Fall detection using history triple features

Accurate identification and timely handling of involuntary events, such as falls, plays a crucial part in effective as-sistive environment systems. Fall detection, in particular, is quite critical, especially in households of lonely elderly people. However, the task of visually identifying a fall is challenging as there is a variety of daily activities that can be mistakenly characterized as falls. To tackle this issue, various feature extraction methods that aim to effectively distinguish unintentional falls from other everyday activi-ties have been proposed. In this study, we examine the capability of the History Triple Features technique based on Trace transform, to provide noise robust and invariant to different variations features for the spatiotemporal represen-tation of fall occurrences. The aim is to effectively detect falls among other household-related activities that usually take place indoors. For the evaluation of the algorithm the video sequences from two realistic fall detection datasets of different nature have been used. One is constructed using a ceiling mounted depth camera and the other is constructed using an RGB camera placed on arbitrary positions in dif-ferent rooms. After forming the feature vectors, we train a support vector machine using a radial basis function kernel. Results show a very good response of the algorithm achiev-ing 100 % on both datasets indicating the suitability of the technique to the specific task. 1

Uncrewed Aircraft Vehicle (UAV) flight oceanic measurements in NetCDF format gathered during MARIA S. MERIAN cruise MSM114/2 (ARC)

The ship campaign MSM114/2 (ARC) with the German research vessel Maria S. Merian took place from 22 January to 23 February, 2023. The campaign started in Mindelo, Cape Verde, and ended in Punta Arenas, Chile. During the campaign, the Intertropical Convergence Zone (ITCZ) was completely crossed three times at an almost constant longitude of 23˚W yielding three profiles of the ITCZ. Afterwards, a storm was passed in the Roaring Forties, in the South Atlantic. Here, we present level 2 oceanographic data from the Uncrewed Aircraft Vehicle flights as part of a series of standardised data sets of the atmospheric and oceanographic observations gathered during the ARC campaign. The data set contains eleven sea water temperature profiles down to a depth of 15 m

Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery

Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials

A computational system to optimise noise rejection in photoplethysmography signals during motion or poor perfusion states

Photoplethysmography (PPG) signals can be used in clinical assessment such as heart rate (HR) estimations and extraction of arterial flow waveforms. Motion artefact and/or poor peripheral perfusion can contaminate the PPG during monitoring. A computational system is presented here to minimise these two intrinsic weaknesses of the PPG signals. Specifically, accelerometers are used to detect the presence of motion artefacts and an adaptive filter is employed to minimise induced errors. Zero-phase digital filtering is engaged to reduce inaccuracy on the PPG signals when measured from a poorly perfused periphery. In this system, a decision matrix adopts the appropriate technique to improve the PPG signal-to-noise ratio dynamically. Statistical analyses show promising results (maximum error < 7.63%) when computed HR is compared to corresponding estimates from the electrocardiogram. Hence, the results here suggest that this dual-mode approach has potential for use in relevant clinical measurements

Motion artefact reduction of the photoplethysmographic signal in pulse transit time measurement

Motion artefact is a common occurrence that contaminates photoplethysmographic (PPG) measurements. To extract timing information from signals during artefact is challenging. PPG signal is very sensitive to artefacts and can be used in applications like, pulse transit time (PTT) as part of the polysomnographic studies. A correlation cancellation or signal processing approach is implemented with the adaptive cancelling filter concept and a triaxial accelerometry. PPG signals obtained from a Masimo (Reference) pulse oximeter is used as reference to compare with the reconstructed PPG signals. Different hands are used for each PPG source, one stationary while the other involves typical movements during sleep. A second Masimo pulse oximeter is used to register intensity of timing errors on commercial PPG signals. 108 PTT measurements are recorded in three different movements with PTT estimates from unprocessed PPG signals showing 35.51±27.42%, Masimo 50.02±29.40% and reconstructed 4.32±3.59% difference against those from the Reference PPG. The triaxial accelerometry can be used to detect the presence of artefact on PPG signals. This is useful in PTT measurements when signal contaminated with artefacts are required for further analysis, especially after and during arousals in sleep. The suggested filtering model can then reconstruct these corrupted PPG signals. Copyrigh