E. Pavlidou

Rolling resistance and propulsion efficiency of manual and power-assisted wheelchairs

Rolling resistance is one of the main forces resisting wheelchair propulsion and thus affecting stress exerted on the upper limbs. The present study investigates the differences in rolling resistance, propulsion efficiency and energy expenditure required by the user during power-assisted and manual propulsion. Different tire pressures (50%, 75%, 100%) and two different levels of motor assistance were tested. Drag force, energy expenditure and propulsion efficiency were measured in 10 able-bodied individuals under different experimental settings on a treadmill. Results showed that drag force levels were significantly higher in the 50%, compared to the 75% and 100% inflation conditions. In terms of wheelchair type, the manual wheelchair displayed significantly lower drag force values than the power-assisted one. The use of extra-power-assisted wheelchair appeared to be significantly superior to conventional power-assisted and manual wheelchairs concerning both propulsion efficiency and energy expenditure required by the user. Overall, the results of the study suggest that the use of power-assisted wheelchair was more efficient and required less energy input by the user, depending on the motor assistance provided.

Finding a needle by removing the haystack

We introduce a normalization algorithm which highlights short-term, localized, non-periodic fluctuations in hyper-temporal satellite data by dividing each pixel by the mean value of its spatial neighbourhood set. In this way we suppress signal patterns that are common in the central and surrounding pixels, utilizing both spatial and temporal information at different scales. We test the method on two subsets of a hyper-temporal thermal infra-red (TIR) dataset. Both subsets are acquired from the SEVIRI instrument onboard the Meteosat-9 geostationary satellite; they cover areas with different spatiotemporal TIR variability. We impose artificial fluctuations on the original data and apply a window-based technique to retrieve them from the normalized time series. We show that localized short-term fluctuations as low as 2K, which were obscured by large-scale variable patterns, can be retrieved in the normalized time series. Sensitivity of retrieval is determined by the intrinsic variability of the normalized TIR signal and by the amount of missing values in the dataset. Finally, we compare our approach with widely used techniques of statistical and spectral analysis and we discuss the improvements introduced by our method. HighlightsWe introduce a normalization approach for detection of extremes.We consider both the spatial and temporal dimensions of geophysical data.We apply the method and test its sensitivity on hyper-temporal satellite data.

Study of Volcanic Activity at Different Time Scales Using Hypertemporal Land Surface Temperature Data

We apply a method for detecting subtle spatiotemporal signal fluctuations to monitor volcanic activity. Whereas midwave infrared data are commonly used for volcanic hot spot detection, our approach utilizes hypertemporal longwave infrared-based land surface temperature (LST) data. Using LST data of the second-generation European Meteorological Satellites, we study (a) a paroxysmal, 1 day long eruption of Mount Etna (Italy); (b) a prolonged, 6 month period of effusive and lateral lava flows of the Nyamuragira volcano (Democratic Republic of Congo); and (c) intermittent activity in the permanent lava lake of Nyiragongo (Democratic Republic of Congo) over a period of 2 years (2011-2012). We compare our analysis with published ground-based observations and satellite-based alert systems; results agree on the periods of increased volcanic activity and quiescence. We further apply our analysis on mid-infrared and long-infrared brightness temperatures and compare the results. We conclude that our study enables the use of LST data for monitoring volcanic dynamics at different time scales, can complement existing methodologies, and allows for use of long time series from older sensors that do not provide midwave infrared data.