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Mariusz Mazurek

dblp:272/3647 · DBLP profile ↗
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4ranked-venue papers
0as first author
4since 2021 · last 2024
0000-0002-9646-6897ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 4 · 4 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Face, body and person analysis · 44% Robot navigation and mapping · 44% Deep learning architectures and training · 13%
Computer networks
2 papers
Internet of things and sensor networks · 100%
Computer graphics and multimedia
1 paper
Image and video coding · 100%
Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 77% Wearable and physiological sensing · 23%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Medical and health informatics · 100%

Topics — the 10 heaviest of 12, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks › wearable computing › wearable sensing
wearable and physiological sensing
1.422024
Poster Abstract: Acoustic Analysis System for Monitoring Respiratory Disease Symptoms · SenSys 2024
Poster Abstract: A Wearable for Non-Invasive Monitoring and Diagnosing Functional Disorders of the Lower Urinary Tract · SenSys 2023
Computer vision › Face, body and person analysis
human pose estimation
0.812024
Poster Abstract: A Computer Vision System for Human Motion Monitoring on a Bicycle Trainer · SenSys 2024
Robotics › Robot navigation and mapping › state estimation › kinematic state estimation
joint angle estimation
0.812024
Poster Abstract: A Computer Vision System for Human Motion Monitoring on a Bicycle Trainer · SenSys 2024
Health and well-being technologies › physical activity
exercise monitoring
0.812024
Poster Abstract: A Computer Vision System for Human Motion Monitoring on a Bicycle Trainer · SenSys 2024
Image and video coding › transform coding
discrete cosine transform
0.512021
Image Reconstruction and Compression in Ultrasound Tomography Using Discrete Cosine Transform · SenSys 2021
Image and video coding
transform coding
0.512021
Image Reconstruction and Compression in Ultrasound Tomography Using Discrete Cosine Transform · SenSys 2021
Machine learning › Deep learning architectures and training
convolutional neural network
0.212024
Poster Abstract: Acoustic Analysis System for Monitoring Respiratory Disease Symptoms · SenSys 2024
Wearable and physiological sensing
motion sensor
0.212024
Poster Abstract: A Computer Vision System for Human Motion Monitoring on a Bicycle Trainer · SenSys 2024
Medical and health informatics › medical image reconstruction
image reconstruction
0.112021
Image Reconstruction and Compression in Ultrasound Tomography Using Discrete Cosine Transform · SenSys 2021
Medical and health informatics › medical imaging › ultrasound imaging
ultrasound tomography
0.112021
Image Reconstruction and Compression in Ultrasound Tomography Using Discrete Cosine Transform · SenSys 2021

Methods — techniques the papers use, named apart from their topics

resnet-50 · 1.5mobilenet · 1.5computer vision · 1.5camera calibration · 1.5CNN · 1.5electrostimulation · 1.3electromyography · 1.3electrical impedance tomography · 1.3discrete cosine transform · 1.0block-wise compression · 1.0
YearPublicationVenuePosition
2024 Poster Abstract: A Computer Vision System for Human Motion Monitoring on a Bicycle Trainer
abstract
During the first stage of the project a computer vision system for human motion tracking was developed. For its implementation, a pair of cameras and a bicycle trainer are required. Human movement is monitored in real time by an effective algorithm that determines the key angles between the joints of a person exercising on a bike trainer. The following phase of the work focused on comparing the discussed system with a reference professional set, based on human motion sensors. The gathered data was then further analysed and compared, indicating that the accuracy of the developed system is fully satisfactory.
Marcin Dziadosz, Mariusz Mazurek, Tomasz Rymarczyk, Dariusz Wójcik, Pawel Olszewski
SenSys2
2024 Poster Abstract: Acoustic Analysis System for Monitoring Respiratory Disease Symptoms
abstract
The aim of this work was to develop a proprietary device for real-time cough detection in audio recordings. Using models such as CNN, ResNet-50, and MobileNet, the system classifies cough sounds, enabling the early detection of potential infection cases, such as COVID-19. The device is intended for use in both private and public spaces, including medical facilities, nursing homes, and doctor's offices.
Tomasz Rymarczyk, Mariusz Mazurek, Marcin Dziadosz, Dariusz Wójcik, Oleksii Hyka, Krzysztof Król
SenSys2
2023 Poster Abstract: A Wearable for Non-Invasive Monitoring and Diagnosing Functional Disorders of the Lower Urinary Tract
abstract
The aim of the research was to develop a new device for monitoring and diagnosing functional disorders of the lower urinary tract, which will enable measurement of muscle tension (EMG) and electrical impedance tomography (EIT) with the possibility of electrostimulation of bladder reconstruction.
Tomasz Rymarczyk, Mariusz Mazurek, Oleksii Hyka, Dariusz Wójcik, Marcin Dziadosz, Marcin Kowalski
SenSys2
2021 Image Reconstruction and Compression in Ultrasound Tomography Using Discrete Cosine Transform
abstract
The study aims to develop a measurement system for acquiring ultrasonic transmission tomography data. Along building the automated measurement system we test the algorithm which use incorporation of image compression into the process of reconstruction what speed up computation and simplifies regularization of the solution to the inverse problem of tomography. The reconstructive algorithm used in transmission tomography is based on the Discrete Cosine Transformation method. The algorithm applies image compression techniques for each block of the image separately, according to the conducted test such a solution seems to be much more robust than classical methods used for ultrasonic tomography. Presented work is a part of the research that aim for applying ultrasonic technology for analyse and visualise technological processes in distributed systems and in the next step for development of wearable equipment for monitoring in medicine.
Konrad Kania, Mariusz Mazurek, Tomasz Rymarczyk, Tomasz Cieplak, Grzegorz Klosowski, Konrad Gauda
SenSys2