Wearables offer a promising solution for simultaneous posture monitoring and/or corrective feedback. The main objective was to identify, synthesise, and characterise the wearables used in the workplace to monitor and postural feedback to workers. The PRISMA-ScR guidelines were followed. Studies were included between 1 January 2000 and 22 March 2023 in Spanish, French, English, and Portuguese without geographical restriction. The databases selected for the research were PubMed^®, Web of Science^®, Scopus^®, and Google Scholar^®. Qualitative studies, theses, reviews, and meta-analyses were excluded. Twelve studies were included, involving a total of 304 workers, mostly health professionals (n = 8). The remaining studies covered workers in the industry (n = 2), in the construction (n = 1), and welders (n = 1). For assessment purposes, most studies used one (n = 5) or two sensors (n = 5) characterised as accelerometers (n = 7), sixaxial (n = 2) or nonaxialinertial measurement units (n = 3). The most common source of feedback was the sensor itself (n = 6) or smartphones (n = 4). Haptic feedback was the most prevalent (n = 6), followed by auditory (n = 5) and visual (n = 3). Most studies employed prototype wearables emphasising kinematic variables of human movement. Healthcare professionals were the primary focus of the study along with haptic feedback that proved to be the most common and effective method for correcting posture during work activities.

Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed^®, Web of Science^®, Institute of Electrical and Electronics Engineers^®, Science Direct^® and Google Scholar^®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs.

Assessment of muscle tone in a clinical setting is important for the physiotherapist to better analyse and establish appropriate treatments for CNS disorders. This study aims to review and summarise how to assess changes in tone in the context of adult rehabilitation. Secondarily, this study aimed to identify the central nervous system disorders, the respective variable/concept under study, and the testing procedures employed. PRISMA-ScR guidelines were followed using the “population”, “concept”, and “context” to define the eligibility criteria and to delineate the research question. PubMed^®, Science Direct^®, Web of Science^™, and Google Scholar^® databases were used to search the literature. The search included studies published between 2011 and March 2023 in Portuguese, English, French, and Spanish that assessed an adult population (>19 years) with CNS injury. Review articles, qualitative studies, conference proceedings, letters to the editor, and editorials were excluded. Initially, 1519 references were identified, of which eight met the eligibility criteria. The measurement instruments included the Modified Ashworth Scale (n = 5), the Modified Modified Ashworth Scale (n = 3), the BioTone^™ system (n = 2), the Montreal Spasticity Measurement (n = 1), and the Tone Evaluation Scale (n = 1). The health conditions considered included stroke sequelae (n = 7), multiple sclerosis (n = 4), spinal cord injuries (n = 4), cerebral palsy (n = 2), brain tumour (n = 2), and traumatic brain injuries (n = 3). The concepts of spasticity (n = 7) and muscle tone (n = 2) were explored. Considering the variables spasticity and muscle tone in different CNS disorders, mainly stroke, subjective instruments were preferred compared to objective ones, with the Modified Ashworth Scale being highlighted.

Nonlinear measures have increasingly revealed the quality of human movement and its behaviour over time. Further analyses of human movement in real contexts are crucial for understanding its complex dynamics. The main objective was to identify and summarize the nonlinear measures used in data processing during out-of-laboratory assessments of human movement among healthy adolescents. Summarizing the methodological considerations was the secondary objective. The inclusion criteria were as follows: According to the Population, Concept, and Context (PCC) framework, healthy teenagers between 10 and 19 years old that reported kinetic and/or kinematic nonlinear data-processing measurements related to human movement in non-laboratory settings were included. PRISMA-ScR was used to conduct this review. PubMed, Science Direct, the Web of Science, and Google Scholar were searched. Studies published between the inception of the database and March 2022 were included. In total, 10 of the 2572 articles met the criteria. The nonlinear measures identified included entropy (n = 8), fractal analysis (n = 3), recurrence quantification (n = 2), and the Lyapunov exponent (n = 2). In addition to walking (n = 4) and swimming (n = 2), each of the remaining studies focused on different motor tasks. Entropy measures are preferred when studying the complexity of human movement, especially multiscale entropy, with authors also carefully combining different measures, namely entropy and fractal analysis.

The widespread adoption of deep learning (DL) models in neuroscience research has introduced a fundamental epistemological paradox: while these models demonstrate remarkable performance in pattern recognition and prediction tasks, their inherent opacity contradicts neuroscience’s foundational goal of understanding biological mechanisms. This review article examines the growing trend of using DL models to interpret neural dynamics and extract insights about brain function, arguing that the black box nature of these models fundamentally undermines their utility for mechanistic understanding. We explore the distinction between computational performance and scientific explanation, analyze the limitations of current interpretability techniques, and discuss the implications for neuroscience research methodology. We propose that the field must critically evaluate whether DL models can genuinely contribute to our understanding of neural processes or whether they merely provide sophisticated curve-fitting tools that obscure rather than illuminate the underlying biology.

Objective: To analyse the relationship between traditional stiffness and muscle antagonist coactivation in both stroke and healthy participants, using linear and non-linear measures of coactivation and COP during standing, stand-to-sit, and gait initiation. Methods: Participants were evaluated through a cross-sectional design. Electromyography, isokinetic dynamometer, and force plate were used to calculate coactivation, intrinsic and functional stiffness, and COP displacement, with both linear and non-linear metrics. Spearman’s correlations and Mann–Whitney tests were applied (p < 0.05). Results: Post-stroke participants showed higher contralesional intrinsic stiffness (p = 0.041) and higher functional stiffness (p = 0.047). Coactivation was higher on the ipsilesional side during standing (p = 0.012) and reduced on the contralesional side during standing and transitions (p < 0.01). Moderate correlations were found between intrinsic and functional stiffness (p = 0.030) and between coactivation and intrinsic stiffness (standing and stand-to-sit: p = 0.048) and functional stiffness (gait initiation: p = 0.045). COP displacement was reduced in post-stroke participants during standing (p < 0.001) and increased during gait initiation (p = 0.001). Post-stroke participants exhibited increased gastrocnemius/tibialis anterior coactivation during gait initiation (p = 0.038) and higher entropy and stability across tasks (p < 0.001). Conclusion: Post-stroke participants showed higher contralesional intrinsic and functional stiffness, reduced coactivation in static tasks, and increased coactivation in dynamic tasks. COP and coactivation analyses revealed impaired stability and random control, highlighting the importance of multidimensional evaluations of postural tone.