Pretty Pelvis

Abstract

Prolonged sitting can be detrimental to our postures which can cause spinal and pelvic problems. It may also lead to a shortened lifespan due to its adverse impact on cardio metabolic health. One way of preventing and mitigating these risks is to break sedentary time. We find it difficult, however, to actively monitor ourselves and commit to making these breaks in the middle of studying or working. The work presented in this paper proposes a motivational smartphone application that connects to a sensor-equipped seat device to track sitting behavior. The application's distinguishing feature is its interactive virtual pet, which acts as a motivational factor to promote healthier sitting habits. The application analyzes sensor readings from the seat device and prompts users with their virtual pet to take physical action. Our prototype is the outcome of a thorough investigation of user needs.

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

“…These parts of the body have been associated with a lot of repetitive workplace stress injuries based on bad postures (Anderson and Oakman, 2016;Catanzarite et al, 2018;Krajnak, 2018). The study by Min et al (2015) and Mudiyanselage et al (2021) presents closely related concepts. Though these studies explored parts of the body such as the back, spine, arm, and wrists, they used sensors, which might not be comfortable for users of systems.…”

“…Research has explored persuasive technologies in relation to best workplace practices. This includes taking regular breaks (Jafarinaimi et al, 2005 ; Ludden and Meekhof, 2016 ; Ren et al, 2019 ), fitness apps (Mohadis et al, 2016 ; Ahtinen et al, 2017 ; Paay et al, 2022 ), feedback systems and wearable devices (Bootsman et al, 2019 ; Jiang et al, 2021 ), workstation movement (Min et al, 2015 ; Damen et al, 2020a , b ), chair, desk, and monitor height adjustments (Kronenberg and Kuflik, 2019 ; Kronenberg et al, 2022 ), posture correction (Min et al, 2015 ; Bootsman et al, 2019 ; Kim M. T. et al, 2019 ), mouse/keyboard use and reduction of glare and reflection (Bailly et al, 2016 ), and other healthy work behaviors (Berque et al, 2011 ; Mateevitsi et al, 2014 ; Gomez-Carmona and Casado-Mansilla, 2017 ; Jiang et al, 2021 ; Brombacher et al, 2023 ; Haliburton et al, 2023 ; Robledo Yamamoto et al, 2023 ).…”

Data_Sheet_1.docx

“…Persuasive systems based on the Yolo-V3 model can be trained to recognize various environmental conditions, such as the lighting conditions of the room, desk height, and leg position of users. While previous study by Min et al (2015) demonstrated the potential of using sensor reading based on back and arm movements, expanding to recognize more positions would necessitate multimodal datasets, sensors, and strategically positioned cameras to provide users with comprehensive feedback. It is important to note that this approach may require privacy permissions.…”

“…Research has explored persuasive technologies in relation to best workplace practices. This includes taking regular breaks (Jafarinaimi et al, 2005;Ludden and Meekhof, 2016;Ren et al, 2019), fitness apps (Mohadis et al, 2016;Ahtinen et al, 2017;Paay et al, 2022), feedback systems and wearable devices (Bootsman et al, 2019;Jiang et al, 2021), workstation movement (Min et al, 2015;Damen et al, 2020a,b), chair, desk, and monitor height adjustments (Kronenberg and Kuflik, 2019;Kronenberg et al, 2022), posture correction (Min et al, 2015;Bootsman et al, 2019;Kim M. T. et al, 2019), mouse/keyboard use and reduction of glare and reflection (Bailly et al, 2016), and other healthy work behaviors (Berque et al, 2011;Mateevitsi et al, 2014;Gomez-Carmona and Casado-Mansilla, 2017;Jiang et al, 2021;Brombacher et al, 2023;Haliburton et al, 2023;Robledo Yamamoto et al, 2023).…”

“…Despite extensive research within this domain, limited study has been conducted on camera posture positions on the face, head, neck, and arms. While Min et al (2015) explored body positions such as the back and spine using sensors, there is still a need to explore additional body positions captured by cameras. In a related study, Mudiyanselage et al (2021) evaluated a workplace that involved lifting work-related materials using wearable sensors and various machine learning models (Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest).…”