Szymon Fedor
Massachusetts Institute of Technology
41 Papers
276 Citations
Szymon Fedor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 14, co-authored 37 publications. Previous affiliations of Szymon Fedor include Carrier Corporation & Dublin City University.
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Papers
Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies.
Evan M. Kleiman,Brianna J. Turner,Szymon Fedor,Eleanor E. Beale,Jeff C. Huffman,Matthew K. Nock +5 more
TL;DR: The results advance the understanding of how suicidal Ideation changes over short periods and provide a novel method of improving the short-term prediction of suicidal ideation.
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Automatic identification of artifacts in electrodermal activity data
Sara Taylor,Natasha Jaques,Weixuan Chen,Szymon Fedor,Akane Sano,Rosalind W. Picard +5 more
- 01 Jan 2015
TL;DR: The development of a machine learning algorithm for automatically detecting EDA artifacts is described, and an empirical evaluation of classification performance is provided.
Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry
TL;DR: Daily life and controlled study data, as well as existing evidence from neuroscience, are presented supporting the influence of multiple emotional substrates in the brain causing innervation on different sides of the body causing asymmetric EDA findings.
Digital phenotyping of suicidal thoughts
Evan M. Kleiman,Brianna J. Turner,Szymon Fedor,Eleanor E. Beale,Rosalind W. Picard,Jeffery C. Huffman,Matthew K. Nock +6 more
TL;DR: To examine whether there are subtypes of suicidal thinking using real‐time digital monitoring, which allows for the measurement of such thoughts with greater temporal granularity than ever before possible.
Objective assessment of depressive symptoms with machine learning and wearable sensors data
Asma Ghandeharioun,Szymon Fedor,Lisa Sangermano,Dawn F. Ionescu,Jonathan E. Alpert,Chelsea Dale,David Sontag,Rosalind W. Picard +7 more
- 02 Jul 2017
TL;DR: Analyzing the features and their relation to depressive symptoms, it was found that poor mental health was accompanied by more irregular sleep, less motion, fewer incoming messages, less variability in location patterns, and higher asymmetry of EDA between the right and the left wrists.
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