Synergy of volunteer measurements and volunteer computing for effective data collecting, processing, simulating and analyzing on a worldwide scale

2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Stirenko

Alienin

et al. 2017

Self Cite

-The concept of the augmented coaching ecosystem for non-obtrusive adaptive personalized elderly care is proposed on the basis of the integration of new and available ICT approaches. They include multimodal user interface (MMUI), augmented reality (AR), machine learning (ML), Internet of Things (IoT), and machine-tomachine (M2M) interactions. The ecosystem is based on the Cloud-Fog-Dew computing paradigm services, providing a full symbiosis by integrating the whole range from low level sensors up to high level services using integration efficiency inherent in synergistic use of applied technologies. Inside of this ecosystem, all of them are encapsulated in the following network layers: Dew, Fog, and Cloud computing layer. Instead of the "spaghetti connections", "mosaic of buttons", "puzzles of output data", etc., the proposed ecosystem provides the strict division in the following dataflow channels: consumer interaction channel, machine interaction channel, and caregiver interaction channel. This concept allows to decrease the physical, cognitive, and mental load on elderly care stakeholders by decreasing the secondary human-to-human (H2H), human-to-machine (H2M), and machine-to-human (M2H) interactions in favor of M2M interactions and distributed Dew Computing services environment. It allows to apply this non-obtrusive augmented reality ecosystem for effective personalized elderly care to preserve their physical, cognitive, mental and social well-being.

Section: Some Implemented Combinations Of Componentsmentioning

confidence: 99%

Augmented Coaching Ecosystem for Non-obtrusive Adaptive Personalized Elderly Care on the basis of Cloud-Fog-Dew computing paradigm

2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Stirenko

Alienin

et al. 2017

Self Cite

“…The further potential for improvement consists in an increase of the investigated datasets:  the image size from the current 256×256 values up to 1024×1024 (ChestXRay dataset) and 2048×2048 (JSRT dataset);  the number of images from current 247 (JSRT and BSE-JSRT datasets) up to >1000 (MC dataset) and > 100 000 (ChestXRay dataset);  by data augmentation with regard to lossy and lossless transformations [17]. The much bigger progress can be obtained from integration of many similar datasets from numerous hospitals around the world in the spirit of the open science data, volunteer data collection, data processing and computing [19]. In conclusion, the results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

Advances in Intelligent Systems and Computing

Peng

Jiang

et al. 2018

Self Cite

158

107

The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).

“…But for this purpose additional research with usage of much larger datasets will be crucially important. In this connection, the more promicing progress can be obtained by creating and sharing the similar datasets around the world in the spirit of open science, volunteer data collection, processing and computing [39]- [41].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises

Peng

Zeng

2019 IEEE Symposium Series on Computational Intelligence (SSCI)

et al. 2019

The assessment of energy expenditure in real life is of great importance for monitoring the current physical state of people, especially in work, sport, elderly care, health care, and everyday life even. This work reports about application of some machine learning methods (linear regression, linear discriminant analysis, k-nearest neighbors, decision tree, random forest, Gaussian naive Bayes, support-vector machine) for monitoring energy expenditures in athletes. The classification problem was to predict the known level of the in-exercise loads (in three categories by calories) by the heart rate activity features measured during the short period of time (1 minute only) after training, i.e by features of the post-exercise load. The results obtained shown that the post-exercise heart activity features preserve the information of the in-exercise training loads and allow us to predict their actual in-exercise levels. The best performance can be obtained by the random forest classifier with all 8 heart rate features (micro-averaged area under curve value AU Cmicro = 0.87 and macro-averaged one AU Cmacro = 0.88) and the k-nearest neighbors classifier with 4 most important heart rate features (AU Cmicro = 0.91 and AU Cmacro = 0.89). The limitations and perspectives of the ML methods used are outlined, and some practical advices are proposed as to their improvement and implementation for the better prediction of in-exercise energy expenditures.