Enzymatic denaturation versus excessive fatigue loading degeneration: Effects on the time-dependent response of the intervertebral disc.

Degenerative disc disease (DDD), regardless of its phenotype and clinical grade, is widely associated with low back pain (LBP), which remains the single leading cause of disability worldwide. This work provides a quantitative methodology for comparatively investigating artificial IVD degeneration via two popular approaches: enzymatic denaturation and fatigue loading. An in-vitro animal study was used to study the time-dependent responses of forty fresh juvenile porcine thoracic IVDs in conjunction with inverse and forward finite element (FE) simulations. The IVDs were dissected from 6-month-old-juvenile pigs and equally assigned to 5 groups (intact, denatured, low-level, medium-level, high-level fatigue loading). Upon preloading, a sinusoid cyclic load (Peak-to-peak/0.1-to-0.8 MPa) was applied (0.01-10 Hz), and dynamic-mechanical-analyses (DMA) was performed. The DMA outcomes were integrated with a robust meta-model analysis to quantify the poroelastic IVD characteristics, while specimen-specific FE models were developed to study the detailed responses. The results demonstrated that enzymatic denaturation had a more significantly pronounced effect on the resistive strength and shock attenuation capabilities of the intervertebral discs. This can be attributed to the simultaneous disruption of the collagen fibers and water-proteoglycan bonds induced by trypsin digestion. Fatigue loading, on the other hand, primarily influenced the disc's resistance to deformation in a frequency-dependent pattern, where alterations were most noticeable at low loading frequencies. This study confirms the intricate interplay between the biochemical changes induced by enzymatic processes and the mechanical behavior stemming from fatigue loading, suggesting the need for a comprehensive approach to closely mimic the interrelated multifaceted processes of human disc degeneration.

Impact of osteoporosis and Cement-Augmented fusion on adjacent spinal levels Post-Fusion Surgery: Patient-Specific finite element analysis.

Cement-augmentation is a technique commonly used during posterior lumbar instrumented fusion (PLIF) to reinforce compromised osteoporotic vertebral bone, minimize the risk of loosening screws, enhance stability, and improve overall surgical outcomes. In this study, we introduce a novel segmented vertebral body regional modeling approach to investigate the effects of osteoporosis and cement-augmented lumbar fusion on disc biomechanics at spinal levels adjacent to the fused vertebrae. Using our previously validated personalized-poroelastic-osteoligamentous FE model of the spine, fusion was simulated at L4-L5, and the biomechanics of adjacent levels were studied for 30 patients (non-osteoporotic patients (N = 15), osteoporotic patients (N = 15)). PLIF models, with and without cement-augmentation, were developed and compared after an 8 h-rest period (200 N), following a 16 h-cyclic compressive loading of 500-1000 N (40 and 20 min, respectively). Movement in different directions (flexion/ extension/ lateral bending/ axial rotation) was simulated using 10Nm moment before and after cyclic loading. The material mapping algorithm was validated by comparing the results of voxel-based and parametric models. The FE cement-augmented models, subject to daily activity loading, demonstrated significant differences in disc height loss and fluid loss as compared to non-cemented models. The calculated axial stress and fiber strain values were also significantly higher for these models. This work demonstrates that although osteoporosis does not significantly alter the time-dependent characteristics of adjacent IVDs post-surgery, cement-augmentation increases the risk of adjacent segment disease (ASD) incidence. A holistic understanding of the trade-offs and long-term complex interplay between structural reinforcement modalities, including cement augmentation, and altered biomechanics warrants further investigation.

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

Muscle-inspired bi-planar cable routing: a novel framework for designing cable driven lower limb rehabilitation exoskeletons (C-LREX).

There is a growing interest in the research and development of Cable Driven Rehabilitation Devices (CDRDs) due to multiple inherent features attractive to clinical applications, including low inertia, lightweight, high payload-to-weight ratio, large workspace, and modular design. However, previous CDRDs have mainly focused on modifying motor impairment in the sagittal plane, despite the fact that neurological disorders, such as stroke, often involve postural control and gait impairment in multiple planes. To address this gap, this work introduces a novel framework for designing a cable-driven lower limb rehabilitation exoskeleton which can assist with bi-planar impaired posture and gait. The framework used a lower limb model to analyze different cable routings inspired by human muscle architecture and attachment schemes to identify optimal routing and associated parameters. The selected cable routings were safeguarded for non-interference with the human body while generating bi-directional joint moments. The subsequent optimal cable routing model was then implemented in simulations of tracking reference healthy trajectory with bi-planar impaired gait (both in the sagittal and frontal planes). The results showed that controlling joints independently via cables yielded better performance compared to dependent control. Routing long cables through intermediate hinges reduced the peak tensions in the cables, however, at a cost of induced additional joint forces. Overall, this study provides a systematic and quantitative in silico approach, featured with accessible graphical user interface (GUI), for designing subject-specific, safe, and effective lower limb cable-driven exoskeletons for rehabilitation with options for multi-planar personalized impairment-specific features.

Time-delay estimation in biomechanical stability: a scoping review.

Despite its high-level of robustness and versatility, the human sensorimotor control system regularly encounters and manages various noises, non-linearities, uncertainties, redundancies, and delays. These delays, which are critical to biomechanical stability, occur in various parts of the system and include sensory, signal transmission, CNS processing, as well as muscle activation delays. Despite the relevance of accurate estimation and prediction of the various time delays, the current literature reflects major discrepancy with regards to existing prediction and estimation methods. This scoping review was conducted with the aim of characterizing and categorizing various approaches for estimation of physiological time delays based on PRISMA guidelines. Five data bases (EMBASE, PubMed, Scopus, IEEE and Web of Science) were consulted between the years of 2000 and 2022, with a combination of four related categories of keywords. Scientific articles estimating at least one physiological time delay, experimentally or through simulations, were included. Eventually, 46 articles were identified and analyzed with 20 quantification and 16 qualification questions by two separate reviewers. Overall, the reviewed studies, experimental and analytical, employing both linear and non-linear models, reflected heterogeneity in the definition of time delay and demonstrated high variability in experimental protocols as well as the estimation of delay values. Most of the summarized articles were classified in the high-quality category, where multiple sound analytical approaches, including optimization, regression, Kalman filter and neural network in time domain or frequency domain were used. Importantly, more than 50% of the reviewed articles did not clearly define the nature of the estimated delays. This review presents and summarizes these issues and calls for a standardization of future scientific works for estimation of physiological time-delay.

Severity of vehicle-to-vehicle accidents in the UAE: An exploratory analysis using machine learning algorithms.

The World Health Organization (WHO) identifies road traffic injuries as a global health problem. The Eastern-Mediterranean region is particularly suffering from low traffic safety levels, recording the third highest death per capita ratio in the world. It is critical to evaluate and understand the causes of crashes and their severity levels as a first step to devising policies that aim to reduce these causes. Previous studies examining the frequency or severity of crashes present important limitations that motivate the need for the current work. While these studies have investigated the relation of contributing factors to severity of crashes, not until recently the importance of these factors are bring investigated. Even then, less research have explored various Machine Learning models and none in the middle-eastern region. This is critical because the WHO report concludes that the chances of dying in a traffic crash in this region are second only to Africa per 100000 population. This is a first study analyzing the severity of vehicle-to-vehicle crashes among drivers in the United Arab Emirates. Traffic Crash Data was obtained from the Abu Dhabi Police, which consisted of 11,400 observations during the period 2014-2017. Machine learning algorithms, including gradient boosting (GB), support vector machines (SVM), and random forest (RF), were trained and tested to predict crash severity and extract (using feature analysis) its determinants. The models were evaluated using two performance metrics: prediction accuracy and F1-scores. The RF model outperformed both GB and SVM, with the confusion matrix of RF reporting a better prediction for all four crash severity classes. The feature importance analysis indicates that the age of car, age of the injured, and the age of the initiator have the highest effect on severity, which is an important finding as the listed factors were rarely considered in previous studies. Vehicle and road characteristics such as vehicle class, crash type, and lighting are slightly associated with the severity. Consistent with other studies, gender was the least essential predictor of severity. Recommendations are finally provided to the Abu Dhabi Department of Municipalities and Transport (AD-DMT) authority to guide the development of road safety policies and countermeasures to mitigate the occurrence and severity of crashes.

A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI.

BACKGROUND: Neurological diseases are a leading cause of disability and mortality. Gait, or human walking, is a significant predictor of quality of life, morbidity, and mortality. Gait patterns and other kinematic, kinetic, and balance gait features are accurate and powerful diagnostic and prognostic tools. OBJECTIVE: This review article focuses on the applicability of gait analysis using fusion techniques and artificial intelligence (AI) models. The aim is to examine the significance of mixing several types of wearable and non-wearable sensor data and the impact of this combination on the performance of AI models. METHOD: In this systematic review, 66 studies using more than two modalities to record and analyze gait were identified. 40 studies incorporated multiple gait analysis modalities without the use of artificial intelligence to extract gait features such as kinematic, kinetic, margin of stability, temporal, and spatial gait parameters, as well as cerebral activity. Similarly, 26 studies analyzed gait data using multimodal fusion sensors and AI algorithms. RESULTS: The research summarized here demonstrates that the quality of gait analysis and the effectiveness of AI models can both benefit from the integration of data from many sensors. Meanwhile, the utilization of EMG signals in fusion data is especially advantageous. CONCLUSION: The findings of this review suggest that a smart, portable, wearable-based gait and balance assessment system can be developed using multimodal sensing of the most cutting-edge, clinically relevant tools and technology available. The information presented in this article may serve as a vital springboard for such development.

Biomechanical Investigation of Bone Screw Head Design for Extracting Stripped Screw Heads: Integration of Mechanical Tests and Finite Element Analyses.

Enhancing the design of bone screw head sockets to prevent stripping and improve the torque required for smooth unscrewing is a significant challenge in orthopedic applications. This research aims to establish a quantitative methodology by integrating mechanical testing with finite element (FE) simulations to determine a safe limitation depth for the screwdriver when engaging with the hexagonal socket, thus avoiding stripped screw heads. A FE model was developed to investigate the biomechanical responses of the screw head design. Five custom-made hexagonal sockets were manufactured, and single load torsional tests were conducted to assess the mechanical performance of the screws and drivers. The results from the mechanical tests were compared with the FE simulations, demonstrating a close agreement and confirming the model's validity. Furthermore, additional FE models were created to study the impact of manufacturing tolerances on the socket width and screwdriver width. The findings revealed that the maximum torque to failure for the four designs was lower than the margins specified in ISO 6475. Additionally, increasing the depth of the screwdriver led to higher maximum torque values. This research suggests that the technique of screw insertion, specifically the depth of the driver tool within the screw socket, holds greater importance in preventing stripped screw heads than the design and manufacturing width of the bone screw's hexagonal socket and screwdriver. This confirms the importance of screwdriver engagement inside the bone screw socket to prevent stripped screw heads and sheds light on the added value of maximum torque prediction for future design modifications.

Bi-Planar Trajectory Tracking with a Novel 3DOF Cable Driven Lower Limb Rehabilitation Exoskeleton (C-LREX).

Although Cable-driven rehabilitation devices (CDRDs) have several advantages over traditional link-driven devices, including their light weight, ease of reconfiguration, and remote actuation, the majority of existing lower-limb CDRDs are limited to rehabilitation in the sagittal plane. In this work, we proposed a novel three degrees of freedom (DOF) lower limb model which accommodates hip abduction/adduction motion in the frontal plane, as well as knee and hip flexion/extension in the sagittal plane. The proposed model was employed to investigate the feasibility of using bi-planar cable routing to track a bi-planar reference healthy trajectory. Various possible routings of four cable configurations were selected and studied with the 3DOF model. The optimal locations of the hip cuffs were determined using optimization. When compared with the five-cable routing configuration, the four-cable routing produced higher joint forces, which motivated the future study of other potential cable routing configurations and their ability to track bi-planar motion.

Distinction of non-specific low back pain patients with proprioceptive disorders from healthy individuals by linear discriminant analysis.

The central nervous system (CNS) dynamically employs a sophisticated weighting strategy of sensory input, including vision, vestibular and proprioception signals, towards attaining optimal postural control during different conditions. Non-specific low back pain (NSLBP) patients frequently demonstrate postural control deficiencies which are generally attributed to challenges in proprioceptive reweighting, where they often rely on an ankle strategy regardless of postural conditions. Such impairment could lead to potential loss of balance, increased risk of falling, and Low back pain recurrence. In this study, linear and non-linear indicators were extracted from center-of-pressure (COP) and trunk sagittal angle data based on 4 conditions of vibration positioning (vibration on the back, ankle, none or both), 2 surface conditions (foam or rigid), and 2 different groups (healthy and non-specific low back pain patients). Linear discriminant analysis (LDA) was performed on linear and non-linear indicators to identify the best sensory condition towards accurate distinction of non-specific low back pain patients from healthy controls. Two indicators: Phase Plane Portrait ML and Entropy ML with foam surface condition and both ankle and back vibration on, were able to completely differentiate the non-specific low back pain groups. The proposed methodology can help clinicians quantitatively assess the sensory status of non-specific low back pain patients at the initial phase of diagnosis and throughout treatment. Although the results demonstrated the potential effectiveness of our approach in Low back pain patient distinction, a larger and more diverse population is required for comprehensive validation.

Plantar pressure alterations associated with increased BMI in young adults.

BACKGROUND: Despite evidence suggesting that excess weight is linked to gait alterations and foot disorders, its effect on peak plantar pressure (PPP) variability and complexity during walking remains poorly understood. RESEARCH QUESTION: This study aimed to examine the influence of overweight (BMI ≥ 25) on the dynamic PPP distribution during gait using traditional and nonlinear dynamic measures in young college students. METHODS: Fifty-two overweight (BMI >25, average 29.3 ± 4.02) and sixty-four control college students (BMI<25, 21.7 ± 1.76) aged 18-25 years, walked across a Tekscan gait assessment system at their preferred speed. A t-test or a Mann Whitney U test was used for analysis, subject to data normality. Kinematic, kinetic, spatiotemporal, and GaitEn (sample entropy of 2D spatial PPP maps) for window lengths (m=2) at various filtering levels (r) were used to explore the impact of BMI on PPP alterations. RESULTS AND SIGNIFICANCE: The overweight group exhibited significantly higher mean PPP. The PPP under the forefoot region was also significantly higher for the overweight group as compared to the heel. The mean GaitEn values of overweight and control groups were found significantly different at r = (0.7-0.8) x STD, where GaitEn of the control group was relatively higher, which indicates better gait performance as compared to the overweight group in alignment with previous studies. A significant correlation of GaitEn with STD of PPP was revealed for the overweight group only, suggesting that overweight could significantly change the regularity or the complexity of the PPP series. Although no spatiotemporal parameters (stride length, step length, step width) were significantly affected by the increased BMI, GaitEn dynamic measure, along with spatiotemporal (decrease in gait velocity and cadence with increased BMI), and kinetic measures (increased maximum forces and plantar pressure with increased BMI), were significantly affected by overweight, indicating the feasibility of assessing the impact of increased BMI using pressure platforms in clinical settings.

A Systematic Review of Fall Risk Factors in Stroke Survivors: Towards Improved Assessment Platforms and Protocols.

Background/Purpose: To prevent falling, a common incident with debilitating health consequences among stroke survivors, it is important to identify significant fall risk factors (FRFs) towards developing and implementing predictive and preventive strategies and guidelines. This review provides a systematic approach for identifying the relevant FRFs and shedding light on future directions of research. Methods: A systematic search was conducted in 5 popular research databases. Studies investigating the FRFs in the stroke community were evaluated to identify the commonality and trend of FRFs in the relevant literature. Results: twenty-seven relevant articles were reviewed and analyzed spanning the years 1995-2020. The results confirmed that the most common FRFs were age (21/27, i.e., considered in 21 out of 27 studies), gender (21/27), motion-related measures (19/27), motor function/impairment (17/27), balance-related measures (16/27), and cognitive impairment (11/27). Among these factors, motion-related measures had the highest rate of significance (i.e., 84% or 16/19). Due to the high commonality of balance/motion-related measures, we further analyzed these factors. We identified a trend reflecting that subjective tools are increasingly being replaced by simple objective measures (e.g., 10-m walk), and most recently by quantitative measures based on detailed motion analysis. Conclusion: There remains a gap for a standardized systematic approach for selecting relevant FRFs in stroke fall risk literature. This study provides an evidence-based methodology to identify the relevant risk factors, as well as their commonalities and trends. Three significant areas for future research on post stroke fall risk assessment have been identified: 1) further exploration the efficacy of quantitative detailed motion analysis; 2) implementation of inertial measurement units as a cost-effective and accessible tool in clinics and beyond; and 3) investigation of the capability of cognitive-motor dual-task paradigms and their association with FRFs.

Comparative biomechanical analyses of lower cervical spine post anterior fusion versus intervertebral disc arthroplasty: A geometrically patient-specific poroelastic finite element investigation.

Background/Objective: The optimal surgical technique for the treatment of cervical degenerative disc disease (CDDD) towards decreasing the risk of adjacent segment disease (ASD) remains elusive. This study aimed to comparatively investigate the biomechanics of the lower cervical spine following fusion (ACDF) and artificial disc arthroplasty (Bryan® and Prestige LP®) using a validated geometrically patient-specific poroelastic finite element modeling (FEM) approach. Methods: Ten subject-specific pre-operative models were developed and validated based on a FEM approach. Poroelastic models were then constructed using post-operation images for three different treatment scenarios: ACDF; Prestige LP® and Bryan® artificial discs at the C5-C6 level. The biomechanical responses at both surgical and adjacent spinal levels were studied subject to static and cyclic loading. Results: Postoperatively, greater range of motion (ROM), higher annulus fibrosus stress and strain values, and increased disc height and fluid loss at the adjacent levels were detected post ACDF, as compared with pre-op as well as artificial disc arthroplasty. The facet joint forces were larger for the Prestige LP® disc, particularly during extension. The lowest values in disc height and fluid exchange were observed in the Bryan® artificial disc arthroplasty models. Conclusion: Biomechanical analyses revealed that ACDF poses the highest potential risk for adjacent disc degeneration. The artificial discs investigated here (Prestige LP® and Bryan®) not only preserved motion at the instrumented level, but also sustained the pre-op ROM and decreased the intradiscal pressure (IDP) and facet joint forces (FJFs) at adjacent levels, particularly during flexion/extension. The Bryan® artificial disc demonstrated the most efficacy in maintaining the natural poroelastic characteristics of adjacent discs. The translational potential of this article: This study provided a technique for clinicians to use quantitative data towards subject-specific evaluation to comparatively evaluate the impact of ACDF and disc arthroplasty using two types of artificial discs on the biomechanics of the cervical spine. It confirms differences in the poroelastic characteristics of adjacent discs for different fixation techniques, and reveals the advantage of artificial discs with a flexible core for decreasing the risk of ASD.

A Framework for Determining the Performance and Requirements of Cable-Driven Mobile Lower Limb Rehabilitation Exoskeletons.

The global increase in the number of stroke patients and limited accessibility to rehabilitation has promoted an increase in the design and development of mobile exoskeletons. Robot-assisted mobile rehabilitation is rapidly emerging as a viable tool as it could provide intensive repetitive movement training and timely standardized delivery of therapy as compared to conventional manual therapy. However, the majority of existing lower limb exoskeletons continue to be heavy and induce unnecessary inertia and inertial vibration on the limb. Cable-driven exoskeletons can overcome these issues with the provision of remote actuation. However, the number of cables and routing can be selected in various ways posing a challenge to designers regarding the optimal design configuration. In this work, a simulation-based generalized framework for modelling and assessment of cable-driven mobile exoskeleton is proposed. The framework can be implemented to identify a 'suitable' configuration from several potential ones or to identify the optimal routing parameters for a given configuration. For a proof of concept, four conceptual configurations of cable-driven exoskeletons (one with a spring) were developed in a manner where both positive and negative moments could be generated for each joint (antagonistic configuration). The models were analyzed using the proposed framework and a decision metric table has been developed based on the models' performance and requirements. The weight of the metrics can be adjusted depending on the preferences and specified constraints. The maximum score is assigned to the configuration with minimum requirement or error, maximum performance, and vice versa. The metric table indicated that the 4-cable configuration is a promising design option for a lower limb rehabilitation exoskeleton based on tracking performance, model requirements, and component forces exerted on the limb.

Trunk muscle activity during holding two types of dynamic loads in subjects with nonspecific low back pain.

BACKGROUND: Chronic low back pain due to manual lifting continues to be one of the significant common public health challenges in modern societies despite increased automation. While there are extensive studies on the biomechanics of lifting as associated with LBP, the role of unstable and time-varying dynamic loads, quite common in industrial lifting and daily life, remains elusive. OBJECTIVES: The present study aimed to investigate the response of trunk muscles in subjects with chronic non-specific low back pain (CNLBP) while holding unstable dynamic loads. METHODS: Twelve male patients with CNLBP and twelve healthy controls participated in this cross-sectional study. The subjects held static and dynamic loads in neutral positions. Normalized EMG data of the trunk muscles were captured and analyzed by repeated-measures ANOVA test. RESULTS: The low back pain group demonstrated significantly higher activation levels of the internal and external abdominal oblique muscles while holding dynamic loads (p < 0.05). CONCLUSION: Our results suggest that the neuromusculoskeletal system in low back patients holding dynamic loads may invoke a motor control strategy that significantly increases muscle co-activation leading to higher joint stiffness at the expense of higher compressive loads on the lumbar spine. Importantly, the type of load plays a critical role in terms of external perturbations that may lead to spinal injury in CNLBP patients and must, therefore, be considered in the risk prevention and assessment of lifting and other manual material handling tasks.

Trunk Dynamic Stability Assessment for Individuals With and Without Nonspecific Low Back Pain During Repetitive Movement.

OBJECTIVE: This study aimed to employ nonlinear dynamic approaches to assess trunk dynamic stability with speed, symmetry, and load during repetitive flexion-extension (FE) movements for individuals with and without nonspecific low back pain (NSLBP). BACKGROUND: Repetitive trunk FE movement is a typical work-related LBP risk factor contingent on speed, symmetry, and load. Improper settings/adjustments of these control parameters could undermine the dynamic stability of the trunk, hence leading to low back injuries. The underlying stability mechanisms and associated control impairments during such dynamic movements remain elusive. METHOD: Thirty-eight male volunteers (19 healthy, 19 NSLBP) enrolled in the current study. All participants performed repetitive trunk FE movements at high/low speeds, in symmetric/asymmetric directions, with/without a wearable loaded vest. Trunk instantaneous rotation angle was computed for each trial to be assessed in terms of local and orbital stability, using maximum finite-time Lyapunov exponents (LyEs) and Floquet multipliers (FMs), respectively. RESULTS: Both groups demonstrated equivalent competency in terms of trunk control and stability, suggesting functional adaptation strategies may be used by the NSLBP group. Wearing the loaded vest magnified the effects of trunk control impairment for the NSLBP group. The combined presence of high-speed and symmetrical FE movements was associated with least trunk local stability. CONCLUSION: Nonlinear dynamic techniques, particularly LyE, are potentially effective for assessing trunk dynamic stability dysfunction for individuals with NSLBP during various activities. APPLICATION: This work can be applied toward the development of quantitative personalized spinal evaluation tools with a wide range of potential occupational and clinical applications.

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies.

Background: Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait. Research objective: This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms. Methods: A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included. Results and significance: Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Determination of Heart Rate Changes using Simulated Head Up Tilt Test for Syncope Patient Assessment.

Home-based self-training can be beneficial to neurocardiogenic patients, particularly for those who experience a decreased heart rate during the clinical head up tilt test (HUT). Many patients, however, may not be able to attend a clinic and/or attend clinics which lack HUT devices. Individualized heart rate prediction based on a simulated HUT (sHUT) model may address this gap in clinical practice. The proposed sHUT model aims to predict whether home-based self-training is an appropriate beneficial intervention based on the calculated decrease in heart rate from the model. The results obtained with the model are in agreement with previous clinical findings with greater than 80% accuracy in identifying patients who could benefit from home training. The predicted home-based training efficacy of syncope against the control group had a corresponding p-value of p<0.0001. Based on these results, physicians may be able to recommend home training as part of online or telemedicine consultation.Clinical Relevance- The simulated Head Up Tilt model predicts the patient-specific efficacy of tilt-training for patients at home diagnosed with syncope.

Comparative biomechanical analysis of rigid vs. flexible fixation devices for the lumbar spine: A geometrically patient-specific poroelastic finite element study.

BACKGROUND AND OBJECTIVE: Lumbar spinal stenosis (LSS), or the narrowing of the spinal canal, continues to be the leading preoperative diagnosis for adults older than 65 years who undergo spine surgery. Although the treatment of LSS depends on its severity, the optimal surgical technique towards decreasing the risk of adjacent segment disease (ASD) remains elusive. This study aimed to comparatively analyze spinal biomechanics with rigid and flexible fixation devices (i.e., rigid and dynamic posterolateral fusion (PLF) and interspinous process (ISP) devices) during daily activities. METHODS: Using a validated parametric poroelastic finite element modeling approach, 8 subject-specific pre-operative models were developed, and their validity was evaluated. Parametric FE models of the lumbar spines were then regenerated based on post-operation images for (A) rigid PLF (B) dynamic PLF (C) rigid ISP device (Coflex) and (D) flexible ISP device (DIAM) at L4-L5 level. Biomechanical responses for instrumented and adjacent intervertebral discs (IVDs) were analyzed and compared subject to static and cyclic loading. RESULTS: The preoperative models were well comparable with previous works in literature. The postoperative results for the PLF and Coflex rigid systems, demonstrated greater ROM; higher values of stress and strain in the AF region; and increased disc height and fluid loss at the adjacent levels, as compared with the pre-op models and the post-op results of the flexible systems (i.e., dynamic PLF and DIAM). The calculated forces on the facet joint were of smaller magnitude for the ISP devices as compared to the PLF, particularly during extension. CONCLUSIONS: This study demonstrates that the dynamic PLF construct and DIAM implants could be effective to maintain the natural poroelastic characteristics of adjacent IVDs, which could be beneficial for enhancing long-term clinical outcomes. FEM provides clinicians with an invaluable patient-specific quantitative tool for informed surgical planning and discerning follow-up management.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods. Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings. Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included. Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.