The accurate measurement and analysis of human movement are essential in fields ranging from rehabilitation and neuroscience to sports science and ergonomics. Traditional methods, though precise, are often constrained by cost, accessibility, and controlled environments. The advent of machine learning (ML) pose estimation models (PEMs) offers an alternative solution, enabling detailed motion analysis using low-cost imaging systems in various settings. The aim of this review is to evaluate ML PEMs and their impact on human movement sciences, focusing on recent advancements in machine learning and computer vision for accurate, non-invasive motion analysis using low-cost imaging systems. A narrative review was conducted by searching electronic databases, including PubMed and Google Scholar, using key terms such as "machine learning," "pose estimation models," and "human movement sciences." Thematic analysis identified key advancements, applications, and challenges in ML PEMs across clinical, sports, and ergonomic contexts. The review highlights the development, capabilities, and applications of models such as OpenPose, PoseNet, AlphaPose, DeepLabCut, HRNet, MediaPipe Pose, BlazePose, EfficientPose, and MoveNet, emphasizing their potential for non-invasive, cost-effective assessments. In clinical settings, these models enable objective gait and posture analysis, aiding in diagnosing musculoskeletal disorders and tracking rehabilitation progress. In sports, ML PEMs enhance performance analysis and injury prevention by providing real-time feedback and detailed biomechanical data. In ergonomics, they offer proactive solutions for workplace injury prevention through real-time posture and movement analysis. While promising, the implementation of ML PEMs faces challenges in accuracy, data quality, and integration into existing practices. Establishing standardized protocols and frameworks is crucial for ensuring reliable, interdisciplinary applications. This review can be useful for coaches, healthcare professionals, and researchers in evaluating and implementing ML PEMs, ultimately advancing the field of human movement sciences.

Technological advancements in the analysis of human motion and posture management through digital devices

Peripheral nerve injury is a complex condition with a variety of signs and symptoms depending on the severity and nerves involved. Peripheral nerve damage may lead to sensory and motor functions deficits and even lifelong disability, causing important socioeconomic costs worldwide. Despite the increase in knowledge of the mechanisms of injury and regeneration, a full functional recovery is still unsatisfying in the majority of patients. It is well known that exercise promotes physical and psychological well-being, by ameliorating general health. In the last years, there has been a growing interest in evaluating the effects of exercise on the peripheral nervous system. Experimental works with rodent models showed the potential utility of exercise following peripheral nerve injuries, as evinced by increasing axon regeneration, muscle reinnervation, better recovery of strength, muscle mass and higher expression of neurotrophic factors. Moreover, clinical evidence showed positive trends in favour of physical therapy following peripheral nerve damage based on the improvement of range of motion (ROM), muscle power grade and pain. After a brief overview of peripheral nerve anatomy and the different types of nerve injury, the present review aims to summarize the impact of exercise on peripheral nerve regeneration. Some clinical evidence regarding the effect of exercise after peripheral nerve injury will also be discussed.

The COVID-19 outbreak forced Italian students to reduce their daily activities, inducing a seden-tary attitude that was worsened by distanced learning. This study aimed to survey the physical activity levels that were maintained before and during the social restrictions following the pan-demic, their correlation to musculoskeletal pain, as well as analyzing the impact of these COVID-19 restrictions on pain and fatigue that affects daily life activities. A total of 2044 students completed the online questionnaire, of which the results of 1654 participants were eligible. Before the pandemic, the levels of physical activity were distributed as: 19.9% no activity, 30.1% light ac-tivity, 21.5% moderate activity, and 28.5% high activity. After one year of the pandemic, 30.6% of the participants were inactive, 48.1%, 10.9%, and 10.5% stated as maintaining, respectively, light, moderate and high levels of physical activity. Furthermore, 43.5% reported neck pain and 33.5% stated to experience low back pain. Physical activity levels lower than 150 min/week may have predisposed students to suffer from neck pain (1.95 OR at 95% CI, 1.44–2.64) and low back pain (1.79 OR at 95% CI, 1.29–2.49). A positive correlation between physical activity levels, Verbal Descriptive Scale (VDS), and pain frequency have been observed for neck and low back pain (p-value < 0.05). Finally, low physical activity levels were associated with musculoskeletal pain onset and pain worsening.

The multifold benefits of regular physical exercise have been largely demonstrated in human and animal models. Several studies have reported the beneficial effects of physical activity, both in peripheral tissues and in the central nervous system (CNS). Regular exercise improves cognition, brain plasticity, neurogenesis and reduces the symptoms of neurodegenerative diseases, making timeless the principle of “mens sana in corpore sano” (i.e., a healthy mind in a healthy body). Physical exercise promotes morphological and functional changes in the brain, acting not only in neurons but also in astrocytes, which represent the most numerous glial cells in the brain. The multiple effects of exercise on astrocytes comprise the increased number of new astrocytes, the maintenance of basal levels of catecholamine, the increase in glutamate uptake, the major release of trophic factors and better astrocytic coverage of cerebral blood vessels. The purpose of this review is to highlight the effects of exercise on brain function, emphasize the role of astrocytes in the healthy CNS, and provide an update for a better understanding of the effects of physical exercise in the modulation of astrocyte function.

Different tools for the assessment of posture exist, from the simplest and cheap plumb line to complex, expensive, 3D-marker-based systems. The aim of this study is to present digital postural normative data of young adults collected through a mobile app to expand the possibilities of digital postural evaluation. A sample of 100 healthy volunteers, 50 males and 50 females, was analyzed with the mobile app Apecs-AI Posture Evaluation and Correction System^® (Apecs). The Student’s t-test evaluated differences between gender to highlight if the digital posture evaluation may differ between groups. A significant difference was present in the anterior coronal plane for axillary alignment (p = 0.04), trunk inclination (p = 0.03), and knee alignment (p = 0.01). Head inclination (p = 0.04), tibia shift (p = 0.01), and foot angle (p < 0.001) presented significant differences in the sagittal plane, while there were no significant differences in the posterior coronal plane. The intraclass correlation coefficient (ICC) was considered to evaluate reproducibility. Thirteen parameters out of twenty-two provided an ICC > 0.90, three provided an ICC > 0.60, and six variables did not meet the cut-off criteria. The results highlight that digital posture analysis of healthy individuals may present slight differences related to gender. Additionally, the mobile app showed good reproducibility according to ICC. Digital postural assessment with Apecs could represent a quick method for preventing screening in the general population. Therefore, clinicians should consider this app’s worth as an auxiliary posture evaluation tool.