CC BY 4.0Schmidt, Angelina ClaraAngelina ClaraSchmidtShahid, HassanHassanShahidKraft, DimitriDimitriKraftBieber, GeraldGeraldBieberFellmann, MichaelMichaelFellmann2024-01-312024-01-312023https://publica.fraunhofer.de/handle/publica/459529https://doi.org/10.24406/publica-252410.1145/3615834.361584010.24406/publica-2524Sedentary behavior in office environments has become a widespread concern due to its negative impact on individuals’ health and well-being. This study not only addresses this issue by providing details about the musculoskeletal disorders pertinent to the wrist, shoulders, and neck that can develop due to immobility or prolonged sitting in front of a computer workstation, but also promotes the regular incorporation of three specific exercises for office workers. In particular, this study contains a comprehensive literature review covering the trade-offs between wearable devices and computer vision techniques in monitoring and counting the repetitive movements of various physical activities. Moreover, this study utilizes the Mediapipe pose estimation technique to track exercise performance and develops algorithms using a state machine for accurately counting repetitions during active breaks in an office environment. The dataset used to evaluate the methods employed consisted of a total of 36 videos and was gathered by engaging the employees working at Fraunhofer IGD and the University of Rostock. The findings of the research validated that the state machine could count the interventions with a mean accuracy of 92. This suggests its incorporation in the future on a larger scale by selecting more exercises, a larger dataset, and various environmental settings.enBranche: HealthcareResearch Line: Machine learning (ML)LTA: Interactive decision-making support and assistance systemsMachine learningHuman activity recognitionResearch Line: Human computer interaction (HCI)conference paper