The Coefficient of Variation of Step Time Can Overestimate Gait Abnormality: Test-Retest Reliability of Gait-Related Parameters Obtained with a Tri-Axial Accelerometer in Healthy Subjects

Detection of mild cognitive impairment using various types of gait tests and machine learning

Introduction

Alzheimer's disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration.

Methods

This study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC). Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions.

Results and discussion

The study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83.9% F-score in detecting MCI during oval walking tests. This research introduces a cost-effective, Kinect-based method that integrates gait analysis-a key behavioral pattern-with machine learning, offering a practical tool for MCI screening in both clinical and home environments.

Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson's Disease

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.

Alzheimer's Disease Detection Using Comprehensive Analysis of Timed Up and Go Test via Kinect V.2 Camera and Machine Learning

Alzheimer's disease (AD) is a progressive neurodegenerative disease affecting cognitive and functional abilities. However, many patients presume lower cognitive or functional abilities because of aging and do not undergo clinical assessments until the symptoms become too advanced. Developing a low-cost and easy-to-use AD detection tool, which can be used in any clinical or non-clinical setting, can enable widespread AD assessments and diagnosis. This paper investigated the feasibility of developing such a tool to detect AD vs. healthy control (HC) from a simple balance and walking assessment called the Timed Up and Go (TUG) test. We collected joint position data of 47 HC and 38 AD subjects as they performed TUG in front of a Kinect V.2 camera. Our signal processing and statistical analyses provided a comprehensive analysis of balance and gait with 12 significant features for discriminating AD from HC after adjusting for age and the Geriatric Depression Scale. Using these features and a support vector machine classifier, our model classified the two groups with an average accuracy of 97.75% and an F-score of 97.67% for five-fold cross-validation and 98.68% and 98.67% for leave-one-subject out cross-validation. These results demonstrate the potential of our approach as a new quantitative complementary tool for detecting AD among older adults. Our work is novel as it presents the first application of Kinect V.2 camera and machine learning to provide a comprehensive and quantitative analysis of the TUG test to detect AD patients from HC. This study supports the feasibility of developing a low-cost and convenient AD assessment tool that can be used during routine checkups or even at home; however, future investigations could confirm its clinical diagnostic value in a larger cohort.

Comparison of Subjective and Objective Assessments on Improvement in Gait Function after Carotid Endarterectomy

The purpose of the present study was to determine whether objective gait test scores obtained using a tri-axial accelerometer can detect subjective improvement in gait as determined by the patient after carotid endarterectomy (CEA). Each patient undergoing CEA for ipsilateral internal carotid artery stenosis determined whether their gait was subjectively improved at six months after CEA when compared with preoperatively. Gait testing using a tri-axial accelerometer was also performed preoperatively and six months postoperatively. Twelve (15%) of 79 patients reported subjectively improved gait. Areas under the receiver operating characteristic curve for differences between pre- and postoperative test values in stride time, cadence, and ground floor reaction for detecting subjectively improved gait were 0.995 (95% confidence interval (CI), 0.945-1.000), 0.958 (95%CI, 0.887-0.990), and 0.851 (95%CI, 0.753-0.921), respectively. Cut-off points for value differences in detecting subjectively improved gait were identical to mean -1.7 standard deviation (SD) for stride time, mean +1.6 SD for cadence, and mean +0.4 SD for ground floor reaction of control values from normal subjects. Objective gait test scores obtained using the tri-axial accelerometer can detect subjective gait improvements after CEA. When determining significant postoperative improvements in gait using a tri-axial accelerometer, optimal cut-off points for each test value can be defined.

Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns

Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.

Investigating Gait, Movement, and Coordination in Children with Neurodevelopmental Disorders: Is There a Role for Motor Abnormalities in Atypical Neurodevelopment?

Motor abnormalities have been suggested to play a role in most neuropsychiatric disorders, as a potential generic neurodevelopmental vulnerability. However, they still represent a neglected area, with a paucity of empirical studies, especially in pediatric populations. This case-control study aimed to comprehensively assess motor functioning in children with atypical neurodevelopment and investigate whether any socio-demographic or clinical characteristics would concur with motor difficulties to distinguish children with neurodevelopmental disorders (NDD) from healthy controls. Socio-demographic (age and gender) and clinical (intelligence quotient, gait, movement, and coordination) data were collected on 114 children aged 5-15 (83 with NDD, 31 healthy controls). Male children were at significantly higher risk for NDD (OR: 13.023, p < 0.001). Furthermore, there was a statistically significant interaction between the total intelligence quotient and overall coordination such that increasing levels of total intelligence quotient appeared to protect against the likelihood of being diagnosed with an NDD, but only in the context of a preserved coordination (OR: 0.964, p = 0.038). Collectively, results may have important public health implications, as they point towards the development of new approaches to establish an early prognosis in neurodevelopment, including assessing motor difficulties and mitigating their impact on children's quality of life.

Improvement in gait function after carotid endarterectomy is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex: a 123I-iomazenil SPECT study

OBJECTIVE

Carotid endarterectomy (CEA) often restores cerebral perfusion and neurotransmitter receptor function, which is seen on early and late images, respectively, on brain I-iomazenil single-photon emission computed tomography (SPECT). The reliability of gait-related parameters obtained using a triaxial accelerometer, a portable device for gait assessment, has been confirmed with test-retest measurements. The purpose of the present prospective cohort study was to determine whether improvement in gait function after CEA is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex.

METHODS

Gait testing using a triaxial accelerometer was performed preoperatively and 6 months postoperatively in 64 patients undergoing CEA for ipsilateral internal carotid artery stenosis (≥70%). I-iomazenil SPECT was also performed with scanning within 30 min (early images) and at 180 min (late images) after tracer administration before and after surgery. SPECT data were analyzed using a three-dimensional stereotactic surface projection, and motor (Brodmann 4) and premotor (Brodmann 6) cortexes in each hemisphere were combined and defined as the motor-related cortex.

RESULTS

Based on preoperative and postoperative gait testing, seven patients (11%) showed postoperative improved gait. Logistic regression analysis revealed that postoperative increase in I-iomazenil uptake in the motor-related cortex ipsilateral to surgery on early [95% confidence interval (CI), 4.32-365.21; P = 0.0477) or late (95% CI, 9.45-1572.57; P = 0.0173) images was an independent predictor of postoperative improved gait.

CONCLUSIONS

Improvement in gait function after CEA is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex.