Search Results (143)

Home-based exercise management for people with knee osteoarthritis (KOA) is often limited by insufficient movement supervision, individualization, and progression support. This study described the development and preliminary evaluation of a KOA-specific 3D exergame exercise management system. The system combined markerless motion analysis using a conventional RGB camera with biomechanical analysis, concurrent avatar-based guidance, task-specific feedback, adaptive difficulty adjustment, pain monitoring, therapist override, and training records. Exercise tasks addressed lower-limb strength, balance, range of motion, and toe-in or toe-out gait retraining. Five physiotherapists conducted a preliminary evaluation of the prototype through technology-acceptance and usability questionnaires and semi-structured interviews. They reported generally favorable perceptions and identified clinically relevant exercise content, low sensing burden, comprehensive feedback, and training-progress monitoring as potential strengths. Concerns included fall risk during demanding tasks, limited individualization of some parameters, and insufficient safeguards for unsupervised use. Selected TDPT-derived kinematic measures were also compared with synchronized marker-based motion capture using a public dataset comprising 18 squat cycles from six participants. Agreement varied by measure: waveform associations were strong for functional hip flexion, knee flexion, trunk inclination, and frontal-plane hip abduction/adduction, whereas absolute errors ranged from 6.59° to 20.00° and the foot-orientation proxy showed negligible correlation. These findings provide preliminary clinician-informed and task-specific technical evidence for the prototype but do not establish patient usability or clinical effectiveness. Further technical validation, safety refinement, and supervised evaluation with people with KOA are required before routine or independent home use can be considered. Full article

Flexible flatfoot (pes planus) alters lower-limb biomechanics and plantar-pressure distribution, raising the risk of pain and injury. Laboratory gait analysis with optical motion capture and force plates is the reference standard but is costly, space-constrained, and ecologically limited. We present the design, fabrication, and validation of a low-cost, sustainable smart insole for Internet-of-Things (IoT) remote body-load monitoring. The device pairs a dual-layer natural-rubber body—a silica-filled sponge–rubber upper for comfort and a carbon-black-reinforced solid outsole for durability—with four load cells per insole at high-pressure plantar landmarks, read through a 24-bit amplifier by an ESP32 that calibrates and streams left/right load over Wi-Fi to the ThingSpeak cloud, with a wrist-worn OLED for real-time feedback. Against reference weights in 25 participants, the system measured total body weight with a mean absolute error of 2.94%, a maximum error of 4.18%, and an RMSE of 1.94 kg (Pearson r = 0.99); the residual was an almost purely systematic proportional bias (slope 0.966, R² = 0.98) removable by a single in-sample scalar recalibration. In 30 adults (15 normal-arch; 15 flexible flatfoot), spatiotemporal gait parameters were compared while both groups wore the smart insole. Forward-progression parameters, including step length, stride length, and walking velocity, did not differ significantly between groups during comfortable walking (all p > 0.18). The flatfoot group showed a wider mediolateral base—greater stance width during standing (+14%, p = 0.008) and step width during comfortable walking (+23%, p = 0.040, uncorrected). After correction for multiple comparisons, only the reduction in fast-walking cadence remained statistically significant. A sustainably sourced, affordable smart insole can thus deliver clinically meaningful remote body-load monitoring. The findings also point to a dissociation: forward propulsion was comparable between the groups while the insole was worn, whereas the mediolateral base remained wider in flatfoot. Controlled trials pairing orthotic support with active gait retraining are therefore warranted. Full article

Vascular smooth muscle cells (VSMCs) exhibit remarkable phenotypic plasticity, dynamically transitioning from a quiescent contractile state to a dedifferentiated synthetic phenotype that constitutes the fundamental cytological driver of pathological vascular remodeling in cardiovascular diseases (CVDs) including atherosclerosis, vascular restenosis, aortic dissection (AD), and vascular calcification (VC). Signal transducer and activator of transcription 3 (STAT3) operates as a master transcriptional and functional convergence node, integrating diverse upstream biochemical stimuli, neurohumoral factors, and biomechanical stressors to govern downstream gene regulatory networks. Aberrant STAT3 activation orchestrates VSMC phenotypic modulation, excessive proliferation, directional migration, programmed cell death involving apoptosis resistance and pyroptosis initiation, extracellular matrix (ECM) reorganization, and glycolytic metabolic reprogramming via canonical nuclear transcription and non-canonical subcellular actions. Here, we systematically delineate the modular structural organization, post-translational modifications, and negative regulatory feedback mechanisms of STAT3 in shaping VSMC functionality. Furthermore, we synthesize recent advances in pharmacological interventions by comprehensively categorizing therapeutic modalities into direct structural-domain inhibitors, upstream kinase-targeted indirect agents, bioactive natural products, and emerging clinical-stage translational candidates. Finally, we critically address translational hurdles regarding on-target systemic toxicities, and highlight site-specific vascular delivery systems alongside localized drug-eluting vascular devices to advance precision STAT3-targeted cardiovascular therapeutics. Full article

The treatment of articular cartilage defects remains a significant clinical challenge due to the tissue’s limited intrinsic repair capacity. This paper presents a review of clinical experiences with the use of Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs) as a novel therapeutic option for cartilage regeneration. WJ-MSCs offer key advantages, including high proliferative potential, strong immunomodulatory properties, and low immunogenicity, making them suitable for allogeneic applications. This review describes a single-step, dry-arthroscopic technique that was employed for the implantation of WJ-MSCs embedded in a scaffold directly into cartilage defects. Clinical follow-up, supported by MRI evaluation, demonstrated favorable outcomes with evidence of defect filling, improved cartilage surface quality, and sustained functional improvement in patients. These results suggest that WJ-MSC-based therapies, delivered through minimally invasive surgical techniques, represent a safe and effective strategy for cartilage repair, with the potential to become an important alternative to current standard treatments. Recent advances in artificial intelligence (AI) and multimodal bioprinting are opening new perspectives for standardizing regenerative therapies. Machine learning models can predict bioink performance, optimize scaffold design, and integrate real-time imaging feedback such as optical coherence tomography and photoacoustic imaging. These approaches allow closed-loop quality control and the creation of digital twins to ensure biomechanical fidelity of constructs. Incorporating AI-assisted bioprinting with Wharton’s jelly MSCs could accelerate the translation of laboratory findings into reproducible, patient-specific cartilage implants. This manuscript is structured as a translational review of WJ-MSC-based cartilage repair, with AI-assisted bioprinting presented as a prospective future manufacturing direction rather than current clinical practice. Full article

Although additive manufacturing enables cost-effective surgical training models, variation in materials and printing parameters limits standardization. This study combined biomechanical testing and user evaluation to identify fused deposition modeling (FDM) settings for osteotomy simulation. Porcine ribs and three-dimensional (3D)-printed models were compared using three-point bending tests. Subsequently, 32 participants performed osteotomies using an ultrasonic device, a Lindemann bur, and a reciprocating saw. Haptic feedback was assessed by questionnaire, while surface artifacts were evaluated by two blinded evaluators. With 25% gyroid infill and two outer layers, maximum bending forces ranged from 322.9 to 552.1 N across the printed materials, compared with 604.9 N for the porcine ribs, with polylactic acid (PLA) showing the closest approximation. Polycarbonate (PC) achieved the highest haptic rating (6.7 ± 1.8), followed by acrylonitrile styrene acrylate (ASA), polyethylene terephthalate glycol (PETG), and PLA, with a significant difference between PC and PLA (p = 0.029). The reciprocating saw received the highest ratings across all criteria. Surface artifacts differed between materials (p < 0.001), with PETG and PLA showing more melting and fraying, and ASA and PC more stringing. No significant differences were observed between students and doctors. Overall, PLA most closely approximated the reference maximum bending force, whereas PC and ASA achieved the highest perceived tactile realism. Full article

The knee is a mechanically demanding synovial organ in which joint loading, tissue deformation, cellular mechanotransduction and matrix turnover are coupled. This narrative review critically links those scales and asks where the evidence is sufficiently mature for mechanistic or clinical inference. PubMed/MEDLINE and Europe PMC were searched from database inception to 20 July 2026 using structured terms for knee biomechanics, cartilage and osteochondral mechanobiology, finite element modelling, mechanosensitive channels, osteoarthritis, machine learning and digital twins. Landmark studies were selected for foundational models, while recent studies were prioritised for causal experiments, validation and translation. Instrumented implants show that common activities generate tibiofemoral forces of several times body weight, but tissue-level exposure also depends on muscle co-contraction, geometry and material properties. Biphasic and fibril-reinforced models explain how those loads become stress, strain, fluid pressure and osmotic signals. At the cell scale, TRPV4 and PIEZO1/2 participate in overlapping, context-dependent calcium signalling rather than a universal protective–pathological binary; most causal evidence remains preclinical. Osteoarthritis is therefore framed as a mechanically amplified feedback process involving cartilage, bone, synovium and systemic modifiers. Computational degeneration models and machine-learning surrogates are increasingly informative, although prospective validation, parameter identifiability and uncertainty propagation remain limiting. The review’s added value is an explicit transmission-and-validation framework that connects whole-joint observables to molecular responses while labelling the evidence source and translational readiness at every step. Full article

Portable electromyography biofeedback (EMG-BFB) may support gait rehabilitation, but whole-gait responses across increasing portable auditory feedback goals are unclear. Twenty-four adults completed baseline treadmill walking and four counterbalanced right medial gastrocnemius activation-goal conditions set at 20%, 40%, 60%, and 80% above baseline; 23 contributed primary biomechanical data. Treadmill speed was adjusted within each condition to support achievement of the activation goal. Outcomes included walking speed, ground-reaction forces, force-time metrics, step length, temporal measures, and asymmetry. Repeated-measures MANOVA, outcome-specific repeated-measures ANOVAs, dose-response coefficients, bootstrap intervals, and leave-one-participant-out analyses were used. The combined gait-biomechanics outcomes differed significantly across activation-goal conditions (p < 0.001), with large condition effects for walking speed, propulsion, braking magnitude, and step length. From baseline to the highest goal, treadmill speed increased from 1.07 to 1.44 m/s, mean propulsion by 0.086 N/BW, braking magnitude by 0.109 N/BW, and mean step length by 0.150 m. Exploratory speed-adjusted models retained anterior–posterior and vertical loading-response associations but not peak propulsion; activation goal and achieved speed were strongly collinear. Unilateral feedback was not associated with systematic step-length or step-time asymmetry. Portable auditory EMG-BFB at increasing activation goals was accompanied by coordinated changes across gait mechanics. Full article

This review paper systematically investigates the surface modification technology of wooden table tennis blades, with a particular focus on the inherent conflict between coating-induced protection and the preservation of acoustic performance—a critical yet underexplored aspect of blade design. While protective coatings are essential for enhancing durability against moisture, wear, and impact, they inevitably alter the blade’s vibrational characteristics and acoustic feedback, compromising the tactile–auditory perception that elite players rely upon. The current literature predominantly treats protection and acoustics as separate design objectives, lacking an integrated framework to resolve their inherent trade-off. To address this gap, this review establishes a material–structure–function integrated design paradigm that elucidates the synergistic optimization of coating protection and acoustic response. We systematically analyze the regulatory mechanisms of key coating parameters—specifically elastic modulus, density, and damping coefficient—on blade vibration modes and impact sound characteristics, demonstrating that conventional singular optimization inevitably leads to undesirable frequency shifts and diminished tactile feedback. Our synthesis of materials science, acoustic analysis, and biomechanics reveals that the key to synergy lies in constructing a mechanical impedance-matching transition system through material selection and thickness gradient design. Notably, we show that a multi-layer gradient coating architecture, guided by finite element simulation, can enhance protective performance by 40% while restricting acoustic deviation to within 5%, validating a rational “design–simulation–verification” closed-loop methodology. Furthermore, this review identifies critical research frontiers, including smart adaptive coatings and sustainable bio-based materials, and proposes a multi-objective optimization framework to bridge the gap between laboratory innovation and manufacturable, high-performance sporting equipment. This work provides a foundational theoretical roadmap for the next-generation design of competition-grade table tennis blades, balancing durability with the nuanced sensory demands of elite athletes. Full article

Robot-assisted surgery has significantly expanded the possibilities of minimally invasive procedures by providing enhanced dexterity and visualization. However, the lack of direct haptic feedback still limits the surgeon’s ability to accurately assess instrument–tissue interactions, motivating the need for automatic intraoperative assistance systems. During vesicourethral anastomosis (VUA) in robot-assisted radical prostatectomy (RARP), accurate engagement of the bladder and urethral mucosa is essential to ensure proper tissue approximation and watertight closure. Nevertheless, automatic identification of fine-grained needle–tissue interactions during this phase remains largely unexplored. In this work, we propose a proof-of-concept framework for vision-based needle–tissue interaction analysis in RARP endoscopic videos, combining semantic segmentation, geometric proximity analysis, and motion coherence estimation to identify biomechanically plausible interaction events. Two independent transformer-based models were fine-tuned for semantic segmentation of the mucosal tissue and the surgical needle using a patient-level split of six real-world RARP procedures, comprising four procedures for training, one for validation, and one for independent testing. The models achieved Dice scores of 0.837 and 0.774, respectively. The segmentation outputs were subsequently used to drive a motion-aware interaction analysis pipeline, combining geometric proximity estimation between the needle endpoint and the mucosal tissue with optical-flow motion coherence analysis. The proposed interaction framework was evaluated on an independent test set, achieving a specificity of 0.933 and a recall of 0.667. An ablation study further demonstrated the complementary contribution of geometric proximity and motion coherence cues for needle–tissue interaction detection. Although limited by the retrospective nature and size of the dataset, this study introduces a low-latency, end-to-end framework for interaction-aware surgical scene understanding during RARP. The proposed approach represents an initial step toward the development of context-aware intraoperative guidance systems for robotic urologic surgery. Full article

This study investigated the biomechanical changes associated with different levels of walker dependence during walker-assisted gait in older adults. Ten community-dwelling older adults participated in this cross-sectional study and performed 10-m walking trials under two walker-dependence conditions: average walker dependence (AWD) and half-average walker dependence (HAWD). Walker dependence was quantified using a weight-feedback walker. In addition, ground reaction forces (GRFs), joint angular velocities, and gait parameters were assessed using a three-dimensional motion analysis system and force plates. Lower walker dependence was associated with greater GRFs (except propulsion force), increased joint angular velocities (except the hip), faster walking velocity and cadence, and shorter stride, stance, and swing times, without significant changes in spatial gait parameters. These findings suggest that the observed biomechanical responses were associated with differences in walker dependence but should be interpreted with caution because walking velocity and the characteristic whole-body biomechanics of walker-assisted gait may also have influenced these responses. Quantitative regulation of walker dependence may influence biomechanical responses during walker-assisted gait. Full article

This article discusses advanced artificial intelligence (AI)-based strategies for the design and personalization of three-dimensionally (3D) fabricated hand exoskeletons, with a focus on adaptive, data-driven methodologies. It highlights the crucial role of intelligent personalization in improving user comfort, functional performance, and rehabilitation outcomes, particularly in medical and care settings. The proposed approach integrates biomechanical modeling, high-resolution 3D scanning, and machine learning (ML) algorithms to create exoskeleton systems tailored to the unique anatomical and motor characteristics of individual users. This article presents both a theoretical framework and practical implementation of AI-based adaptation, addressing key challenges such as precise anatomical fit, ergonomic optimization, and real-time responsiveness. Specific emphasis is placed on AI-based feedback mechanisms that enable continuous, dynamic adjustment of control parameters during device operation. Case studies illustrate the effectiveness of these techniques in improving performance and rehabilitation progress for individual users. By combining intelligent modeling, adaptive control, and additive manufacturing, this research advances the field of wearable robotics and points the way to more accessible, efficient, and fully personalized assistive technologies. Full article

Human gait analysis is a key component of rehabilitation medicine, enabling objective assessment of patient recovery. In crutch-assisted locomotion, however, conventional forearm crutches operate as passive devices, providing no quantitative information on load distribution or patient adherence to partial weight-bearing (PWB) prescriptions. This work presents the design and dynamic validation of a sensorized forearm crutch system for biomechanical monitoring during assisted gait. The proposed device combines a force-sensing module based on a full Wheatstone bridge strain-gauge configuration with a 6-axis inertial measurement unit (IMU) to capture both axial load and crutch orientation. Sensor fusion was implemented through a complementary filter to estimate pitch and roll angles under dynamic conditions. The system was calibrated through static loading procedures and validated against reference instrumentation, including an optoelectronic motion capture system and an instrumented dual-belt treadmill with force platforms. Unlike previous studies relying on stationary force platforms that capture discrete steps and may alter natural gait, this validation approach enabled continuous, stride-by-stride force and orientation measurements without restricting foot placement. Experimental trials were conducted with unimpaired participants performing assisted gait using 2-point and 3-point patterns at two partial weight-bearing levels (20% and 40% body weight) and two walking speeds (0.80 m/s and 1.20 m/s). Dynamic validation showed good agreement with the treadmill reference, with force RMSE values of $9.33\pm 1.70$ N for the left crutch and $12.90\pm 2.85$ N for the right crutch, and with coefficients of determination of $R^2=0.9956$ and $R^2=0.9927$, respectively. Orientation RMSE values were $1.08\pm 0.44$° (roll, right), $2.06\pm 0.56$° (roll, left), $1.79\pm 0.55$° (pitch, right), and $1.66\pm 0.37$° (pitch, left). Beyond validation accuracy, the system enabled extraction of a set of quantitative biomechanical descriptors directly from crutch signals, axial load, cadence, crutch contact variability, load asymmetry, pitch asymmetry, and crutch stance/swing asymmetries, characterizing walking stability, bilateral coordination, and gait regularity during continuous assisted locomotion. These results demonstrate the feasibility of integrating force and inertial sensors into forearm crutches to enable quantitative monitoring of assisted gait, with potential applications in rehabilitation assessment and real-time feedback. Full article

This study aimed to develop a monocular vision-based tennis serve analysis system and evaluate its effectiveness in beginner training. The system uses MediaPipe Pose to extract 33 body landmarks from monocular video, calculates joint angles using three-dimensional vector operations, identifies serve phases through threshold-based rules, constructs an approximate 3D pose representation using anthropometric constraints, and generates corrective feedback through a rule-based expert system. In a quasi-experimental study, 90 beginner tennis players (final n = 82) completed an 8-week intervention and were allocated to a high-frequency feedback group, a moderate-frequency feedback group, or a conventional training group. All groups showed significant improvements in Serve Quality Mastery (SQM) scores (p < 0.001). The high-frequency feedback group showed the greatest SQM improvement (SQM: +30.5 points), followed by the moderate-frequency feedback group (+24.2 points) and the conventional training group (+15.5 points). Between-group differences were significant F(2, 79) = 74.30, p < 0.001, η² = 0.65. These findings indicate a graded pattern across the feedback-frequency groups, with more frequent system-generated feedback being associated with greater improvements in training performance. The findings support the potential use of the monocular pose-based, rule-driven feedback system as a supplementary tool for beginner tennis serve instruction. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

The evaluation of wearable robotic systems remains a challenge, particularly in real-world environments where laboratory-based methods are impractical. Existing approaches rely on external instrumentation, such as surface electromyography (sEMG) or motion capture, which are difficult to deploy continuously and do not directly measure key internal metrics such as joint loading or spinal forces. This work introduces a new paradigm for exoskeleton evaluation in which biomechanical assessment is embedded directly within the device’s sensing and computational architecture. We present the ExoMetrix system, a platform that integrates onboard sensing, real-time data acquisition, cloud-based processing, and user-facing analytics into a unified workflow for continuous evaluation of human–exoskeleton interaction. Sensor data from the device are streamed and processed using physics-based models. The resulting outputs are translated into estimates of internal biomechanical quantities, including joint torques, spinal compression and shear forces, and muscle loading. By enabling real-time feedback and longitudinal monitoring without external instrumentation, this approach transforms evaluation from an external, episodic process into an embedded and continuous capability, supporting safer and more scalable deployment of exoskeleton technologies. Full article