scholarly journals POSITION, LOCATION, PLACE AND AREA: AN INDOOR PERSPECTIVE

2016 ◽  
Vol III-4 ◽  
pp. 89-96 ◽  
Author(s):  
George Sithole ◽  
Sisi Zlatanova
Keyword(s):  
Mobile Devices ◽  
Human Activity ◽  
Cognitive Maps ◽  
Indoor Environments ◽  
Position Location ◽  
And Topology ◽  
Different Types ◽  
Referential Expressions ◽  
Smart Mobile ◽  
Indoor Spaces

Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework. <br><br> Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible.

scholarly journals POSITION, LOCATION, PLACE AND AREA: AN INDOOR PERSPECTIVE

2016 ◽  
Vol III-4 ◽  
pp. 89-96 ◽  
Author(s):  
George Sithole ◽  
Sisi Zlatanova
Keyword(s):  
Mobile Devices ◽  
Human Activity ◽  
Cognitive Maps ◽  
Indoor Environments ◽  
Position Location ◽  
And Topology ◽  
Different Types ◽  
Referential Expressions ◽  
Smart Mobile ◽  
Indoor Spaces

Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework. &lt;br&gt;&lt;br&gt; Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible.

scholarly journals Energy-Efficient Real-Time Human Activity Recognition on Smart Mobile Devices

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jin Lee ◽  
Jungsun Kim
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Activity Recognition ◽  
Human Activity ◽  
Energy Efficient ◽  
Window Size ◽  
Human Activity Recognition ◽  
Smart Mobile ◽  
The Impact ◽  
Smart Mobile Devices

Nowadays, human activity recognition (HAR) plays an important role in wellness-care and context-aware systems. Human activities can be recognized in real-time by using sensory data collected from various sensors built in smart mobile devices. Recent studies have focused on HAR that is solely based on triaxial accelerometers, which is the most energy-efficient approach. However, such HAR approaches are still energy-inefficient because the accelerometer is required to run without stopping so that the physical activity of a user can be recognized in real-time. In this paper, we propose a novel approach for HAR process that controls the activity recognition duration for energy-efficient HAR. We investigated the impact of varying the acceleration-sampling frequency and window size for HAR by using the variable activity recognition duration (VARD) strategy. We implemented our approach by using an Android platform and evaluated its performance in terms of energy efficiency and accuracy. The experimental results showed that our approach reduced energy consumption by a minimum of about 44.23% and maximum of about 78.85% compared to conventional HAR without sacrificing accuracy.

scholarly journals Visual Positioning in Indoor Environments Using RGB-D Images and Improved Vector of Local Aggregated Descriptors

2021 ◽  
Vol 10 (4) ◽  
pp. 195
Author(s):  
Longyu Zhang ◽  
Hao Xia ◽  
Qingjun Liu ◽  
Chunyang Wei ◽  
Dong Fu ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Depth Information ◽  
Indoor Environments ◽  
Color Information ◽  
Training Set ◽  
Visual Positioning ◽  
Opponent Color ◽  
Positioning Method ◽  
Smart Mobile ◽  
Smart Mobile Devices

Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each image in the training set is calculated through feature extraction and description, image registration, and pose map optimization. Then, in the image retrieval stage, the training set and the query set are clustered to generate the vector of local aggregated descriptors (VLAD) description vector. In order to overcome the problem that the description vector loses the image color information and improve the retrieval accuracy under different lighting conditions, the opponent color information and depth information are added to the description vector for retrieval. Finally, using the point cloud corresponding to the retrieval result image and its pose, the pose of the retrieved image is calculated by perspective-n-point (PnP) method. The results of indoor scene positioning under different illumination conditions show that the proposed method not only improves the positioning accuracy compared with the original VLAD and ORB-SLAM2, but also has high computational efficiency.

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

Effects of urban noise variability on cognitive abilities in indoor spaces: Gender differences

2021 ◽  
pp. 095745652110307
Author(s):  
Massimiliano Masullo ◽  
Gennaro Ruggiero ◽  
Daniel Alvarez Fernandez ◽  
Tina Iachini ◽  
Luigi Maffei
Keyword(s):  
Working Memory ◽  
Verbal Memory ◽  
Cognitive Abilities ◽  
Cognitive Domain ◽  
Cognitive Tasks ◽  
Indoor Environments ◽  
Urban Noise ◽  
Male And Female ◽  
Open Window ◽  
Indoor Spaces

Previous evidence has shown that exposure to urban noise negatively influences some cognitive abilities (i.e. verbal fluency and delayed recall of prose memory) of people in indoor spaces. However, long-standing literature in the cognitive domain has reported that men and women can show different performance on cognitive tasks. Here, we aimed to investigate if and how different patterns of perceived urban noises in indoor environments could affect male and female participants’ cognitive abilities. Ambisonic sound recordings representing scenarios with varying noise patterns (low, medium and high variability) were acquired with an open window at three dwellings in a southern Italian city. As a control condition, the recordings were caught inside a quiet room. While exposed to theses four auditory conditions, participants had to perform cognitive tasks assessing free verbal memory recall, auditory–verbal recognition and working memory. The results show that male and female participants have a different tolerance to noise patterns. Women overperform men on verbal tasks, while the contrary effect emerges with men outperforming women on visuospatial working memory tasks.

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