Evaluation of Dust Deposition on Parabolic Trough Collectors in the Visible and Infrared Spectrum.

Solar energy is mostly harnessed in arid areas where a high concentration of atmospheric dust represents a major environmental degradation factor. Gravitationally settled particles and other solid particles on the surface of the photovoltaic panels or thermal collectors greatly reduce the absorbed solar energy. Therefore, frequent cleaning schedules are required, consuming high quantities of water in regions where water precipitation is rare. The efficiency of this cleaning maintenance is greatly improved when methods to estimate the degree of cleanness are introduced. This work focuses on the need for better detecting the degradation created by dust deposition, considering experimental data based on different air pollutants, and analyzing the resulting thermal and visible signatures under different operating environments. Experiments are performed using six different types of pollutants applied to the surface of parabolic trough collectors while varying the pollutant density. The resulting reflectivity in the visible and infrared spectrum is calculated and compared. Results indicate that the pollutants can be distinguished, although the reflectivity greatly depends on the combination of the particle size of the pollutant and the applied amount, with greater impact from pollutants with small particles.

Driver Distraction Using Visual-Based Sensors and Algorithms.

Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

The ELF@Home project: Elderly sELF-care based on sELF-check of health conditions and sELF-fitness at home.

The ELF@Home project is a research and innovation project running from June 1st 2013 to May 31st 2016 and co-funded by the Ambient Assisted Living Joint Programme (AAL JP) and National Authorities in Spain, Sweden and Germany. The ELF@Home project relies on the use of the proven advantages of elderly fitness to develop a self-care solution based on self-check of health conditions and self-fitness at home. The project uses information and communication technologies (ICT) to build an autonomous fitness system targeting healthy or pre-frail elderly people aged over 65 and living independently at home.

Unsupervised abnormal human behaviour detection using acceleration data.

Abnormal human behavior detection under free-living conditions is a reliable technique to detect activity disorders and diseases. This work proposes an acceleration-based algorithm to detect abnormal behavior as an abnormal increase or decrease in physical activity (PA). The algorithm is based on statistical features of human physical activity. Using a period of observed physical activity as a reference, the algorithm is able to detect abnormal behavior in other periods of time. The approach is unsupervised as the modeling of the reference behavior is not required. It has been validated with a group of 12 users under free-living conditions for two days. Results show a precision greater than 75% and a recall of 92%.

JIM: a novel and efficient accelerometric magnitude to measure physical activity.

There are various techniques available to measure human physical activity (PA). Accelerometer based techniques claim to be non-invasive and easy to use. The signal magnitude area (SMA) is the most extended feature used to measure the physical activity. It is calculated by sampling and filtering an accelerometer signal of at least at 50 Hz. SMA has a proven and widely accepted linear relation with the energy expenditure. A novel magnitude called JIM, which is more efficient than SMA, is proposed in this paper. The jerk-based inactivity magnitude (JIM) is also calculated from the acceleration signal, but at a sampling rate of 1Hz, increasing the battery life of the measuring system. This magnitude gives the same information as the SMA (correlation of 95%) and is validated with a group of 39 users in free-living conditions for at least 24 hours.