Citation: Feradov,F.; Markova, V.; Ganchev, T. Automated Detection of Improper Sitting Postures in Computer Users Based on Motion Capture Sensors. Computers 2022, 11, 116. https://doi.org/10.3390/ computers11070116 Academic Editors: Antonio Celesti, Ivanoe De Falco, Antonino Galletta and Giovanna Sannino Received: 31 May 2022 Accepted: 18 July 2022 Published: 20 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). computers Article Automated Detection of Improper Sitting Postures in Computer Users Based on Motion Capture Sensors Firgan Feradov *, Valentina Markova and Todor Ganchev Faculty of Computer Science and Automation, Technical University of Varna, 1 Studentska Str., 9010 Varna, Bulgaria; via@tu-varna.bg (V.M.); tganchev@tu-varna.bg (T.G.) * Correspondence: firgan.feradov@tu-varna.bg Abstract: Prolonged computer-related work can be linked to musculoskeletal disorders (MSD) in the upper limbs and improper posture. In this regard, we report on developing resources supporting improper posture studies based on motion capture sensors. These resources were used to create a baseline detector for the automated detection of improper sitting postures, which was next used to evaluate the applicability of Hjorth’s parameters—Activity, Mobility and Complexity—on the specific classification task. Specifically, based on accelerometer data, we computed Hjorth’s time-domain parameters, which we stacked as feature vectors and fed to a binary classifier (kNN, decision tree, linear SVM and Gaussian SVM). The experimental evaluation in a setup involving two different keyboard types (standard and ergonomic) validated the practical worth of the proposed sitting posture detection method, and we reported an average classification accuracy of up to 98.4%. We deem that this research contributes toward creating an automated system for improper posture monitoring for people working on a computer for prolonged periods. Keywords: machine-learning; work posture; posture detection; Hjorth’s parameters; ergonomic keyboard; standard keyboard 1. Introduction Computer-related activity is becoming a significant factor in the private and profes- sional life of people around the world. In 2021, the average number of people who used computers and the internet for work-related tasks in the European Union was 58% of the workforce, with this percentage varying between 37% and 85% for the different member countries and an average total increase of 14% for the period 2012–2021 [1]. It was docu- mented that prolonged computer use might be linked to a number of health problems, such as eyesight deterioration, weight gain and musculoskeletal disorders [2]. The development of musculoskeletal problems can be additionally highlighted, as it affects 47% of computer users [3], with problems manifesting most commonly as headaches, neck and shoulder problems and back pain. The most common causes of these health issues are incorrect and non-ergonomic work posture and environment, which can lead to neck–shoulder disorders [4–6] and discomfort of the upper limbs [7,8]. These conditions often result from static muscle load and awkward wrist position during keyboard operation [9]. Automated monitoring of the work environment and sitting postures during pro- longed computer work is an essential component of efforts for musculoskeletal health safeguarding. Studies aimed at assessing workstation usage data [10] show that recordings of workstation behavioral patterns contain sufficient differences, allowing for the distinc- tion of activities that lead to pain and fatigue or help avoid them. Based on these, different approaches for activity monitoring and assistance devices have been proposed. Among those are systems based on image processing techniques, which assess the correctness of the work posture [11–13], smart Internet of Things (IoT)-based office chairs, which classify Computers 2022, 11, 116. https://doi.org/10.3390/computers11070116 https://www.mdpi.com/journal/computers