)1 Department of Mechanical and Machine Dynamics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; [EMAIL_ADDRESS] (A.P. )1 Department of Mechanical and Machine Dynamics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; emil. often remains overlooked. Recognizing the paramount importance of early detection, particularly in the context of osteoporosis, emerges as a crucial factor in averting fractures and alleviating the overall burden imposed by this debilitating condition. Regrettably, a noteworthy challenge persists in the realm of osteoporosis diagnostics, as a substantial majority of cases go undetected until a bone fracture occurs [4; 5]. This delay in diagnosis not only impedes timely intervention but also underscores the urgent need for increased emphasis on proactive screening and early detection strategies. By addressing this gap in the current healthcare landscape and placing a renewed focus on early diagnosis, we can significantly enhance our capacity to prevent fractures, mitigate the impact of osteoporosis, and ultimately improve the overall quality of care for individuals at risk. Therapy, medication, and fall prevention measures, along with lifestyle changes, can impact the daily living of a person with osteoporosis, reducing the occurrence of fractures by between 21% and 66% [6]. Hence, early intervention to prevent fractures can be achieved through earlier osteoporosis detection. Typically, osteoporosis is diagnosed when one of three things occur [7]: (1) fragility fractures at the vertebral column, hip joint, wrists, or shoulders, which can lead to further fractures and reduced quality of life, disability, and even mortality [8]; (2) a dual-energy X-ray absorptiometry (DEXA) scan showing a bone mineral density (BMD) T-score of less than or equal to \(-2.5\) in the lower spine and distal radius; or (3) the Fracture Risk Assessment Tool (FRAX), an online tool, showing a T-score of \(-1.0\) to \(-2.5\) with increased fracture risk (according to country-specific threshold). These diagnosis options, however, are not very useful in the early stages and are even less useful in trabecular bone osteoporosis. For example, DEXA scans are considered the gold standard for diagnosing osteoporosis and estimating fracture risk. However, DEXA scans may not produce accurate results if the person has osteoarthritis, spinal fractures, or if the bone mineral density is caused by vitamin D deficiency [9]. DEXA scanners produce 2D images of complex 3D structures, calculating bone density by the ratio of mineral content to bone area. However, this method has a drawback, as larger bones might appear stronger despite having similar density to smaller bones. Machine learning, a field concerned with training algorithms on pre-collected data, is gaining traction in osteoporosis diagnosis. Analyzing extensive patient data, machine learning identifies patterns and predicts osteoporosis development earlier, enabling more effective prevention and treatment strategies. A review of machine learning-based approaches to diagnosing osteoporosis reports that there is vast diversity in the success rates of the chosen machine learning algorithms [10]. Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) are among the most popular machine learning algorithms [8; 11; 12; 13; 14; 15; 16; 17]. Typically, studies involving classification tasks evaluate model performance through metrics such as Pearson's correlation coefficient, accuracy, and AUC [18; 19; 20; 21]. Yet, accuracy, influenced by the majority class, tends to overstate the true performance of machine learning models [22]. In this regard, a study was conducted on the use of machine learning to create personalized gait anomaly detectors that focus on individuals who have multiple sclerosis (MS) and need walking aids [23]. This technique used sensorized tips mounted on canes as data collectors to produce individual models that could identify alterations in the walking pattern reflecting changes in functional states.