Mobile sensing of pedestrian flocks in indoor environments using WiFi signals

AbstractHuman distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ ± 0.2 m in continuous-based estimation and $$96.8\%$$ 96.8 % achieved-accuracy in discrete distance estimation.

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Testing the Toxicity of Stachybotrys chartarum in Indoor Environments—A Case Study

Energies ◽

10.3390/en14061602 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1602

Author(s):

Marlena Piontek ◽

Katarzyna Łuszczyńska

Keyword(s):

Health Concern ◽

Major Constituent ◽

Stachybotrys Chartarum ◽

Malt Extract ◽

Indoor Environments ◽

Class Iii ◽

Potential Health ◽

Dugesia Tigrina ◽

Ecotoxicological Tests ◽

Interior Walls

Infestation of interior walls of buildings with fungal mould is a reason for health concern which is exacerbated in energy-efficient buildings that limit air circulation. Both mycological and mycotoxicological studies are needed to determine the potential health hazards to residents. In this paper, a rare case of the occurrence of Stachybotrys chartarum in an apartment building in the Lubuskie Province in Poland has been described. Isolated as the major constituent of a mixed mycobiota, its specific health relevance still needs to be carefully analyzed as its biochemical aptitude for the synthesis of mycotoxins may be expressed at different levels. Therefore, ecotoxicological tests were performed using two bioindicators: Dugesia tigrina Girard and Daphnia magna Straus. D. tigrina was used for the first time to examine the toxicity of S. chartarum. The ecotoxicological tests showed that the analyzed strain belonged to the third and fourth toxicity classes according to Liebmann’s classification. The strain of S. chartarum was moderately toxic on Potato Dextrose Agar (PDA) as a culture medium (toxicity class III), and slightly toxic on Malt Extract Agar (MEA) (toxicity class IV). Toxicity was additionally tested by instrumental analytical methods (LC-MS/MS). This method allowed for the identification of 13 metabolites (five metabolites reported for Stachybotrys and eight for unspecific metabolites). Spirocyclic drimanes were detected in considerable quantities (ng/g); a higher concentration was observed for stachybotryamide (109,000 on PDA and 62,500 on MEA) and lower for stachybotrylactam (27,100 on PDA and 46,300 on MEA). Both may explain the result observed through the bioindicators. Highly toxic compounds such as satratoxins were not found in the sample. This confirms the applicability of the two bioindicators, which also show mutual compatibility, as suitable tools to assess the toxicity of moulds.

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Semantic 3D Mapping from Deep Image Segmentation

Applied Sciences ◽

10.3390/app11041953 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1953

Author(s):

Francisco Martín ◽

Fernando González ◽

José Miguel Guerrero ◽

Manuel Fernández ◽

Jonatan Ginés

Keyword(s):

Point Cloud ◽

Autonomous Mobile Robots ◽

Semantic Mapping ◽

Indoor Environments ◽

3D Mapping ◽

Direct Mapping ◽

Learning Techniques ◽

3D Camera ◽

Deep Image ◽

Boundary Pixel

The perception and identification of visual stimuli from the environment is a fundamental capacity of autonomous mobile robots. Current deep learning techniques make it possible to identify and segment objects of interest in an image. This paper presents a novel algorithm to segment the object’s space from a deep segmentation of an image taken by a 3D camera. The proposed approach solves the boundary pixel problem that appears when a direct mapping from segmented pixels to their correspondence in the point cloud is used. We validate our approach by comparing baseline approaches using real images taken by a 3D camera, showing that our method outperforms their results in terms of accuracy and reliability. As an application of the proposed algorithm, we present a semantic mapping approach for a mobile robot’s indoor environments.

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