Ergonomic posture correction through a camera live feed and its applicability in terms of usability.
2020 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE credits
Student thesis
Abstract [en]
In this thesis, we summarize our empirical research as well as findings towards an implementation and perception of a live feedback posture correction tool. Our goal is to develop a tool that could help a sedentary user to maintain correct posture without having to evaluate by themselves if their sitting position is ergonomical. The focus of the thesis lies in the identification of a posture recognizing tool, how to make use of the posture recognizing tool to evaluate posture status, and how the user interface should be presented towards the user. We also, based on user experience, focus on the usability in terms of effectiveness, efficiency, satisfaction and user friendliness of said posture correction tool.We chose to evaluate how well 3 different posture detection algorithms would fit our implementation based upon a certain number of criteria. We used the algorithm to get joint position data and drew out vectors between every joint. With our vectors we could calculate the angles between limbs in the human body and by doing so, evaluate if the user has good or bad posture. We conducted experiments on 7 participants towards our system, 3 of them are UX experts and 4 of them had no previous experience in UX design, after each experiment we provided the participant with a survey to share their experiences. The survey was focused on usability and user experiences. We then drew an analysis from our empirical research to form a conclusion.We managed to create a fully functional prototype which accurately could determine the participants posture. Out of our 3 different posture detection algorithms, we selected Openpose. We used their unity plugin and were able to calculate angles between limbs with unity’s built in functions towards vectors. From our testing, we got a lot of useful critique towards how the tool waspresented and explained towards the user, most of the feedback was towards the guidelines of the camera setup and its lack of guidance in that phase. We also received very positive feedback towards the tool and functionality itself, how responsive and accurate the system was for example.We have concluded that even though the tool we developed has some faults from a user perspective, we have come a long way to create something innovative and interesting. If there would be more time and resources to be spent towards the tool, this tool could definitely become big in the ergonomic world.
Place, publisher, year, edition, pages
2020. , p. 65
Keywords [en]
Ergonomic Posture detection, Machine learning with a neural network, HCI - Human computer interaction, Usability
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-20079OAI: oai:DiVA.org:bth-20079DiVA, id: diva2:1450425
Subject / course
PA1445 Kandidatkurs i Programvaruteknik
Educational program
PAGPT Software Engineering
Supervisors
Examiners
2020-07-022020-07-012020-07-02Bibliographically approved