## 1 Introduction  
Back pain and musculoskeletal disorders (MSDs) bear well-understood social significance because they influence people's productivity and quality of life [1, 2]. In numerous studies, it has been reported that back pain and the emergence of MSDs often occur among individuals who spend prolonged time working with computer screens, keyboards, and mice. The primary reason for MSDs, chronic back or neck pain, and other chronic pain conditions is improper sitting positions for prolonged hours. The improper sitting positions are most often due to the non-ergonomic environment set by the old-fashioned paradigm, which forces computer users to adapt to ad hoc computer tables/desks at home or in the office [3, 4]. Even well-informed computer users and professional software developers who are working in an ergonomic environment (including office chairs, desks, keyboards, mice, etc.) tend to complain of body pain, most often due to the prolonged hours in front of the computer and the inevitable deviations from the proper sitting posture caused by the accumulation of fatigue or relaxing of muscles due to intense focusing on work tasks [4, 5]. In recent years, plenty of research efforts have focused on research and innovation activities that aim to provide technological support for the prevention of back and neck pain or the development of MSDs. These studies have demonstrated that despite some well-understood shortcomings, technological tools can play an important role in the process of unhealthy habit alteration, including MSD prevention through smartphone-based applications [6; 7], multisensory smart-chair systems [8; 9], or other specialized technology [10]. Specifically, a smart-chair system using six pressure sensors and six infrared reflective distance sensors was reported to identify sitting postures in real time [8]. Posture identification accuracy of up to 92% was reported for the \(k\)-nearest neighbors (\(k\)-NN) classifier. Another recent study on sitting posture recognition successfully captured and distinguished ten common sitting postures based on arrays of 12 to 25 pressure sensors placed on cushioned chairs in different configurations [9]. The applicability of various classification methods was evaluated, including \(k\)-NN, support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR). Recognition accuracy of up to 99.18% was reported for the SVM-based classifier with 25 sensors. These and many other studies that rely on pressure and ultrasonic or reflective distance estimation sensors have shown excellent results and will hopefully be embedded in office equipment. However, as they all depend on the availability of smart-chair equipment, which is not yet readily available in low-cost office environments, their contribution to MSD prevention is limited. A recent study reported on the development of a sitting posture monitoring system using low-cost electromyography (EMG) sensors in the context of energy-efficient Bluetooth communication [11]. The improper sitting posture monitoring was addressed through both binary classification (distinguishing between good and bad postures) and multi-class classification (identifying good posture and deviations tilted to the front, right, and left). The performance of various machine learning methods, including SVM, \(k\)-NN, DT, RF, and multi-layer perceptron neural network (MLP NN), was evaluated on a real-world dataset. The highest classification accuracy (91%) was reported with a \(k\)-NN-based detector achieving real-time performance. A comprehensive review [12] covered over 130 publications that made use of machine learning algorithms for preventing or detecting MSD. The analysis identified artificial neural network (ANN), DT, and SVM as the most frequently used classification methods. These studies report on the development of methods and resources using one or several types of sensors, primarily focusing on specific technologies or applications.