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Conference

IEEE International Symposium on Medical Measurements and Applications 

About: IEEE International Symposium on Medical Measurements and Applications is an academic conference. The conference publishes majorly in the area(s): Computer science & Imaging phantom. Over the lifetime, 1568 publications have been published by the conference receiving 9105 citations.


Papers
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Proceedings ArticleDOI
11 Jun 2014
TL;DR: A unobtrusive smartphone based fall detection system that uses a combination of information derived from machine learning classification applied in a state machine algorithm and can reliably detect fall events without disturbing the users with excessive false alarms.
Abstract: Falls are considered the main cause of fear and loss of independence among the elderly population and are also a major cause of morbidity, disability and health care utilization. In the majority of fall events external support is imperative in order to avoid major consequences. Therefore, the ability to automatically detect these fall events could help reducing the response time and significantly improve the prognosis of fall victims. This paper presents a unobtrusive smartphone based fall detection system that uses a combination of information derived from machine learning classification applied in a state machine algorithm. The data from the smartphone built-in accelerometer is continuously screened when the phone is in the user's belt or pocket. Upon the detection of a fall event, the user location is tracked and SMS and email notifications are sent to a set of contacts. The accuracy of the fall detection algorithm here proposed is near 97.5% for both the pocket and belt usage. In conclusion, the proposed solution can reliably detect fall events without disturbing the users with excessive false alarms, presenting also the advantage of not changing the user's routines, since no additional external sensors are required.

112 citations

Proceedings ArticleDOI
30 May 2011
TL;DR: The main objective of this paper is to analyze cough sounds and extract features that can be used in differentiation of dry and wet cough sounds using a set of eight highly dry and eight highly wet cough sound recordings.
Abstract: Differentiating dry and wet cough is an important factor in respiratory disease. The main objective of this paper is to analyze cough sounds and extract features that can be used in differentiation of dry and wet cough sounds. This paper proposes two features to achieve this goal. The first feature is the number of peaks of the energy envelope of the cough signal. The second feature is the power ratio of two frequency bands of the second phase of the cough signal. A set of eight highly dry and eight highly wet cough sound recordings were used. Using these two features, a clear separation was observed among the dry and wet cough sound recordings.

111 citations

Proceedings ArticleDOI
04 May 2013
TL;DR: This report presents the results of KINECT applications used in physical rehabilitation tests and the implementation, evaluation and advantages of a proposed “Real-time ROM Measurement” application, useful for enhancement of KinECT technical capabilities and for further advancements in medical care.
Abstract: This report presents the results of KINECT applications used in physical rehabilitation tests. Aoyama Gakuin and Kitasato universities collaborated on this project, which is supported by SCOPE. The applications, following standard tests, are for the timed “Up & Go Test”, the timed “10-Meter Walk Test” and for a Joint “Range of Motion” Measurement”; test results are given. The implementation, evaluation and advantages of a proposed “Real-time ROM Measurement” are also given. The proposed KINECT application will be useful for enhancement of KINECT technical capabilities and for further advancements in medical care.

86 citations

Proceedings ArticleDOI
11 Jun 2018
TL;DR: This work demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided in computer-aided mammography.
Abstract: Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.

77 citations

Proceedings ArticleDOI
18 May 2012
TL;DR: The system proposed in this paper aims to measure the heart rate of neonatal infants without any direct contact with the patient through the use of standard, low-cost and commercially available digital webcamera, based on a specifically developed algorithm.
Abstract: At present there is a clear need for non-contact monitoring of the physiological signs of the patients. The system proposed in this paper aims to measure the heart rate of neonatal infants without any direct contact with the patient. The solution proposed is based on the use of standard, low-cost and commercially available digital webcamera by which it has been possible to observe defined portions of the patient face; the sequence of such images has then been used, by a specifically developed algorithm (based on Indipendent Component Analysis), to extract the heart rate of the patients. Data collected on 7 patients demonstrate the feasibility of the measurement method proposed. Data acquired on the same patients with standard electrocardiography (ECG) has been used for comparison. Bland-Altman analysis of data show close correlation of the heart rates measured with the two approaches (correlation coefficient of 0.94) with an uncertainty of 4.5 bpm (k=1). This technique has a valuable interest for the use in clinical environment as non-contact, easily deployable and economic monitoring system, but it also shows an interesting potential for remote, home health monitoring.

75 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202399
2022177
2021122
2020150
201998
2018196