Wissenschaftliche Arbeiten

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Abschlussarbeiten

2014

Wertner Alfred

array(37) { ["Start"]=> string(0) "" ["year"]=> int(2014) ["title"]=> string(76) "Stress prediction for knowledge workers based on PC activity and noise level" ["Abstract de"]=> string(3313) "Knowledge workers are exposed to many influences which have the potential to interrupt work. The impact of these influences on individual’s, not only knowledge workers, often cause detrimental effects on physical health and well-being. Twelve knowledge workers took part as participants of the experiment conducted for this thesis. The focus of the experiment was to analyse if sound level and computer interactions of knowledge workers can predict their self reported stress levels. A software system was developed using sensors on knowledge worker’s mobile and desktop devices. Records of PC activity contain information about foreground windows and computer idle times. Foreground window records include the timestamp when a window received focus, the duration the window was held in the foreground, the window title and the unique number identifying the window. Computer idle time records contain information about the timestamp when idle time began and the duration. Computer idle time was recorded only after a minimum idle interval of one minute. Sound levels were recorded using an smartphone’s microphone (Android). The average sound pressure level from the audio samples was computed over an one minute timeframe. Once initialized with an anonymous participant code, the sensors record PC activity and sound level and upload the records enriched with the code to a remote service. The service uses a key value based database system with the code as key and the collection of records as value. The service stores the records for each knowledge worker over a period of ten days. After this period, the preprocessing component of the system splits the records of PC activity and sound level into working days and computes measures approximating worktime fragmentation and noise. Foreground window records were used to compute the average time a window was held in the foreground and the average time an application was held in the foreground. Applications are sets of foreground window records which share the same window title. Computer idle time records were used to compute the number of idle times between one and five minutes and the period of those idle times which lasted more than twenty. From the sound pressure levels the average level and the period of all levels which exceeded 60 decibels were computed. The figures were computed with the scope of an participant’s working day for five different temporal resolutions. Additionally, the stress levels are computed from midday and evening scales. Participants recorded stress levels two times a working day and entered them manually in the system. The first self report was made close to lunch break and the second at the end of an day at work. Since participants forgot to enter self assessed stress levels, the number of working days containing data of all types ranges between eight and ten. As a result, the preprocessing component stores the measures and stress levels used by the stress predicition analysis component. The correlation of the measures with the self reported stress levels showed that a prediction of those stress levels is possible. The state of well-being (mood, calm) increased the higher the number of idle times between one and five minutes in combination with an sound pressure level not exceeding 60 decibels." ["AutorId"]=> string(0) "" ["author"]=> string(14) "Wertner Alfred" ["Autor_extern_Geschlecht"]=> string(9) "männlich" ["BetreuerId"]=> string(3) "143" ["Betreuer"]=> string(28) "Pammer-Schindler_TU Viktoria" ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(0) "" ["Zweitbetreuer"]=> string(0) "" ["Zweitbetreuer1_ID"]=> string(0) "" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "intern" ["Zweitbetreuer1_extern"]=> string(0) "" ["ZweitBetreuer1Affiliation"]=> string(0) "" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "intern" ["Zweitbetreuer2_extern"]=> string(0) "" ["ZweitBetreuer2Affiliation"]=> string(0) "" ["meta"]=> string(1) "2" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(0) "" ["Link"]=> string(0) "" ["ID"]=> string(3) "889" ["angestellt bei"]=> string(10) "TUG-IWT KC" ["Text_intern_extern"]=> string(0) "" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(14) "Wertner Alfred" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(9) "männlich" ["Erstelldatum"]=> string(10) "30/10/2017" ["Letzter_Aufruf"]=> string(10) "11.06.2018" ["Letzte_Änderung_Person"]=> string(10) "alangmaier" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "2" ["organ"]=> string(6) "Master" ["thesis"]=> string(6) "Master" }

Stress prediction for knowledge workers based on PC activity and noise level i

Master

Master
Knowledge workers are exposed to many influences which have the potential to interrupt work. The impact of these influences on individual’s, not only knowledge workers, often cause detrimental effects on physical health and well-being. Twelve knowledge workers took part as participants of the experiment conducted for this thesis. The focus of the experiment was to analyse if sound level and computer interactions of knowledge workers can predict their self reported stress levels. A software system was developed using sensors on knowledge worker’s mobile and desktop devices. Records of PC activity contain information about foreground windows and computer idle times. Foreground window records include the timestamp when a window received focus, the duration the window was held in the foreground, the window title and the unique number identifying the window. Computer idle time records contain information about the timestamp when idle time began and the duration. Computer idle time was recorded only after a minimum idle interval of one minute. Sound levels were recorded using an smartphone’s microphone (Android). The average sound pressure level from the audio samples was computed over an one minute timeframe. Once initialized with an anonymous participant code, the sensors record PC activity and sound level and upload the records enriched with the code to a remote service. The service uses a key value based database system with the code as key and the collection of records as value. The service stores the records for each knowledge worker over a period of ten days. After this period, the preprocessing component of the system splits the records of PC activity and sound level into working days and computes measures approximating worktime fragmentation and noise. Foreground window records were used to compute the average time a window was held in the foreground and the average time an application was held in the foreground. Applications are sets of foreground window records which share the same window title. Computer idle time records were used to compute the number of idle times between one and five minutes and the period of those idle times which lasted more than twenty. From the sound pressure levels the average level and the period of all levels which exceeded 60 decibels were computed. The figures were computed with the scope of an participant’s working day for five different temporal resolutions. Additionally, the stress levels are computed from midday and evening scales. Participants recorded stress levels two times a working day and entered them manually in the system. The first self report was made close to lunch break and the second at the end of an day at work. Since participants forgot to enter self assessed stress levels, the number of working days containing data of all types ranges between eight and ten. As a result, the preprocessing component stores the measures and stress levels used by the stress predicition analysis component. The correlation of the measures with the self reported stress levels showed that a prediction of those stress levels is possible. The state of well-being (mood, calm) increased the higher the number of idle times between one and five minutes in combination with an sound pressure level not exceeding 60 decibels.
2014

Prinz Martin

array(37) { ["Start"]=> string(0) "" ["year"]=> int(2014) ["title"]=> string(49) "Mobile Sensordata to Support Stroke Rehabilitatio" ["Abstract de"]=> string(0) "" ["AutorId"]=> string(0) "" ["author"]=> string(12) "Prinz Martin" ["Autor_extern_Geschlecht"]=> string(9) "männlich" ["BetreuerId"]=> string(3) "143" ["Betreuer"]=> string(28) "Pammer-Schindler_TU Viktoria" ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(0) "" ["Zweitbetreuer"]=> string(0) "" ["Zweitbetreuer1_ID"]=> string(0) "" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "intern" ["Zweitbetreuer1_extern"]=> string(0) "" ["ZweitBetreuer1Affiliation"]=> string(0) "" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "intern" ["Zweitbetreuer2_extern"]=> string(0) "" ["ZweitBetreuer2Affiliation"]=> string(0) "" ["meta"]=> string(1) "1" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(0) "" ["Link"]=> string(0) "" ["ID"]=> string(3) "892" ["angestellt bei"]=> string(7) "Student" ["Text_intern_extern"]=> string(0) "" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(12) "Prinz Martin" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(9) "männlich" ["Erstelldatum"]=> string(10) "30/10/2017" ["Letzter_Aufruf"]=> string(10) "11.06.2018" ["Letzte_Änderung_Person"]=> string(10) "alangmaier" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "1" ["organ"]=> string(4) "Bakk" ["thesis"]=> string(4) "Bakk" }

Mobile Sensordata to Support Stroke Rehabilitatio

Bakk

Bakk
2014

Simon Jörg Peter

array(37) { ["Start"]=> string(0) "" ["year"]=> int(2014) ["title"]=> string(72) "Mobile Mind Mapping Applikationen – Anforderungen und Herausforderunge" ["Abstract de"]=> string(0) "" ["AutorId"]=> string(3) "127" ["author"]=> string(0) "" ["Autor_extern_Geschlecht"]=> string(0) "" ["BetreuerId"]=> string(3) "143" ["Betreuer"]=> string(28) "Pammer-Schindler_TU Viktoria" ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(0) "" ["Zweitbetreuer"]=> string(0) "" ["Zweitbetreuer1_ID"]=> string(0) "" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "intern" ["Zweitbetreuer1_extern"]=> string(0) "" ["ZweitBetreuer1Affiliation"]=> string(0) "" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "intern" ["Zweitbetreuer2_extern"]=> string(0) "" ["ZweitBetreuer2Affiliation"]=> string(0) "" ["meta"]=> string(1) "1" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(0) "" ["Link"]=> string(0) "" ["ID"]=> string(3) "893" ["angestellt bei"]=> string(2) "KC" ["Text_intern_extern"]=> string(2) "KC" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(17) "Simon Jörg Peter" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(9) "männlich" ["Erstelldatum"]=> string(10) "30/10/2017" ["Letzter_Aufruf"]=> string(10) "05.04.2018" ["Letzte_Änderung_Person"]=> string(14) "dhinterleitner" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "1" ["organ"]=> string(4) "Bakk" ["thesis"]=> string(4) "Bakk" }

Mobile Mind Mapping Applikationen – Anforderungen und Herausforderunge

Bakk

Bakk
2014

Keller Stephan

array(37) { ["Start"]=> string(0) "" ["year"]=> int(2014) ["title"]=> string(60) "Praktische Anwendungen von BCI unter Android – Tic Tac Toe" ["Abstract de"]=> string(0) "" ["AutorId"]=> string(0) "" ["author"]=> string(14) "Keller Stephan" ["Autor_extern_Geschlecht"]=> string(9) "männlich" ["BetreuerId"]=> string(3) "212" ["Betreuer"]=> string(25) "Pammer-Schindler Viktoria" ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(11) "Know-Center" ["Zweitbetreuer"]=> string(18) "Scherer Reinhold; " ["Zweitbetreuer1_ID"]=> string(0) "" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "extern" ["Zweitbetreuer1_extern"]=> string(16) "Scherer Reinhold" ["ZweitBetreuer1Affiliation"]=> string(3) "TUG" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "intern" ["Zweitbetreuer2_extern"]=> string(0) "" ["ZweitBetreuer2Affiliation"]=> string(0) "" ["meta"]=> string(1) "1" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(0) "" ["Link"]=> string(0) "" ["ID"]=> string(3) "894" ["angestellt bei"]=> string(7) "Student" ["Text_intern_extern"]=> string(0) "" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(14) "Keller Stephan" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(9) "männlich" ["Erstelldatum"]=> string(10) "30/10/2017" ["Letzter_Aufruf"]=> string(10) "11.06.2018" ["Letzte_Änderung_Person"]=> string(10) "alangmaier" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "1" ["organ"]=> string(4) "Bakk" ["thesis"]=> string(4) "Bakk" }

Praktische Anwendungen von BCI unter Android – Tic Tac Toe

Bakk

Bakk
Kontakt Karriere

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