Intelligent furniture : Reducing negative health effects of prolonged sitting
Hakkarainen, Aleksi (2019)
2019
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-201904134977
https://urn.fi/URN:NBN:fi:amk-201904134977
Tiivistelmä
The aim of this thesis was to learn by researching and examining whether an intelligent chair could reduce negative health effects of prolonged sitting and improve the user’s sitting position. Furthermore, the thesis aimed to find the most ideal and effective implementation of such chair.
The thesis has three main parts to it: First the thesis researches common health problems regarding prolonged sitting and how it is possible to prevent these. This part of the research was done by interviewing a physiotherapist and by using literature in the field and online sources.
The second part focuses on designing, building and programming a chair based on this information. Lastly, the third part focuses on researching whether the chair has an impact into the sitting habits of the test users. This was done by using a questionnaire and the logs that the chair recorded.
The data acquired during the third part indicated that the chair, that makes decisions based on the sitting habits of the test users and then communicates with them by vibrating the seat with different patterns to improve their sitting habits, can potentially have a positive impact on the sitting habits and sitting position of the user. More thorough testing is still needed with greater number of test users and chairs for more reliable results. Next version of the chair should be more ergonomic oriented and intelligent with the use of artificial neural networks in detecting reliably greater variety of different sitting positions.
The thesis has three main parts to it: First the thesis researches common health problems regarding prolonged sitting and how it is possible to prevent these. This part of the research was done by interviewing a physiotherapist and by using literature in the field and online sources.
The second part focuses on designing, building and programming a chair based on this information. Lastly, the third part focuses on researching whether the chair has an impact into the sitting habits of the test users. This was done by using a questionnaire and the logs that the chair recorded.
The data acquired during the third part indicated that the chair, that makes decisions based on the sitting habits of the test users and then communicates with them by vibrating the seat with different patterns to improve their sitting habits, can potentially have a positive impact on the sitting habits and sitting position of the user. More thorough testing is still needed with greater number of test users and chairs for more reliable results. Next version of the chair should be more ergonomic oriented and intelligent with the use of artificial neural networks in detecting reliably greater variety of different sitting positions.