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Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00

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Linda Ong (University of Groningen)
Ming Cao (University of Groningen)
G.J (Bart) Verkerke (University of Groningen, University of Twente)
Claudine J.C Lamoth (Universitair Medisch Centrum Groningen)
Elisabeth Wilhelm (University of Groningen)

Abstract:
Musculoskeletal disorders (MSDs) and metabolic syndrome (MetS) are two common health conditions in office workers. According to WHO, 1.7 billion people worldwide experience MSDs [1]. Around a quarter of cases occur in Europe [2] and MSDs frequently are related to the neck, upper, and lower back regions. Furthermore, MetS, which is known as a risk factor of developing chronic diseases, such as diabetes type II, and cardiovascular diseases, occurs up to 32.6% in office workers in some European countries [3]. Currently, support for the prevention of these two health issues is limited. People only seek treatment once the symptoms are perceived and they are limited in daily functioning. Unfortunately, body function limitation indicates MSDs and MetS might be already in the chronic phase, which will be more difficult to treat and reverse the condition. Therefore, the prevention of developing MSDs and MetS is necessary. One of the main risk factors for developing MSDs and MetS is sedentary behaviour during performing work that requires static and prolonged sitting. Even sitting upright can cause serious health issues if one keeps a static posture for a prolonged time. Therefore, the main aim of the study is to detect patterns in sitting behaviour, including different postures, sitting transition in postures while sitting, and duration of static postures. Existing sitting posture detection algorithms were based on pressure mat data in a controlled environment [4]. We will develop a posture detection algorithm based on real-life environment measurements. The study will involve 82 participants that spend most of their working hours (4-8 hours) in their working space sitting. The sitting monitoring is done for 5 working days. 80% of the data will be used for training, 10% for development, and 10% for testing. Sitting posture classification will be developed based on a novel machine learning approach while sitting transition and duration will be identified by thresholding the result from posture classification. To be useful for health advice, the system should classify common sitting postures as upright, slouch, slump, lean left, right, and backward with accuracy and f1-score of at least 85% and 80% respectively. The study has been running since mid-July 2022 and per October 2022, 15 participants have been following the whole measurement procedure.