Search Results (204)

Gait impairment, postural instability, and freezing of gait are major contributors to mobility limitations in Parkinson’s disease (PD). Robot-assisted gait training (RAGT) has emerged as a promising rehabilitation strategy; however, its association with phase-specific lower-limb kinetic outcomes remains insufficiently characterized. This study compared Walkbot-based RAGT and conventional rehabilitation in terms of Walkbot-derived phase-specific lower-limb torque, functional balance, self-reported freezing-related gait impairment, and health-related quality of life in individuals with PD. This retrospective comparative study included 60 individuals with idiopathic PD (Hoehn and Yahr stages II–III) who received either Walkbot-based RAGT (n = 30) or conventional rehabilitation (n = 30). Both groups completed 30 supervised sessions over 6 weeks. Biomechanical outcomes comprised Walkbot-derived lower-limb torque during the stance and swing phases for the clinically more affected and contralateral limbs. Clinical outcomes included the Berg Balance Scale (BBS), Freezing of Gait Questionnaire (FOG-Q), and Parkinson’s Disease Questionnaire-39 (PDQ-39). Between-group post-treatment differences were evaluated using analysis of covariance (ANCOVA), adjusting each outcome for its corresponding baseline value. After baseline adjustment, post-treatment phase-specific lower-limb torque was higher in the RAGT group for the more affected limb during swing (adjusted difference, 0.057 Nm/kg; 95% CI, 0.019–0.096; p = 0.004) and stance (0.145 Nm/kg; 95% CI, 0.098–0.193; p < 0.001), and for the contralateral limb during swing (0.051 Nm/kg; 95% CI, 0.006–0.096; p = 0.027) and stance (0.205 Nm/kg; 95% CI, 0.088–0.321; p = 0.001). The RAGT group also had higher baseline-adjusted BBS scores (adjusted difference, 5.78 points; 95% CI, 3.86–7.69; p < 0.001) and lower FOG-Q scores (−0.94 points; 95% CI, −1.27 to −0.61; p < 0.001). No significant between-group difference was observed for PDQ-39 (0.77 points; 95% CI, −0.16 to 1.69; p = 0.102). Walkbot-based RAGT was associated with more favorable baseline-adjusted phase-specific lower-limb torque, functional balance, and self-reported freezing-related gait impairment than conventional rehabilitation, whereas disease-specific quality of life did not differ significantly between groups. These findings highlight the potential value of integrating Walkbot-derived phase-specific kinetic assessment with clinical outcomes to provide a more quantitative characterization of rehabilitation-related locomotor changes in PD. Prospective randomized studies are needed to confirm these associations and establish the clinical utility of Walkbot-derived phase-specific lower-limb torque as an outcome measure. Full article

Wearable technologies are reshaping gait rehabilitation by shifting assessment and therapy from intermittent, clinic-based observation to continuous, data-driven, and adaptive care. This narrative review synthesizes the biomechanical basis of gait instability, the architecture and classification of wearable rehabilitation systems, and the clinical evidence for motion sensors, smart insoles, biofeedback devices, robotic and orthotic wearables, neuromodulatory systems, immersive platforms, and artificial intelligence (AI)-enabled closed-loop interventions. Its main contribution is an integrated framework that links AI, digital biomarkers, device components, adaptive control, and translational implementation, rather than treating wearable rehabilitation as a device-only or disease-specific topic. Current evidence indicates that these technologies can improve gait speed, symmetry, balance, endurance, fall-risk monitoring, and dual-task performance in neurological, musculoskeletal, frailty-related, and aging populations. However, the field is still limited by heterogeneous protocols, small samples, limited longitudinal validation, insufficient device standardization, usability barriers, cybersecurity concerns, uncertain reimbursement, and restricted interoperability with healthcare systems. Future progress will depend on multimodal sensor fusion, explainable and federated AI, digital twins, adaptive wearable robotics, tele-rehabilitation pathways, and large-scale pragmatic trials that validate effectiveness in real-world rehabilitation settings. Full article

Wearable inertial measurement units (IMUs) offer an accessible alternative to optical motion capture (MoCap) gait analysis, but their performance in Progressive Supranuclear Palsy (PSP) requires validation. We assessed the concurrent validity of IMU-derived versus MoCap-derived gait metrics and static postural sway in 30 patients with PSP using Bland–Altman analysis, Intraclass Correlation Coefficients (ICC), and Spearman rank correlations. Finally, we assessed equivalence using the Two one-sided tests (TOST) procedure. Multivariable linear regression was used to determine whether clinical severity, as measured by the PSP Rating Scale (PSPRS), independently predicted absolute IMU measurement error while controlling for patient age and gait velocity. IMUs demonstrated excellent between-system agreement for parameters such as cadence (100.76 ± 11.42 vs. 100.52 ± 11.59) and cycle time (1.21 ± 0.15 vs. 1.22 ± 0.15; ICC > 0.98), despite a systematic underestimation of gait velocity (p < 0.05). Agreement significantly diminished for micro-phases (e.g., single/double support times) and spatial asymmetry. Interestingly, the TOST procedure revealed that only sagittal and transverse trunk kinematics were equivalent between systems, with all other measures failing to find equivalency. For static sway, the IMU demonstrated strong rank-order correspondence for tracking relative postural instability (ρ = 0.82, p < 0.05). Multivariable analysis revealed that higher PSPRS scores are independently associated with greater between-system discrepancies in support phases and pelvic and trunk kinematics (p < 0.05), irrespective of reduced gait speed. These findings highlight the need to develop disease-specific algorithms, rather than relying on normative commercial models, to establish reliable digital biomarkers for monitoring progressive motor decline. Full article

Background/Objectives: Wolfram syndrome is an ultra-rare, progressive multisystem disorder in which endocrine and sensory manifestations coexist with neurological involvement. Quantitative magnetic resonance imaging (MRI) may help characterize central nervous system involvement in this condition; however, evidence derived from small imaging cohorts requires cautious interpretation. This study aimed to examine the relationships between different MRI-derived attributes and neurological symptoms in Wolfram syndrome, with the goal of identifying exploratory imaging patterns that may suggest the involvement of specific neural systems. Methods: We analyzed a Spanish cohort of 45 genetically confirmed patients with Wolfram syndrome. A homogeneous subset of 15 patients with standardized 3-Tesla multimodal MRI and adequate image quality was included in the quantitative imaging analysis. T1-weighted MRI, T2-weighted/fluid-attenuated inversion recovery (FLAIR) imaging, and diffusion tensor imaging (DTI) were processed using a standardized workflow for brain extraction, anatomical segmentation, cortical reconstruction, and quantitative feature extraction. A total of 172 MRI-derived features were examined in relation to neurological phenotypes, including dysphagia, ataxia, gait instability, and cognitive impairment. Analyses included principal component analysis, exploratory factor analysis, correlation analyses, and symptom-specific group comparisons. Given the small MRI sample size and the high feature-to-subject ratio, all analyses were considered exploratory and hypothesis-generating, and the findings should be interpreted cautiously pending validation in larger, independent cohorts. Results: Multimodal MRI-derived features showed distributed associations with neurological manifestations. The most recurrent exploratory imaging correlates involved the thalamus, lateral geniculate nucleus, cerebellum, brainstem, ventricular system, corpus callosum, posterior cortical regions, and white-matter pathways. FLAIR-derived signal heterogeneity in the thalamus and lateral geniculate nucleus appeared repeatedly across several clinical manifestations. Dysphagia was associated with a distributed pattern involving cortical thinning, thalamic and brainstem volume reduction, reduced cerebellar white-matter integrity, increased FLAIR heterogeneity, and ventricular enlargement. Ataxia and gait instability showed overlapping but partially distinct imaging profiles, whereas cognitive impairment was associated with broader cortical, subcortical, callosal, cerebellar, and ventricular alterations. Conclusions: In this exploratory pilot study, multimodal MRI-derived features showed clinically plausible associations with neurological manifestations in Wolfram syndrome. The findings support a distributed model of neurological involvement affecting cerebello-thalamo-cortical circuits, visual relay structures, brainstem pathways, and long-range white-matter connections. These results should be interpreted as exploratory MRI-derived attributes rather than as validated biomarkers, prognostic indicators, or clinically applicable imaging signatures. Confirmation in future longitudinal, multicenter studies with harmonized imaging protocols and external validation will be required. Full article

Many people with Parkinson’s disease (PwPD) lack optimal care due to limited access to neurologists and a reliance on subjective rating scales for treatment decisions. The Ceraxis Insight platform was designed to provide quantitative measures of the cardinal motor symptoms of Parkinson’s disease (PD) through self-administered assessments performed using a tablet paired with a sensor-embedded stylus. The aim of this study was to assess the validity of the Ceraxis Insight outcome metrics against clinical gold-standard measures of PD motor symptoms. Nineteen PwPD completed a clinical examination and the nine Ceraxis Insight assessment modules. Quantitative performance metrics were calculated from the platform’s IMU, force transducer, and touchscreen inputs. Mixed-effect models and correlation analyses determined that multiple quantitative metrics from the Ceraxis Insight modules significantly predicted (p < 0.05) and were significantly correlated (correlation coefficients > 0.70) with the MDS-UPDRS III total score, bradykinesia, tremor, rigidity, and postural instability and gait difficulty sub-scores. Logistic regression models determined that multiple Ceraxis Insight metrics discriminated between ON- and OFF-deep brain stimulation (DBS) conditions, with Area Under the Receiver Operating Characteristic Curve (AUC) values exceeding 0.70. The Ceraxis Insight platform provides a validated, objective assessment of PD motor symptoms that may be performed within clinical or remote settings for data-driven evaluation and treatment. Full article

Background: Artificial intelligence (AI)-driven gait analysis using plantar pressure and ground reaction force (GRF) signals may provide objective digital biomarkers for rehabilitation, but clinical translation remains uncertain. This scoping review and evidence map aimed to summarize clinical applications, compare evidence maturity, and identify methodological and translational gaps. Methods: PubMed, Web of Science, Embase, and Scopus were searched from the earliest available indexed records in each database to May 2026. Original clinical studies using plantar pressure- or GRF-derived signals with AI methods for disease recognition, severity assessment, risk prediction, rehabilitation monitoring, or decision support were included. Results: Fifteen studies met the eligibility criteria. Evidence was concentrated in Parkinson’s disease (PD), particularly PD recognition and freezing of gait prediction, where relatively more mature evidence was supported by multiple studies and participant-level or cross-dataset validation. Evidence for PD severity assessment, knee osteoarthritis monitoring, chronic ankle instability rehabilitation, fall-risk stratification, sarcopenia screening, peripheral artery disease recognition, and functional gait disorder classification remained less mature. Translation was limited by small or single-center samples, unclear participant-level data splitting, limited external validation, absent calibration, sparse explainable AI reporting, and insufficient real-world workflow testing. Conclusions: Future studies should prioritize prospective, externally validated, interpretable, calibrated, and clinically embedded models before routine rehabilitation implementation. Full article

Background: Knee pain, proprioceptive deficits, and muscle weakness brought about by articular degeneration in knee osteoarthritis (OA) can cause postural instability and gait asymmetry. Arm swing plays a major role in balance control during human walking. However, few studies have examined the arm movement and gait stability in knee OA. Therefore, the purpose of this study is to investigate the gait asymmetry in upper and lower limb movement and to understand the overall movement pattern during walking in people with knee osteoarthritis. Methods: Thirty-five people with knee OA and twenty-four age-matched controls were enrolled in this study. The arm swing amplitude, gait biomechanics, and spatiotemporal parameters during walking were measured using a Vicon motion capture system incorporating two AMTI force plates. The differences between the knee OA and control groups were analyzed using independent t-tests. Results: The knee OA patients walked with a slower, smaller step and lower arm swing amplitude. The asymmetries of step time, foot progression angle, and total arm swing amplitude were significantly greater in the knee OA group than in the controls (p < 0.05). There was no significant difference in the mean value of step width and the foot progression angle and the asymmetry of step length and the knee adduction moment between the groups. Conclusions: This study demonstrates that the pathological changes in the lower limbs in knee OA are intrinsically reflected in the upper limbs through diagonal coordination. Asymmetry of arm swing and foot progression angle may be key for understanding OA-related instability. Full article

Background/Objectives: Adult Polyglucosan Body Disease (APBD) is a rare neurodegenerative glycogen storage disorder characterized by progressive gait disturbance, sensory impairment, and balance dysfunction. Although rehabilitation is recommended for functional maintenance, evidence regarding robot-assisted gait training (RAGT) in APBD remains extremely limited. Methods: A 58-year-old man with progressive lower extremity sensory and motor symptoms was diagnosed with APBD in 2026. Neurological examination revealed severe proprioceptive impairment in both great toes, generalized sensory deficits, gait instability, and impaired balance. Functional assessment demonstrated mild balance impairment with generally preserved muscle strength except for mild weakness in the lower extremities. RAGT was initiated and performed for 19 sessions over approximately 6 weeks in combination with conventional rehabilitation therapy, including gait and balance training with visual feedback. Results: Following the combined rehabilitation program, improvements were observed in balance function, postural stability and proprioceptive function. Conclusions: This case suggests that RAGT combined with conventional rehabilitation may improve balance and gait-related function in patients with APBD. Repetitive task-specific gait training with enhanced sensory feedback may be particularly beneficial in APBD, where proprioceptive impairment and sensory ataxia are major contributors to gait dysfunction; however, this remains a hypothesis that requires validation in future studies. This report highlights the feasibility and potential applicability of RAGT in rare neurodegenerative disorders such as APBD. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Background: Spatial cognition, a skill paramount to survival, is impaired in Parkinson’s disease (PD) but has been little researched. Spatial cognition is utilized during motor–cognitive integration, which impacts daily functioning and quality of life in PD. As PD is a unilateral-onset condition, spatial–cognitive and motor–cognitive ability may differ by side of onset. Spatial cognition is suggested to be modulated by the right hemisphere; thus, we hypothesize to observe worse spatial and motor–cognitive performance by people with left-onset PD (LOPD) than right-onset PD (ROPD). Methods: 216 participants with PD were recruited (LOPD = 107; M = 62; mean age = 69.80 ± 8.5). Spatial outcomes were collected via the body position spatial task (BPST), Reverse Corsi Blocks, and visuospatial items of the Montreal Cognitive Assessment (MoCA); motor–cognitive outcomes were collected by a Trails test, a Four Square Step Test (FSST), and a Timed Up and Go test. An independent t-test and the Mann–Whitney U test compared outcome variables between onset groups. Results: No significant differences were found between onset groups. Exploratory subgroup analyses revealed differences. Significantly worse performance by LOPD in single- and dual-task TUG was found within people with bilateral symptoms and postural instability (Hoehn & Yahr stage, >2; LOPD, N = 33; single, p = 0.001; dual, p = 0.021) and worse performance in single-task TUG in people with MoCA < 18 (LOPD, N = 5; single, p = 0.036) and people with freezing of gait (FOGQ, >0; LOPD, N = 14, p = 0.048). Significantly larger DTC by LOPD was found within frequent freezers (FOGQ, >3; LOPD, N = 9; p = 0.003). Conclusions: LOPD may tend to perform worse in motor–cognitive tasks among subgroups of those with more severe symptoms, i.e., those at later stages of disease. These findings may have implications for prognoses of those with LOPD versus ROPD and suggest that those with LOPD may have worse long-term outcomes in spatial cognition and motor–cognitive integration. Full article

Background: Traditional gait assessments in adult degenerative scoliosis (ADS) often rely on prohibitively expensive, laboratory-bound optoelectronic systems that lack clinical accessibility. This research aims to independently evaluate both lower limbs using a wearable Inertial Measurement Unit (IMU) system, in contrast to studies that employ a unilateral reference, thereby elucidating the unique bilateral asymmetries and dynamic stability patterns exhibited in ADS. Methods: Gait patterns of 20 ADS patients and 15 healthy controls were analyzed using the Rokoko Smartsuit Pro. Segmental kinematic data were integrated with anthropometric mass distribution models to calculate the total body center of mass (CoM). Spatiotemporal parameters, joint range of motion (RoM), and CoM excursions in three planes were statistically compared between the groups. Results: ADS patients exhibited a cautious gait strategy characterized by significantly reduced step speed, shortened step lengths, and increased step width ($p<0.05$). Temporal analysis showed prolonged stride, stance, and double support time ($p<0.001$), while cadence remained comparable to healthy controls. A triple-joint deficit, including hip, knee, and ankle, was identified in the sagittal plane, especially with peak flexion reductions reaching up to 55% in the left knee and 38% in the right knee, highlighting profound functional asymmetry ($p<0.001$). Additionally, the CoM analysis reflected these stability restrictions, showing increased horizontal excursion and reduced vertical oscillation. Conclusions: Our findings suggest that ADS is associated with distinct, bilateral alterations in the lower limb kinematic chain and notable adaptations in dynamic balance parameters, characterized by a cautious gait strategy and profound sagittal triple-joint asymmetries. These findings highlight the feasibility of full-body wearable IMU technology in capturing objective, bilateral gait alterations, providing a foundational baseline that could complement standard static radiography in future clinical evaluations. Full article

Background: The superficial plantar-medial release (S-PMR) refers to a group of surgical procedures involving the release of the plantar aponeurosis and adjacent medial plantar soft tissues that are used in selected cases of plantar fasciitis and cavovarus foot deformity. The procedure aims to address pain and contracture of the plantar aponeurosis and intrinsic foot muscles, which may contribute to pathological foot alignment and gait instability. Due to the close proximity of highly variable neurovascular structures in the plantar region, precise anatomical knowledge and a patient-specific, personalized approach are essential to reduce the risk of iatrogenic injury during surgery. This study defined procedure-specific anatomical “low-risk” and “high-risk” zones. Methods: From the initial forty-two included feet, one specimen was excluded due to insufficient tissue quality, leaving forty-one specimens for analysis. The plantar aponeurosis, origins of the abductor hallucis muscle and regional neurovascular structures were analyzed. Distances between key landmarks were measured. Results: Abductor hallucis origins I and IV were present in all specimens, while origins II and III showed variable presence. Subdivision of muscle origin I was observed and was associated with the course of the medial calcaneal nerve. The medial calcaneal nerve demonstrated the closest proximity to origin I (3.2 mm) whereas both the medial and lateral plantar nerves showed close proximity to origin II (3 mm and 5.3 mm). Conclusion: Significant interindividual variability exists in the plantar region, highlighting the need for a personalized, anatomy-based approach for patients considered for surgical intervention. Anatomical “high-risk” zones were identified between origin I and the medial calcaneal nerve and near origin II by the bifurcation of the tibial nerve and posterior tibial artery. Anatomical “low-risk” zones were defined as dorsal regions at the calcaneus between origin I and the tibial neurovascular bundle, as well as medial areas near the malleolus. Full article

Background/Objectives: Frailty is a validated predictor of waitlist mortality and perioperative risk in liver transplant candidates, but its association with early post-transplant outcomes in large real-world cohorts remains incompletely characterized. This study evaluated the association between administratively defined pre-transplant frailty and early clinical outcomes following liver transplantation. Methods: We conducted a retrospective cohort study using the TriNetX US Collaborative Research Network. Adults undergoing first-time isolated liver transplantation through February 2026 were included. Frailty was identified using ICD-10-CM codes for frailty, sarcopenia, cachexia, weakness, abnormal gait/mobility, or reduced mobility documented within 12 months before transplantation; patients coded only for nonspecific weakness were excluded from the frailty cohort. Patients underwent 1:1 propensity score matching using 18 baseline covariates, including demographics, comorbidities, laboratory values, albumin, and MELD-Na. The primary outcome was all-cause mortality at 7, 30, and 90 days. Secondary outcomes included acute kidney injury, prolonged mechanical ventilation, vasopressor requirement/hemodynamic instability, renal replacement therapy, ICU and hospital length of stay, and 90-day readmission. Sensitivity analyses used a restrictive ≥ 2-code frailty definition and substituted MELD 3.0 for MELD-Na in the propensity model. Results: Among 4860 eligible recipients, 742 had administratively defined frailty and 4118 did not. After matching, 730 patients remained in each group with well-balanced covariates. Administratively defined frailty was associated with higher mortality at 7, 30, and 90 days, with numerically smaller relative risks at later time points. It was also associated with higher risks of acute kidney injury, prolonged mechanical ventilation, vasopressor requirement/hemodynamic instability, renal replacement therapy, longer ICU and hospital stays, and 90-day readmission. Findings were directionally consistent in both sensitivity analyses. Etiology-stratified analyses were exploratory and showed no statistically significant heterogeneity across liver disease etiologies. Conclusions: In this large propensity-matched multicenter cohort, administratively defined pre-transplant frailty was associated with worse early outcomes after liver transplantation. Because frailty and several outcomes were identified using structured EHR and administrative data, findings should be interpreted as associative and hypothesis-generating. Prospective studies using validated frailty instruments and granular donor, intraoperative, and center-level variables are needed to confirm these findings. Full article

WiFi sensing has gained widespread attention as a promising technology, owing to its non-intrusiveness, strong privacy-preserving characteristics, and cost-effective deployment, enabling diverse application scenarios. In addition, the stable spatial characteristics and symmetry-related patterns exhibited by human body postures in WiFi signal propagation provide new possibilities for robust person identification. In traditional WiFi-based person identification technologies, although gait recognition has achieved certain success, it is complex to operate and limited in application scenarios, increasing the constraints on recognition. This issue becomes more pronounced in large-scale user scenarios, where the system performance tends to degrade and exhibit instability. To overcome these challenges, we introduce a new person identification system called WiPID. The WiFi signals extracted from the static postures of users are treated as a “biometric fingerprint” for identity verification. An end-to-end deep learning framework is utilized by WiPID to process WiFi signals, and a convolutional autoencoder is adopted to preprocess the signals directly, effectively reducing redundant information and greatly simplifying the WiFi data processing. Furthermore, the integration of a multi-scale feature extraction module improves the system’s ability to capture discriminative features. The proposed system not only reduces operational complexity but also extends its applicability to a wider range of scenarios, thereby enhancing recognition performance. In an experiment involving 50 volunteers, WiPID achieved an average recognition accuracy of up to 98%, demonstrating the method’s suitability for large-scale person identification scenarios. In addition, a real-time identification experiment has been conducted on PCs and commercial WiFi devices. Experiments have proven that WiPID can achieve real-time person identification on Internet of Things devices, further validating its feasibility and stability in practical applications. Full article