Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Robots are moving from well-controlled lab environments to the real world, where an increasing number of environments has been transformed into smart sensorized IoT spaces. Users will expect these robots to adapt to their preferences and needs, and even more so for social robots that engage in personal interactions. In this paper, we present declarative ontological models and a middleware platform for building services that generate interaction tasks for social robots in smart IoT environments. The platform implements a modular, data-driven workflow that allows developers of interaction services to determine the appropriate time, content and style of human-robot interaction tasks by reasoning on semantically enriched loT sensor data. The platform also abstracts the complexities of scheduling, planning and execution of these tasks, and can automatically adjust parameters to the personal profile and current context. We present motivational scenarios in three environments: a smart home, a smart office and a smart nursing home, detail the interfaces and executional paths in our platform and present a proof-of-concept implementation. (C) 2018 Elsevier Inc. All rights reserved

Pro-active positioning of a social robot intervening upon behavioral disturbances of persons with dementia in a smart nursing home

Behavioral disturbances of persons with dementia residing in a nursing home impose a significant burden on other residents and on the care staff. A social robot can provide an adequate technological support tool for the caregivers by approaching a resident that exhibits a behavioral disturbance. In this paper, we focus on how to position the robot in the nursing home, taking into account the profile and location of the residents. We minimize the time between the detection of a behavioral disturbance and the robot having arrived near the resident and starting an interaction scenario. Our algorithm is evaluated using realistic data that was collected during 3 months in two Belgian nursing homes. (C) 2019 Elsevier B.V. All rights reserved

Validation of methane and carbon monoxide from Sentinel-5 Precursor using TCCON and NDACC-IRWG stations

The Sentinel-5 Precursor (S5P) mission with the TROPOspheric Monitoring Instrument (TROPOMI) on board has been measuring solar radiation backscattered by the Earth\u27s atmosphere and surface since its launch on 13 October 2017. In this paper, we present for the first time the S5P operational methane (CH4) and carbon monoxide (CO) products\u27 validation results covering a period of about 3 years using global Total Carbon Column Observing Network (TCCON) and Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) network data, accounting for a priori alignment and smoothing uncertainties in the validation, and testing the sensitivity of validation results towards the application of advanced co-location criteria. We found that the S5P standard and bias-corrected CH4 data over land surface for the recommended quality filtering fulfil the mission requirements. The systematic difference of the bias-corrected total column-averaged dry air mole fraction of methane (XCH4) data with respect to TCCON data is −0.26±0.56 % in comparison to −0.68±0.74 % for the standard XCH4 data, with a correlation of 0.6 for most stations. The bias shows a seasonal dependence. We found that the S5P CO data over all surfaces for the recommended quality filtering generally fulfil the missions requirements, with a few exceptions, which are mostly due to co-location mismatches and limited availability of data. The systematic difference between the S5P total column-averaged dry air mole fraction of carbon monoxide (XCO) and the TCCON data is on average 9.22±3.45 % (standard TCCON XCO) and 2.45±3.38 % (unscaled TCCON XCO). We found that the systematic difference between the S5P CO column and NDACC CO column (excluding two outlier stations) is on average 6.5±3.54 %. We found a correlation of above 0.9 for most TCCON and NDACC stations. The study shows the high quality of S5P CH4 and CO data by validating the products against reference global TCCON and NDACC stations covering a wide range of latitudinal bands, atmospheric conditions and surface conditions

Pro-active positioning of a social robot intervening upon behavioral disturbances of persons with dementia in a smart nursing home

Behavioral disturbances of persons with dementia residing in a nursing home impose a significant burden on other residents and on the care staff. A social robot can provide an adequate technological support tool for the caregivers by approaching a resident that exhibits a behavioral disturbance. In this paper, we focus on how to position the robot in the nursing home, taking into account the profile and location of the residents. We minimize the time between the detection of a behavioral disturbance and the robot having arrived near the resident and starting an interaction scenario. Our algorithm is evaluated using realistic data that was collected during 3 months in two Belgian nursing homes

Comparison of surface and column carbon monoxide at a high altitude, a megacity and a southern hemisphere site

info:eu-repo/semantics/nonPublishe