, respectively. Error rates (ERs) and reaction times (RTs) were also collected for each condition and task level. Task levels had significant effects on MWL, increasing subjective measures and decreasing performance, in both conditions. EEG MWL index have shown a significant increase especially if compared to rest. Different degrees of immersion did not show significant differences neither in individual’s performance nor in MWL as estimated by subjective ratings. However, HMD reduced the EEG-derived MWL in most conditions indicating a lower cognitive load. In conclusion, HMD may reduce the cognitive load of some tasks. The reduced level of MWL, as depicted by the EEG MWL index, may have implications for the design and future evaluation of VR-based applications.

Research in the fields of the Smart Home and Ambient Assisted Living has increased in the last decade. While some solutions are available to general public, there are still some concerns related to the design of smart solutions and their acceptance, especially when it comes to residents' monitoring and privacy. In this context, this paper introduces an ontology-based smart home framework, LIDoMS, aimed at focusing on inhabitants needs by providing a representation of their health conditions, while offering them customized services. End-users can interact with the system thanks to an adaptive and ubiquitous graphical interface. In this work, the ontological framework underlying LIDoMS is described; the system exploits the knowledge regarding the status of appliances and residents' location within the smart home to infer the activity they are involved in, and to provide customized adaptation of indoor comfort metrics, thus enabling a less invasive monitoring of the inhabitants. Two scenarios describe how different residents can interact with LIDoMS to personalize comfort metrics - pivotal for their health condition. Finally, a framework for the validation of LIDoMS is presented.

To allow wheelchair (electronic or manual) users to practice driving in different safe, repeatable and controlled scenarios, the use of simulator as a training tool is considered here. In this context, the capabilities of providing high fidelity motions for users of the simulator is highlighted as one of the most important aspects for the effectiveness of the tool. For this purpose, the motion cueing algorithm (MCA) is studied in our work to regenerate wheelchair motion cues by transforming motions of the real or simulated wheelchair into the simulator motion. The studied algorithm is developed based on Model Predictive Control (MPC) approach to efficiently optimize the motions of the platform. The overall problem is formulated using mixed-integer quadratic programming (MIQP) which involves not only the vestibular model, strict constraints of the platform but also the perception threshold in the optimization cost function. In the end, the performance assessment of the system using different control techniques is analyzed, showing the effectiveness of the proposed approach in the simulation environment.