FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765693785> ?p ?o ?g. }

• W2765693785 endingPage "131" @default.
• W2765693785 startingPage "119" @default.
• W2765693785 abstract "Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric." @default.
• W2765693785 created "2017-11-10" @default.
• W2765693785 creator A5004185756 @default.
• W2765693785 creator A5009963037 @default.
• W2765693785 creator A5011513807 @default.
• W2765693785 creator A5013398412 @default.
• W2765693785 creator A5014409004 @default.
• W2765693785 creator A5017748174 @default.
• W2765693785 creator A5020278451 @default.
• W2765693785 creator A5030506936 @default.
• W2765693785 creator A5033331389 @default.
• W2765693785 creator A5034996314 @default.
• W2765693785 creator A5037526812 @default.
• W2765693785 creator A5043206017 @default.
• W2765693785 creator A5046135003 @default.
• W2765693785 creator A5047846421 @default.
• W2765693785 creator A5049174479 @default.
• W2765693785 creator A5049267208 @default.
• W2765693785 creator A5056750841 @default.
• W2765693785 creator A5061155845 @default.
• W2765693785 creator A5065605923 @default.
• W2765693785 creator A5068258238 @default.
• W2765693785 creator A5076288422 @default.
• W2765693785 creator A5079147780 @default.
• W2765693785 creator A5088370205 @default.
• W2765693785 date "2018-01-01" @default.
• W2765693785 modified "2023-10-13" @default.
• W2765693785 title "Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit" @default.
• W2765693785 cites W1456053769 @default.
• W2765693785 cites W1479935944 @default.
• W2765693785 cites W1492555660 @default.
• W2765693785 cites W1703062847 @default.
• W2765693785 cites W1900048720 @default.
• W2765693785 cites W1920394150 @default.
• W2765693785 cites W1964200076 @default.
• W2765693785 cites W1966267838 @default.
• W2765693785 cites W1968018273 @default.
• W2765693785 cites W1968232944 @default.
• W2765693785 cites W1983960824 @default.
• W2765693785 cites W1985419794 @default.
• W2765693785 cites W1998182557 @default.
• W2765693785 cites W1998395846 @default.
• W2765693785 cites W2008560062 @default.
• W2765693785 cites W2013818400 @default.
• W2765693785 cites W2016043834 @default.
• W2765693785 cites W2024046085 @default.
• W2765693785 cites W2025926226 @default.
• W2765693785 cites W2042094283 @default.
• W2765693785 cites W2063024868 @default.
• W2765693785 cites W2064675550 @default.
• W2765693785 cites W2079985134 @default.
• W2765693785 cites W2084553926 @default.
• W2765693785 cites W2112796928 @default.
• W2765693785 cites W2114691160 @default.
• W2765693785 cites W2117037448 @default.
• W2765693785 cites W2122354382 @default.
• W2765693785 cites W2127700844 @default.
• W2765693785 cites W2128892560 @default.
• W2765693785 cites W2132894148 @default.
• W2765693785 cites W2134886133 @default.
• W2765693785 cites W2136883398 @default.
• W2765693785 cites W2145362071 @default.
• W2765693785 cites W2145954079 @default.
• W2765693785 cites W2149522871 @default.
• W2765693785 cites W2150656456 @default.
• W2765693785 cites W2171181994 @default.
• W2765693785 cites W2234559310 @default.
• W2765693785 cites W2238108400 @default.
• W2765693785 cites W2257979135 @default.
• W2765693785 cites W2260246117 @default.
• W2765693785 cites W2333467212 @default.
• W2765693785 cites W2345010043 @default.
• W2765693785 cites W2346427186 @default.
• W2765693785 cites W2465612755 @default.
• W2765693785 cites W2530887700 @default.
• W2765693785 cites W2531085657 @default.
• W2765693785 cites W2561981131 @default.
• W2765693785 cites W2562437049 @default.
• W2765693785 cites W2567718306 @default.
• W2765693785 cites W2588989789 @default.
• W2765693785 cites W2604019843 @default.
• W2765693785 cites W2605717917 @default.
• W2765693785 cites W2615874593 @default.
• W2765693785 cites W2919115771 @default.
• W2765693785 cites W2964199361 @default.
• W2765693785 cites W4212883601 @default.
• W2765693785 cites W4239510810 @default.
• W2765693785 doi "https://doi.org/10.1016/j.knosys.2017.10.017" @default.
• W2765693785 hasPublicationYear "2018" @default.
• W2765693785 type Work @default.
• W2765693785 sameAs 2765693785 @default.
• W2765693785 citedByCount "142" @default.
• W2765693785 countsByYear W27656937852018 @default.
• W2765693785 countsByYear W27656937852019 @default.
• W2765693785 countsByYear W27656937852020 @default.
• W2765693785 countsByYear W27656937852021 @default.
• W2765693785 countsByYear W27656937852022 @default.
• W2765693785 countsByYear W27656937852023 @default.

• next