Autor: Dobosz, P. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Fall detection of the elderly using a smartphone
Autorzy:: Dobosz, K.
Wreczycki, P.
Powiązania:: https://bibliotekanauki.pl/articles/333233.pdf
Data publikacji:: 2016
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: elderly
fall detection
smartphone
accelerometer
osoby starsze
detekcja upadków
smartfon
akcelerometr
Opis:: Fall detection of the elderly is a major public health problem. The probability of falls makes them dependent on others and restricts their freedom of movement. Although many fall detection methods have been developed to recognize falls in a real-time, most are inaccurate and inconvenient to use. In this paper we describe two methods for detecting the fall of a human body that can be implemented for the smartphones with built-in accelerometer. The first one used the raw data obtained from the sensor, and the second one - filtered data. In addition to the measuring a load factor, an important role in the algorithms has also a mobile device orientation to the ground. The assumption for the study was the localization of the smartphone in a right pocket of trousers - common in right-handed people. The experiment consisted in simulation the falls from different initial postures (standing, sitting, kneeling) in four directions (front, back, left, right). The results are satisfactory for detection of falls from a standing position. In conclusion, correct detection of falls based on the accelerometer built into the smartphone is possible after the filtration of the raw data, although the location of this device, the initial body position and direction of the fall have significant impact.
Źródło:: Journal of Medical Informatics & Technologies; 2016, 25; 19-27
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Brain-computer interface for mobile devices
Autorzy:: Dobosz, K.
Wittchen, P.
Powiązania:: https://bibliotekanauki.pl/articles/333571.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: brain computer interface
mobile devices
software tool
motor disability
interfejs mózg-komputer
urządzenia mobilne
oprogramowanie
Opis:: The article presents the results of research in controlling the mobile application with the EEG signals and eye blinking. Authors proposed a prototype solution of a brain-computer interface that can be used by people with total motor impairment to control chosen mobile application on their mobile phone. There was a NeuroSky MindWave Mobile device used during experiments. Two software tools for mobile devices were specially implemented. First one helps to analyse the EEG signals and recognize eye blinks, second one - interprets them and executes assigned actions. Different configurations of settings were used during the studies. They included: single blink or double blink, level of focus, period of focus. Experiments results show that a man equipped with a personal EEG sensor and eye blinking detector can remotely touchless use mobile applications installed on smartphones or tablets.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 215-222
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: The pair-wise linear classifier and the k-NN rule in application to ALS progression differentiation
Autorzy:: Sokołowska, B.
Jóźwik, A.
Niebroj-Dobosz, I.
Janik, P.
Powiązania:: https://bibliotekanauki.pl/articles/333011.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie wzorców
wybór funkcji
klasyfikator liniowy
zasada k-NN
biomarkery
stwardnienie zanikowe boczne
pattern recognition
feature selection
linear classifier
k-NN rule
pair-wise classifier
biomarkers
amyotrophic lateral sclerosis
Opis:: The two kinds of classifier based on the k-NN rule, the standard and the parallel version, were used for recognition of severity of ALS disease. In case of the second classifier version, feature selection was done separately for each pair of classes. The error rate, estimated by the leave one out method, was used as a criterion as for determination the optimum values of k's as well as for feature selection. All features selected in this manner were used in the standard and in the parallel classifier based on k-NN rule. Furthermore, only for the verification purpose, the linear classifier was applied. For this kind of classifier the error rates were calculated by use the training set also as a testing one. The linear classifier was trained by the error correction algorithm with a modified stop condition. The data set concerned with the healthy subjects and patients with amyotrophic lateral sclerosis (ALS). The set of several biomarkers such as erythropoietin, matrix metalloproteinases and their tissue inhibitors measured in serum and cerebrospinal fluid (CSF) were treated as features. It was shown that CSF biomarkers were very sensitive for the ALS progress.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 20; 79-83
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Analysis of matrix metalloproteinases (MMPs) in cerebrospinal fluid of patients with amyotrophic lateral sclerosis (ALS)
Autorzy:: Sokołowska, B.
Jóźwik, A.
Niebroj-Dobosz, I.
Janik, P.
Kwieciński, H.
Powiązania:: https://bibliotekanauki.pl/articles/333116.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: klasyfikacja K-NN
stwardnienie zanikowe boczne
metaloproteinazy macierzy
płyn mózgowo-rdzeniowy
pattern recognition
k-NN classifier
amyotrophic lateral sclerosis
matrix metalloproteinase
cerebrospinal fluid
Opis:: Matrix metalloproteinases (MMPs) are implicated in the pathogenesis of motor neuron degeneration in amyotrophic lateral sclerosis (ALS) and might be potential markers of diagnosis, prognosis and monitoring treatment effects. The aim of the present study was evaluation of the MMPs significance in cerebrospinal fluid (CSF MMPs) of patients with ALS in relation to severity of the disease. Metalloproteinases MT-MMP-1, MMP-2, MMP-9 and additionally age of subjects and disease duration were analyzed. The results demonstrate that the error of differentiation between healthy subjects and ALS patients (for MMP-2 feature) as well as between mild and severe ALS states (for CSF MMPs set) equalled to 0.033. In conclusion, the pattern recognition approach may be useful for differentation of ALS progressing on the basis of CSF MMPs features.
Źródło:: Journal of Medical Informatics & Technologies; 2009, 13; 147-150
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Pattern recognition approach to differentiation of disease severity in patients with amyotrophic lateral sclerosis
Autorzy:: Jóźwik, A.
Sokołowska, B.
Niebroj-Dobosz, I.
Janik, P.
Kwieciński, H.
Powiązania:: https://bibliotekanauki.pl/articles/333433.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie obrazu
klasyfikacja K-NN
erytropoetyna
pattern recognition
k-NN classifier
amyotropic lateral sclerosis
erythropoietin
Opis:: A possibility of recognition of the clinical status of patients with amyotrophic lateral sclerosis (ALS) in relation to severity of the disease was investigated. Three groups: (i) healthy controls (n=15) and two subgroups of ALS patients (ii) mild (n=15) and (iii) severe (n=15) were considered as classes. Four features of the subjects: (i) their age (AGE) (ii) erythropoietin concentration in serum (SERUM), (iii) in cerebrospinal fluid (CSF), and (iv) duration time of the disease (Tdis) were used for classifier construction based on the k Nearest Neighbours (k-NN) rule, known from pattern recognition theory. The presented results demonstrate that the pattern recognition approach may be useful for the evaluation of the severity of the ALS disease.
Źródło:: Journal of Medical Informatics & Technologies; 2008, 12; 143-147
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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