Quantitative Gait Analysis Research Articles

The influence of knee position during static calibration trials on evaluation of knee loading during gait in individual with medial knee osteoarthritis

Quantitative three-dimensional gait analysis has been used to evaluate the loading at the knee (i.e. external knee adduction moment, EKAM) during level ground walking in individuals with knee osteoarthritis (OA). The magnitude of EKAM can be influenced by some factors, such as knee marker position and foot placement angles in static calibration trials, which may lead to inaccurate functional assessments and intervention planning. This study aimed to clarify the effects of knee position during static calibration trials on the evaluation of knee loading during gait in individuals with medial knee OA. Seventeen individuals with medial knee OA completed three different static standing trials; (1) knee flexed at 0 degrees, (2) knee flexed at 15 degrees, and (3) knee flexed at 30 degrees before walking at their self-selected speed. A sixteen-camera three-dimensional VICON gait analysis system with four AMTI force platforms was used to collect the EKAM, knee adduction angular impulse (KAAI), knee joint center (KJC), and other knee kinematic and kinetic variables during gait. A repeated measures ANOVA was used to investigate the differences between conditions. The 1st peak of EKAM, the 1st peak EKAM arm, KAAI, and knee extension moment were significantly increased at the 15-degree and 30-degree conditions in comparison with the 0-degree condition (P < 0.05). Additionally, the knee flexion moment and knee external rotation moment were significantly reduced at the 15-degree and 30-degree conditions in comparison with the 0-degree condition (P < 0.05). All biomechanical variables were influenced by the localization of the KJC during static calibration trials. The changes in knee position during static trials significantly affected the 1st peak EKAM, KAAI, and other knee kinematics and kinetics variables during gait. Therefore, future studies should consider keeping the participants’ knees in a consistent position during static trials between visits, as the variations in knee position could mask or exaggerate the differences between groups and interventions.

Use of predictive analytics for the screening of patients with adolescent idiopathic scoliosis with quantitative gait analysis

Use of predictive analytics for the screening of patients with adolescent idiopathic scoliosis with quantitative gait analysis

MP-Gait: Fine-grained Parkinson's Disease Gait Impairment Assessment with Robust Feature Analysis

Patients with Parkinson's disease (PD) often show gait impairments including shuffling gait, festination, and lack of arm and leg coordination. Quantitative gait analysis can provide valuable insights for PD diagnosis and monitoring. Prior work has utilized 3D motion capture, foot pressure sensors, IMUs, etc. to assess the severity of gait impairment in PD patients These sensors, despite their high precision, are often expensive and cumbersome to wear which makes them not the best option for long-term monitoring and naturalistic deployment settings. In this paper, we introduce mP-Gait, a millimeter-wave (mmWave) radar-based system designed to detect the gait features in PD patients and predict the severity of their gait impairment. Leveraging the high frequency and wide bandwidth of mmWave radar signals, mP-Gait is able to capture high-resolution reflected signals from different body parts during walking. We develop a pipeline to detect walking, extract gait features using signal analysis methods, and predict patients' UPDRS-III gait scores with a machine learning model. As gait features from PD patients with gait impairment are correctly and robustly extracted, mP-Gait is able to observe the fine-grained gait impairment severity fluctuation caused by medication response. To evaluate mP-Gait, we collected gait features from 144 participants (with UPDRS-III gait scores between 0 and 2) containing over 4000 gait cycles. Our results show that mP-Gait can achieve a mean absolute error of 0.379 points in predicting UPDRS-III gait scores.

Vibrotactile Foot Device for Freezing of Gait in Parkinson's Disease: A Pilot Study.

Vibrotactile stimulation has been studied in its efficacy of reducing freezing of gait (FOG) in patients with Parkinson's disease (PD). However, the results are still controversial. We evaluated the efficacy of a newly developed vibrotactile foot device on freezing severity and gait measures in PD patients with FOG. To evaluate the efficacy of vibrotactile foot device on PD patients with FOG. Thirty-three PD patients with FOG were examined during their "off" medication state. The efficacy of the vibrotactile foot device was evaluated using a gait protocol comprising walking trials with vibrotactile stimulation "off" and "on." Walking trials were videotaped for the offline rating by two movement disorder specialists. The Opal inertial sensor unit (128 Hz; Mobility Lab; APDM Inc., Portland, OR, USA) was used for quantitative gait analysis. The results demonstrated 33.1% reduction in number of FOG episodes (P < 0.001) and 32.6% reduction of freezing episodes (P < 0.001). Quantitative gait analysis showed a significant increase in step length (P = 0.033). A moderate negative correlation was observed between the change of percent time frozen and age (r = -0.415, P = 0.016). 73% of participants reported minimal to substantial improvement in walking with this vibrating stimulation delivered by the vibrotactile foot device. The vibrotactile foot device is an efficient device that could significantly reduce freezing severity and provide gait regulation to patients with PD experiencing frequent freezing. It could potentially be used in the home environment for improving the quality of life.

Neural network-based estimation of lower limb joint kinematics: A minimally intrusive approach for gait analysis

The establishment of a quantitative gait analysis system holds paramount importance, particularly in the context of functional rehabilitation of the lower limbs. Clinicians emphasize the imperative for sensors to be portable, compact, integrated, and non-intrusive, crucial characteristics in the rehabilitation field to facilitate their use and ensure optimal integration into care protocols. This study investigates an innovative approach aimed at reducing the reliance on body-fixed sensors by harnessing their data within a neural network, thus concentrating on the joint kinematics of the lower limbs. The primary objective is to estimate the flexion-extension angles of the hip, knee, and ankle during walking, utilizing data collected by two sensors positioned on the subject's legs. Initially, the neural network undergoes training with calculated data (leg tilt angles and angular velocities) sourced from the OpenSim database, followed by further refinement with experimental data obtained from a subject walking on a treadmill, wherein leg tilt angles and angular velocities are measured. The significance of this research is underscored by the demonstrated capability, through conducted tests, of the implemented networks to efficiently fuse data from a minimal set of sensors. Consequently, the proposed approach emerges as both practical and minimally intrusive, facilitating a robust evaluation of gait kinematic parameters.

Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes

Gait impairments are among the most common and disabling symptoms of Parkinson’s disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes. To characterize comprehensive and objective gait alterations and to identify the potential gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring. We analyzed gait parameters related to upper/lower limbs, trunk and lumbar, and postural transitions from 24 tremor-dominant (TD) and 20 postural instability gait difficulty (PIGD) dominant PD patients who were in early stage and 39 matched healthy controls (HC) during the Timed Up and Go test using wearable sensors. Results show: (1) Both TD and PIGD groups showed restricted backswing range in bilateral lower extremities and more affected side (MAS) arm, reduced trunk and lumbar rotation range in the coronal plane, and low turning efficiency. The receiver operating characteristic (ROC) analysis revealed these objective gait features had high discriminative value in distinguishing both PD subtypes from the HC with the area under the curve (AUC) values of 0.7~0.9 (p < 0.01). (2) Subtle but measurable gait differences existed between TD and PIGD patients before the onset of clinically apparent gait impairment. (3) Specific gait parameters were significantly associated with disease severity in TD and PIGD subtypes. Objective gait biomarkers based on wearable sensors may facilitate timely and personalized gait treatments in PD subtypes through early diagnosis, subtype differentiation, and disease severity monitoring.

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients

BackgroundRecently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases.MethodsWe recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal.ResultsWe analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3–13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8–39] ms) and 26 ms (IQR [12–50] ms).ConclusionsOur results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

SAGA: Stability-Aware Gait Analysis in constraint-free environments

Background:Gait abnormality detection is a challenging task in clinical practice. The majority of the current frameworks for gait abnormality detection involve the individual processes of segmentation, feature estimation, feature learning, and similarity assessment. Since each component of these modules is fixed and they are mutually independent, their performance under difficult circumstances is not ideal. We combine those processes into a single framework, a gait abnormality detection system with an end-to-end network. Methods:It is made up of convolutional neural networks and Deep-Q-learning methods: one for coordinate estimation and the other for classification. In a single joint learning technique that may be trained together, the two networks are modeled. This method is significantly more efficient for use in real life since it drastically simplifies the conventional step-by-step approach. Results:The proposed model is experimented on MATLAB R2020a. While considering into consideration the stability factor, our proposed model attained an average case accuracy of 95.3%, a sensitivity of 96.4%, and a specificity of 94.1%. Significance:Our paradigm for quantifying gait analysis using commodity equipment will improve access to quantitative gait analysis in medical facilities and rehabilitation centers while also allowing academics to conduct large-scale investigations for gait-related disorders. Numerous experimental findings demonstrate the effectiveness of the proposed strategy and its ability to provide cutting-edge outcomes.

Pharmacological inhibition of STING reduces neuroinflammation-mediated damage post-traumatic brain injury.

Traumatic brain injury (TBI) remains a major public health concern worldwide with unmet effective treatment. Stimulator of interferon genes (STING) and its downstream type-I interferon (IFN) signalling are now appreciated to be involved in TBI pathogenesis. Compelling evidence have shown that STING and type-I IFNs are key in mediating the detrimental neuroinflammatory response after TBI. Therefore, pharmacological inhibition of STING presents a viable therapeutic opportunity in combating the detrimental neuroinflammatory response after TBI. This study investigated the neuroprotective effects of the small-molecule STING inhibitor n-(4-iodophenyl)-5-nitrofuran-2-carboxamide (C-176) in the controlled cortical impact mouse model of TBI in 10- to 12-week-old male mice. Thirty minutes post-controlled cortical impact surgery, a single 750-nmol dose of C-176 or saline (vehicle) was administered intravenously. Analysis was conducted 2h and 24 h post-TBI. Mice administered C-176 had significantly smaller cortical lesion area when compared to vehicle-treated mice 24 h post-TBI. Quantitative temporal gait analysis conducted using DigiGait™ showed C-176 administration attenuated TBI-induced impairments in gait symmetry, stride frequency and forelimb stance width. C-176-treated mice displayed a significant reduction in striatal gene expression of pro-inflammatory cytokines Tnf-α, Il-1β and Cxcl10 compared to their vehicle-treated counterparts 2h post-TBI. This study demonstrates the neuroprotective activity of C-176 in ameliorating acute neuroinflammation and preventing white matter neurodegeneration post-TBI. This study highlights the therapeutic potential of small-molecule inhibitors targeting STING for the treatment of trauma-induced inflammation and neuroprotective potential.

Human Infrapatellar Fat Pad Mesenchymal Stem Cell-derived Extracellular Vesicles Purified by Anion Exchange Chromatography Suppress Osteoarthritis Progression in a Mouse Model.

Human Infrapatellar Fat Pad Mesenchymal Stem Cell-derived Extracellular Vesicles Purified by Anion Exchange Chromatography Suppress Osteoarthritis Progression in a Mouse Model.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis.

Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models-logistic regression, support vector machine, random forest, and LightGBM-were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.

Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change.

Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only simple videos recorded using low-cost devices (tablets). We report four primary advances: 1) a novel, versatile workflow that leverages an open-source human pose estimation algorithm (OpenPose) to perform gait analyses using videos recorded from multiple different perspectives (e.g., frontal, sagittal), 2) validation of this workflow in three different populations of participants (adults without gait impairment, persons post-stroke, and persons with Parkinson's disease) via comparison to ground-truth three-dimensional motion capture, 3) demonstration of the ability to capture clinically relevant, condition-specific gait parameters, and 4) tracking of within-participant changes in gait, as is required to measure progress in rehabilitation and recovery. Importantly, our workflow has been made freely available and does not require prior gait analysis expertise. The ability to perform quantitative gait analyses in nearly any setting using only low-cost devices and computer vision offers significant potential for dramatic improvement in the accessibility of clinical gait analysis across different patient populations.

Characterizing ambulatory function in children with PPP2R5D-related neurodevelopmental disorder

BackgroundIndividuals with PPP2R5D-related neurodevelopmental disorder have an atypical gait pattern characterized by ataxia and incoordination. Structured, quantitative assessments are needed to further understand the impact of these impairments on function. Research questionHow do gait parameters and ambulatory function of individuals with PPP2R5D-related neurodevelopmental disorder compare to age and sex matched healthy norms? MethodsTwenty-six individuals with PPP2R5D pathogenic genetic variants participated in this observational, single visit study. Participants completed at least one of the following gait assessments: quantitative gait analysis at three different speeds (preferred pace walking (PPW), fast paced walking (FPW) and running, six-minute walk test (6MWT), 10-meter walk run (10MWR), and timed up and go (TUG). Descriptive statistics were used to summarize gait variables. Percent of predicted values were calculated using published norms. Paired t-tests and regression analyses were used to compare gait variables. ResultsThe median age of the participants was 8 years (range 4–27) and eighteen (69.2 %) were female. Individuals with PPP2R5D-related neurodevelopmental disorder walked slower and with a wider base of support than predicted for their age and sex. Stride velocity ranged from 48.9 % to 70.1 % and stride distance from 58.5 % to 81.9 % of predicted during PPW. Percent of predicted distance walked on the 6MWT ranged from 30.6 % to 71.1 % representing varied walking impairment. Increases in stride distance, not cadence, were associated with changes in stride velocity in FPW (R2 = 0.675, p =< 0.001) and running conditions (R2 = 0.918, p =< 0.001). SignificanceWe quantitatively assessed the abnormal gait in individuals with PPP2R5D-related neurodevelopmental disorder. These impairments may affect ability to adapt to environmental changes and participation in daily life.Rehabilitative interventions targeting gait speed and balance may improve function and safety for individuals with PPP2R5D-related neurodevelopmental disorder.

Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment.

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders

Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.

GenPup-M: A novel validated owner-reported clinical metrology instrument for detecting early mobility changes in dogs.

To use a previously validated veterinary clinical examination sheet, Liverpool Osteoarthritis in Dogs (LOAD) questionnaire, combined with kinetic and kinematic gait analysis in dogs with/without mobility problems to demonstrate the capacity of a novel clinical metrology instrument ("GenPup-M") to detect canine mobility impairments. Quantitative study. 62 dogs (31 with mobility impairments and 31 without mobility impairments). The dogs' clinical history was obtained from owners and all dogs underwent a validated orthopaedic clinical examination. Mobility impairments were diagnosed in the mobility impaired group based on clinical history and orthopaedic examination. Owners were asked to complete GenPup-M along with a previously validated mobility questionnaire (Liverpool Osteoarthritis in Dogs (LOAD)) to identify construct validity. As a test of criterion validity, the correlation between instrument scores and the overall clinical examination scores, along with force-platform obtained peak vertical forces (PVF) were calculated. GenPup-M underwent internal consistency and factor analysis. Spatiotemporal parameters were calculated for dogs with/without mobility impairments to define the gait differences between these two groups. Principal Component Analysis identified GenPup-M had two components with Eigenvalues >1 ("stiffness/ease of movement" and "willingness to be active/exercise"). Cronbach's α was used to test internal consistency of GenPup-M and was found to be "good" (0.87). There was a strong, positive correlation between GenPup-M and LOAD responses (r2 = 0.69, p<0.001) highlighting construct validity. Criterion validity was also shown when comparing GenPup-M to clinical examination scores (r2 = 0.74, p<0.001) and PVF (r2 = 0.43, p<0.001). Quantitative canine gait analysis showed that there were statistically significant differences between peak vertical forces (PVF) of mobility impaired and non-mobility impaired dogs (p<0.05). Analyses of PVF showed that non-mobility impaired dogs more evenly distributed their weight across all thoracic and pelvic limbs when compared to mobility impaired dogs. There were also consistent findings that mobility impaired dogs moved slower than non-mobility impaired dogs. GenPup-M is a clinical metrology instrument (CMI) that can be completed by dog owners to detect all mobility impairments, including those that are early in onset, indicating the versatility of GenPup-M to assess dogs with and without mobility impairments. Results of the study found that GenPup-M positively correlated with all three objective measures of canine mobility and consequently showed criterion and construct validity. Owner-reported CMIs such as GenPup-M allow non-invasive scoring systems which veterinary surgeons and owners can use to allow communication and longitudinal assessment of a dog's mobility. It is anticipated that GenPup-M will be used by owners at yearly vaccinations/health checks, allowing identification of any subtle mobility changes, and enabling early intervention.

Wearable Internet of Things Gait Sensors for Quantitative Assessment of Myers–Briggs Type Indicator Personality

Gait is a typical habitual human behavior and manifestation of personality. The unique properties of individual gaits may offer important clues in the assessment of personality. However, assessing personality accurately through quantitative gait analysis remains a daunting challenge. Herein, targeting young individuals, standardized gait data are obtained from 114 subjects with a wearable gait sensor, and the Myers–Briggs Type Indicator (MBTl) personality scale is used to assess their corresponding personality types. Artificial intelligence algorithms are used to systematically mine the relationship between gaits and 16 personality types. The work shows that gait parameters can indicate the personality of a subject from the four MBTI dimensions of E‐l, S‐N, T‐F, and J‐P with a concordance rate as high as 95%, 96%, 91%, and 91%, respectively. The overall measurement accuracy for the 16 personality types is 88.16%. Moreover, a personality tracking experiment on all the subjects after one year to assess the stability of their personality is also conducted. This research, which is based on a smart wearable Internet of Things gait sensor, not only establishes a new connection between behavioral analysis and personality assessment but also provides a set of accurate research tools for the quantitative assessment of personality.

Gait analysis using digital biomarkers including smart shoes in lumbar spinal canal stenosis: a scoping review.

Lumbar spinal canal stenosis (LSS) is characterized by gait abnormalities, and objective quantitative gait analysis is useful for diagnosis and treatment. This review aimed to provide a review of objective quantitative gait analysis in LSS and note the current status and potential of smart shoes in diagnosing and treating LSS. The characteristics of gait deterioration in LSS include decreased gait velocity and asymmetry due to neuropathy (muscle weakness and pain) in the lower extremities. Previous laboratory objective and quantitative gait analyses mainly comprised marker-based three-dimensional motion analysis and ground reaction force. However, workforce, time, and costs pose some challenges. Recent developments in wearable sensor technology and markerless motion analysis systems have made gait analysis faster, easier, and less expensive outside the laboratory. Smart shoes can provide more accurate gait information than other wearable sensors. As only a few reports exist on gait disorders in patients with LSS, future studies should focus on the accuracy and cost-effectiveness of gait analysis using smart shoes.

Stretching and Eccentric Exercises Normalize Gait Parameters in Gastrocnemius Tightness Subjects

Background: Isolated gastrocnemius tightness (IGT) is a prevalent condition linked to various foot pathologies. In a previous quantitative gait analysis study, we identified an increase in knee flexion during the midstance phase in IGT patients compared with controls. Although stretching and eccentric exercises (the Stanish protocol) are commonly used for IGT management, their impact on gait parameters remains poorly understood. This study aimed to assess the influence of a Stanish protocol on gait parameters in bilateral IGT subjects. Methods: We enrolled 10 asymptomatic bilateral IGT subjects and 10 controls. Quantitative gait analysis and dynamic baropodometry were carried out on each subject. A Stanish protocol was applied for 4 weeks (five sessions/week) by the IGT group, followed by a similar gait analysis. The ankle and knee range of motion and foot pressure distribution were assessed during the midstance phase of the gait. Results: An increase in knee flexion was initially present in the IGT group compared with controls (8.9 +/− 4.6 vs. 3.4 +/− 2.3 degrees, p &lt; 0.001). There was no difference in the ankle range of motion and foot pressures between the groups at that time. Significant reductions in knee flexion during gait were observed in the IGT subjects after the Stanish protocol (8.9 +/− 4.6 to 3.7 +/− 2.3 degrees, p &lt; 0.001) with a normalization of this parameter (3.4 +/− 2.3 in controls vs. 3.7 +/− 2.3 degrees in IGT, p = 0.72). There was no change in ankle range of motion and foot pressure after the Stanish protocol. Conclusions: Our findings support the effectiveness of the Stanish protocol in reducing knee flexion and normalizing gait in IGT subjects. This protocol not only offers a noninvasive approach for IGT-related issues management but could also enable prophylactic care in asymptomatic cases.