An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments

Recently, different solutions leveraging Internet of Things (IoT) technologies have been adopted to avoid accidents in agricultural working environments. As an example, heavy vehicles, e.g., tractors or excavators, have been upgraded with remote controls. Nonetheless, the community continues to encourage discussions on safety issues. In this framework, a localization system installed on remote-controlled farm machines (RCFM) can help in preventing fatal accidents and reduce collision risks. This paper presents an innovative system that exploits passive UHF-RFID technology supported by commercial BLE Beacons for monitoring and preventing accidents that may occur when ground-workers in RCFM collaborate in outdoor agricultural working areas. To this aim, a modular architecture is proposed to locate workers, obstacles and machines and guarantees the security of RCFM movements by using specific notifications for ground-workers prompt interventions. Its main characteristics are presented with its main positioning features based on passive UHF-RFID technology. An experimental campaign discusses its performance and determines the best configuration of the UHF-RFID tags installed on workers and obstacles. Finally, system validation demonstrates the reliability of the main components and the usefulness of the proposed architecture for worker safety

Ventilation and outcomes following robotic-assisted abdominal surgery: an international, multicentre observational study

Background: International data on the epidemiology, ventilation practice, and outcomes in patients undergoing abdominal robotic-assisted surgery (RAS) are lacking. The aim of the study was to assess the incidence of postoperative pulmonary complications (PPCs), and to describe ventilator management after abdominal RAS. Methods: This was an international, multicentre, prospective study in 34 centres in nine countries. Patients ≥18 yr of age undergoing abdominal RAS were enrolled between April 2017 and March 2019. The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score was used to stratify for higher risk of PPCs (≥26). The primary outcome was the incidence of PPCs. Secondary endpoints included the preoperative risk for PPCs and ventilator management. Results: Of 1167 subjects screened, 905 abdominal RAS patients were included. Overall, 590 (65.2%) patients were at increased risk for PPCs. Meanwhile, 172 (19%) patients sustained PPCs, which occurred more frequently in 132 (22.4%) patients at increased risk, compared with 40 (12.7%) patients at lower risk of PPCs (absolute risk difference: 12.2% [95% confidence intervals (CI), 6.8–17.6%]; P<0.001). Plateau and driving pressures were higher in patients at increased risk, compared with patients at low risk of PPCs, but no ventilatory variables were independently associated with increased occurrence of PPCs. Development of PPCs was associated with a longer hospital stay. Conclusions: One in five patients developed one or more PPCs (chiefly unplanned oxygen requirement), which was associated with a longer hospital stay. No ventilatory variables were independently associated with PPCs. Clinical trial registration: NCT02989415

Experimental Assessment of Passive UHF-RFID Sensor Tags for Environment and Kinematic Data

Radio Frequency IDentification (RFID) technology and low power electronic sensors may be combined to build wireless passive sensors which might cover many applications ranging from Internet of Things to Healthcare. This work presents a multi-perspective overview of RFID-based sensor tags and then proceed to experimentally characterize the sensing capabilities of two commercial fully-passive RFID sensor tags for acceleration and relative humidity data acquisition, respectively. Measurements are compared with data acquired through commercial traditional non-RFID wired sensor

Robot Localisation using UHF-RFID Tags for Industrial IoT Applications

Autonomous systems are becoming more and more affordable and effective in dynamic industrial environments. Effective localisation algorithm are usually considered as enablers to increase the efficiency and the flexibility of industrial warehouses and production plants using automation. The paper presents a solution to both localise a mobile agent and reconstruct its entire trajectory through sensor fusion and using UHF-RFID passive tags. Solutions on dummy trajectories are reported to show the effectiveness of the proposed method

Robot localisation using UHF-RFID tags: A kalman smoother approach

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively

Bioclimate map of Sardinia (Italy)

Bioclimatology deals with the interrelation between climate and living organisms, in particular, plants and plant communities, considering the main climate variables that are relevant for species distribution. In this context spatial interpolation of monthly temperature and precipitation data using 203 rain gauges and 68 temperature gauges for Sardinia (Italy) was undertaken. As interpolation technique, we used regression kriging which combines multiple linear regression (MLR) with ordinary kriging of the residuals. MLR procedures include as independent variables: altitude, latitude, longitude, coast distance and a topographic factor of relative elevation. Elevation data were obtained from digital elevation model at 40 m resolution. Following the approach of the Worldwide Bioclimatic Classification System, a bioclimatic diagnosis of the entire territory was derived using map algebra calculations of the bioclimatic indices proposed by Rivas-Mart ́ınez et al. [(2011). Worldwide Bioclimatic classification system. Global Geobotany, 1, 1–638]. Two macrobioclimates (Mediterranean pluviseasonal oceanic and Temperate oceanic), one macrobioclimatic variant (Submediterranean), and four classes of continentality (from weak semihyperoceanic to weak semicontinental), eight thermotypic horizons (from lower thermomediterranean to upper supratemperate) and seven ombrotypic horizons (from lower dry to lower hyperhumid) were identified, resulting in a combination of 43 isobioclimates. The resulting map represents a useful environmental stratum, for regional planning, ecological modeling and biodiversity conservation

The MONITOR project: RFID-based robots enabling real-time inventory and localization in warehouses and retail areas

Inventory is one of the most problematic tasks in warehouses and retail areas. It is a challenging activity which still suffers from item losses, by producing waste in terms of time, materials, and profits. To solve this big problem, the Radio Frequency Identification (RFID) technology can be profitably employed, with great advantage if combined with robots by allowing the implementation of an automatic inventory system, without the need of operator actions. Besides, a localization system can be designed by exploiting a synthetic-array approach, with no additional cost, as proposed within the MONITOR Project here presented. In particular, this paper describes the main design criteria to implement an autonomous inventory and localization system through a UHF-RFID Robot

UHF-RFID AoA Positioning with Multiple Arrays on Agricultural Machinery

Worker safety in agricultural and forestry sites where Remotely Controlled Farm Machinery (RCFM) operate is a serious problem which requires the development of innovative smart systems. Radiolocalization of workers developing their tasks in the machinery vicinity turns out to be an effective method to prevent fatal collisions. In this context, passive RFID technology at the UHF band is a favorable solution due to the simplicity of installation. This paper proposes the use of two commercial RFID arrays to triangulate the location of workers and other obstacles in the surroundings of a RCFM. optimization and performance of the system geometry are investigated by numerical analysis as the most significant parameters change, by obtaining sizing criteria useful for on-site deployment of the system

An RFID Tracking System for Agricultural Safety

This paper proposes a tracking system of workers operating in proximity of remote-control farm machineries (RCFMs) in agricultural and forestry working areas by aiming to enhance the operator safety. It consists of a passive Ultra-High-Frequency (UHF) Radio Frequency Identification (RFID) system where the RFID reader connected to an antenna array is installed on-board of RCFMs, whereas each worker is equipped with a pair of RFID tags placed at different heights. The onboard hardware is exploited to both measure the worker distance and the Direction of Arrival (DoA). The worker trajectory is then estimated with an Unscented Kalman Filter (UKF) and an alarm is raised if the operator distance from the RCFM is is below an assigned threshold, thus stopping the dangerous manoeuvre. The work is part of the SMARTGRID Project co-funded by the Italian National Institute for Insurance against Accidents at Work (INAIL)