Zakljucno, potrebno je i nadalje svakodnevno raditi na prevenciji demencije, osiguravati resurse i osmisljavati strategije borbe protiv demencije, jer ce broj osoba s demencijom i u Hrvatskoj u buducnosti biti veci. No, s obzirom da se u svijetu ulažu znatna sredstva u podrucju istraživanja demencije, za ocekivati je pozitivnih pomaka, kako na podrucju terapije, rane dijagnostike, tako i na podrucju rehabilitacije osoba s demencijom. Stoga - vrijeme je na nasoj strani!

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Dementia: a public health priority

The B Book

Shape is one of the most important features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exists. However, most of those methods either do not well represent shape or are difficult to do normalization (making matching hard). Among them, methods based Fourier descriptors (FD) achieve both well representation and well normalization. Different shape signatures have been exploited to derive FDs, however, FDs derived from different signatures can have significant different effect on the result of retrieval. In this paper, we build a Java retrieval framework to compare shape retrieval using FDs derived from different signatures. Common issues and techniques for shape representation and normalization are also analyzed in the paper. Data is given to show the retrieval result.

A comparative study on shape retrieval using Fourier descriptiors with different shape signatures

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Activity recognition is a popular research area with a number of applications, particularly in the smart home environment. The unique features of smart home sensors have challenged traditional data analysis methods. However, the recognition of anomalous activities is still immature in the smart home when compared with other domains such as computer security, manufacturing defect detection, medical image processing, etc. This chapter reviews smart home’s dense sensing approaches, an extensive review from sensors, data, analysis, algorithms, prompting reminder system, to the recent development of anomaly activity detection.

Activity and Anomaly Detection in Smart Home: A Survey

Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

https://www.mdpi.com/1424-8220/20/9/2641/pdf

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision.

Interconnectivity of digital camera with other devices is one of the main concerns of consumers and digital camera manufacturers. Interconnectivity features in digital cameras allow more consumer-friendly usage of digital cameras. Moreover, with a suitable application layer software, digital photographs can be sent directly from the camera to a desired target: disk storage, printer, Web site, as an e-mail message or Web print, using a single, purpose-designed, communication protocol: the picture transfer protocol, PTP.

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Digital camera connectivity solutions using the picture transfer protocol (PTP)

This paper presents an ambient intelligent system capable of monitoring and assisting human subjects at home. AmIHomCare not only monitors the vital signs of the patient or elderly people in attempt to identify a potential dangerous situation but it is also designed to ensure a personalized environment for the user. Other functions are offered: access control to restricted areas based on biometric recognition, gesture and object identification based on image retrieval and processing, mobile robot assistance, real time data processing, data logging and archiving, communication with a remote call center. The system is based on 4 independent modules which may cooperate if required by a specific situation. The core of the system is represented by the environment monitoring module where all the data from other modules is gathered and kept in a data base. The core also acts as a relay for alert messages that are being sent by the other modules (when detecting an emergency situation) to a call center. The medical personnel that arrives at patient's home has access to his/her medical history from the database. The results obtained so far show a correct proposed architecture, minimal adjustments need to be made when the system will be totally integrated.

AmIHomCare: a complex ambient intelligent system for home medical assistance

This paper presents a method for image based human activity recognition, in a smart environment. We use background subtraction and skeletisation as image processing techniques, combined with Artificial Neural Networks for human posture classification and Hidden Markov Models for activity interpretation. By this approach we successfully recognized basic human actions such as walking, rotating, sitting and bending up/down, lying and falling. The method can be applied in smart houses, for elderly people who live alone.

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Human Activity Recognition in Smart Environments

This paper describes an application that aims to stimulate physical activity adapted to elderly people. The application is implemented as a game with two avatars: user and trainer. The user avatar must reproduce the movements of the trainer's avatar. The similarity between two movements is computed using Dynamic Time Warping. Also the speed of the trainer is adapted to the user's movements using cross-correlation. Thus the game can stimulate for a long time physically activity. The preliminary evaluations with ten people have shown that the system can be an effective tool that engages users into physical activity.

Irina Mocanu

Papers

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision.

Digital camera connectivity solutions using the picture transfer protocol (PTP)

AmIHomCare: a complex ambient intelligent system for home medical assistance

Human Activity Recognition in Smart Environments

A Kinect based adaptive exergame