Background Virtual reality (VR) has several applications in the medical domain and also generates a secure environment to carry out activities. Evaluation of the effectiveness of VR among older populations revealed positive effects of VR as a tool to reduce risks of falls and also improve the social and emotional well-being of older adults. The decline in physical and mental health, the loss of functional capabilities, and a weakening of social ties represent obstacles towards active aging among older adults and indicate a need for support. Existing research focused on the effects of VR among older populations, and its uses and benefits. Our study investigated the acceptance and use of VR by the elderly. Methods This pilot study was conducted on 30 older adults who voluntarily participated during March to May 2018. Nine VR applications that promote physical activities, motivate users, and provide entertainment were chosen for this study. Participants were asked to use any one of the applications of their choice for 15 min twice a week for 6 weeks. At the end of 6 weeks, participants were asked to fill out a questionnaire based on the Technology Acceptance Model and a literature review, to evaluate their acceptance of VR technology. Cronbach’s alpha reliability analysis was used to test the internal consistency of the questionnaire items. Pearson’s product moment correlation was used to examine the validity of the questionnaire. A linear regression and mediation analysis were utilized to identify relationships among the variables of the questionnaire. Results In total, six male and 24 female participants aged 60~95 years volunteered to participate in the study. Perceived usefulness, perceived ease of use, social norms, and perceived enjoyment were seen to have had significant effects on the intention to use VR. Participants agreed to a large extent regarding the perceived usefulness, perceived enjoyment, and their experience of using VR. Thus, VR was seen to have high acceptance among this elderly population. Conclusions Older people have positive perceptions towards accepting and using VR to support active aging. They perceived VR to be useful, easy to use, and an enjoyable experience, implying positive attitudes toward adopting this new technology. Electronic supplementary material The online version of this article (10.1186/s12877-019-1218-8) contains supplementary material, which is available to authorized users.

Highly regio-regular poly(3-hexylthiophenes) (P3HT) thin films have been produced using the Tzrnadel method. They have head to tail counts approaching very close to 100% and high molecular mass. Thin-film transistors and Schottky diodes have been used to study the effects of counter ion and carrier density on field-effect and bulk mobility, respectively. The density of counter ions was increased using 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) and had a profound effect on both the field effect and bulk mobility with a ratio between the two of 102, in a similar way to DDQ in poly(β′-dodecyloxy-α,α′,-α′,α″terthienyl) (polyDOT3), but with substantially higher drift mobilities. A method is described, using Schottky barriers, of simply and accurately determining carrier drift mobility. The effect of carrier density on hole mobility is believed to be indirect, filling traps in the regions separating the highly ordered domains until trap free conduction and hence high mobility is reached.

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In advanced driver assistance systems to conditional automation systems, monitoring of driver state is vital for predicting the driver's capacity to supervise or maneuver the vehicle in cases of unexpected road events and to facilitate better in-car services. The paper presents a technique that exploits millimeter-wave Doppler radar for 3D head tracking. Identifying the bistatic and monostatic geometry for antennas to detect rotational vs. translational movements, the authors propose the biscattering angle for computing a distinctive feature set to isolate dynamic movements via class memberships. Through data reduction and joint time-frequency analysis, movement boundaries are marked for creation of a simplified, uncorrelated, and highly separable feature set. The authors report movement-prediction accuracy of 92%. This non-invasive and simplified head tracking has the potential to enhance monitoring of driver state in autonomous vehicles and aid intelligent car assistants in guaranteeing seamless and safe journeys.

We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2D/3D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation (ontology models). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smartspace environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards. Index Terms-Passive radio sensing, cloud-assisted Internet of Things, real-time data analysis, semantic data models.

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