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Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

Geographical information is gaining new momentum as an analysis and visualization tool for collections of cultural objects. It provides all kinds of users with new opportunities to contextualize and understand these objects in ways that resemble our ordinary spatially-located experience and to do so better than textual narratives. The SeMap project has built an online resource that shows more than 200,000 cultural objects through spatiotemporal maps, thus enabling new experiences and perspectives around these objects. Data come from the CER.ES repository and were created by a network of more than 100 Spanish museums. This article explains the refinement of the data provided by the repository, mostly by adding a semantic structure thanks to the CIDOC-CRM ontology, and by simplifying the exceedingly complex terminologies employed in the original records. Particular attention is paid to the methods for geolocating the information, as well as adding temporal filters (among others) to user queries. The functionalities, interface, and technical requirements are also explored at length. [ABSTRACT FROM AUTHOR]

Traffic incidents endanger the smooth running of vehicles. Congestion caused by traffic incidents has caused a waste of time and fuel and seriously affected transportation efficiency. At present, most methods use manual judgment or image features to detect traffic incidents, but these methods lack timeliness, leading to secondary incidents. For dangerous road sections such as ramp-free and long downhills, this paper proposes an algorithm to quickly detect traffic incidents based on a spatiotemporal map of vehicle trajectories. First, a vehicle dataset from the monitoring perspective is constructed, and an improved YOLOv4 detection algorithm is used to detect images organized as batches. Based on the detection result, the multi-object tracking method of vehicle speed prediction in key frames is used to obtain the vehicle trajectory. Then according to the vehicle trajectory obtained in a single scene, the vehicle trajectory is reidentified and associated in the continuous monitoring scene to construct a long-distance vehicle trajectory spatiotemporal map. Finally, according to the distribution and generation status of the trajectory in the spatiotemporal map, traffic incidents such as vehicle parking, vehicle speeding, and vehicle congestion are analyzed. Experimental results show that the proposed method greatly increases the speed of vehicle detection and tracking and obtains high mAP, MOTA, and MOTP indicators. The global spatiotemporal map constructed by trajectory reidentification can achieve high detection rates for traffic incidents, reduce the average elapsed time, and avoid the problems of the inaccuracy of analyzing image features. [ABSTRACT FROM AUTHOR]

Geographical information is gaining new momentum as an analysis and visualization tool for collections of cultural objects. It provides all kinds of users with new opportunities to contextualize and understand these objects in ways that resemble our ordinary spatially-located experience and to do so better than textual narratives. The SeMap project has built an online resource that shows more than 200,000 cultural objects through spatiotemporal maps, thus enabling new experiences and perspectives around these objects. Data come from the CER.ES repository and were created by a network of more than 100 Spanish museums. This article explains the refinement of the data provided by the repository, mostly by adding a semantic structure thanks to the CIDOC-CRM ontology, and by simplifying the exceedingly complex terminologies employed in the original records. Particular attention is paid to the methods for geolocating the information, as well as adding temporal filters (among others) to user queries. The functionalities, interface, and technical requirements are also explored at length.

Remote photoplethysmography (rPPG) is an effective way of measuring Heart Rate (HR) from facial video of a person. Several methods have been proposed but measurement in realistic situation i.e, uncontrolled motion and illumination, is still a challenging task. It is observed that the performance of state-of-the-art methods often gets degraded in weekly illuminated environment. In this paper, a simple fusion based approach is proposed to reduce the performance degradation due to non-uniform illumination. An algorithm is proposed to fuse two complimentary color spaces i.e, Red, Green and Blue (RGB) and Multi Scale Retinex (MSR). The methodology includes salient spatiotemporal map generation from fusion of RGB and MSR. CNN is employed to map spatiotemporal features to corresponding HR value. To prove the efficacy of the proposed method, comparison is done with the state-of-the art methods with and without deep learning approach. Achieved experimental results proves that the proposed method yields comparable result for both inter and intra database testing on publicly available databases. • A novel HR estimation method, using RGB and MSR images for varying illumination. • Spatiotemporal maps are formed to improve the HR detection accuracy. • CNN is used to estimate HR from spatiotemporal maps. [ABSTRACT FROM AUTHOR]

Spontaneous regular gut motility in zebrafish begins around 4 days post fertilisation (d.p.f.) and is modulated by release of acetylcholine and nitric oxide. The role of intrinsic or extrinsic innervation for initiating and propagating the spontaneous contractions, however, is not well understood. By creating spatiotemporal maps, we could examine spontaneous motility patterns in zebrafish larvae in vivo at 4 and 7 d.p.f. in more detail. Tetrodotoxin (TTX) was added to elucidate the importance of nervous control. Anterograde and retrograde contraction waves originated in the same region, just posterior to the intestinal bulb. This area correlates well with the distribution of Hu (human neuronal protein C/D)-immunoreactive nerve cell bodies. Whereas numerous immunoreactive nerve cells were present in the mid and distal intestine at both 4 and 7 d.p.f., fewer cells were seen anterior to the origin of contractions. The overall frequency of contractions (1.16±0.15 cycles min-1, N=14 at 4 d.p.f.; 1.05±0.09 cycles min-1, N=13 at 7 d.p.f.) and the interval between individual anterograde contraction waves (54.8±7.9 s at 4 d.p.f., N=14; 56.9±4.4 s, N=13 at 7 d.p.f.) did not differ between the two stages but the properties of the contractions were altered. The distance travelled by each wave increased from 591.0±43.8 μm at 4 d.p.f. (N=14) to 719.9±33.2 μm at 7 d.p.f. (N=13). By contrast, the velocity decreased from 4 d.p.f. (49.5 ±5 .5 μm s-1, N=12) to 7 d.p.f. (27.8±3.6 μm s-1, N=13). At 4 d.p.f., TTX did not affect any of the parameters whereas at 7 d.p.f. anterograde frequency (control 1.07±0.12 cycles min-1, N=8; TTX 0.55±0.13 cycles min-1, N=8) and distance travelled (control 685.1±45.9 μm, N=8; TTX 318.7±88.7 μm, N=6) were decreased. In conclusion, enteric or extrinsic innervation does not seem to be necessary to initiate spontaneous contractions of the gut in zebrafish larvae. However, later in development, nerves have an increasingly important role as modulators of intestinal activity. [ABSTRACT FROM AUTHOR]