Spatial-Temporal Parameters of Gait Associated With Alzheimer Disease: A Longitudinal Analysis

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.

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

Cognitive-motor dual-task interference in Alzheimer's disease, Parkinson's disease, and prodromal neurodegeneration: A scoping review

BACKGROUND

Cognitive-motor interference (CMI) is a common deficit in Alzheimer's (AD) disease and Parkinson's disease (PD) and may have utility in identification of prodromal neurodegeneration. There is lack of consensus regarding measurement of CMI resulting from dual task paradigms.

RESEARCH QUESTION

How are individuals with AD, PD, and prodromal neurodegeneration impacted by CMI as measured by dual-task (DT) performance?

METHODS

A systematic literature search was performed in six datasets using the PRISMA guidelines. Studies were included if they had samples of participants with AD, PD, or prodromal neurodegeneration and reported at least one measure of cognitive-motor DT performance.

RESULTS

4741 articles were screened and 95 included as part of this scoping review. Articles were divided into three non-mutually exclusive groups based on diagnoses, with 26 articles in AD, 56 articles in PD, and 29 articles in prodromal neurodegeneration, and results presented accordingly.

SIGNIFICANCE

Individuals with AD and PD are both impacted by CMI, though the impact is likely different for each disease. We found a robust body of evidence regarding the utility of measures of DT performance in the detection of subtle deficits in prodromal AD and some signals of utility in prodromal PD. There are several key methodological challenges related to DT paradigms for the measurement of CMI in neurodegeneration. Overall, DT paradigms show good potential as a clinical method to probe specific brain regions, networks, and function; however, task selection and effect measurement should be carefully considered.

Executive function and relation to static balance metrics in chronic mild TBI: A LIMBIC-CENC secondary analysis

Introduction

Among patients with traumatic brain injury (TBI), postural instability often persists chronically with negative consequences such as higher fall risk. One explanation may be reduced executive function (EF) required to effectively process, interpret and combine, sensory information. In other populations, a decline in higher cognitive functions are associated with a decline in walking and balance skills. Considering the link between EF decline and reduction in functional capacity, we investigated whether specific tests of executive function could predict balance function in a cohort of individuals with a history of chronic mild TBI (mTBI) and compared to individuals with a negative history of mTBI.

Methods

Secondary analysis was performed on the local LIMBIC-CENC cohort (N = 338, 259 mTBI, mean 45 ± STD 10 age). Static balance was assessed with the sensory organization test (SOT). Hierarchical regression was used for each EF test outcome using the following blocks: (1) the number of TBIs sustained, age, and sex; (2) the separate Trail making test (TMT); (3) anti-saccade eye tracking items (error, latency, and accuracy); (4) Oddball distractor stimulus P300 and N200 at PZ and FZ response; and (5) Oddball target stimulus P300 and N200 at PZ and FZ response.

Results

The full model with all predictors accounted for between 15.2% and 21.5% of the variability in the balance measures. The number of TBI's) showed a negative association with the SOT2 score (p = 0.002). Additionally, longer times to complete TMT part B were shown to be related to a worse SOT1 score (p = 0.038). EEG distractors had the most influence on the SOT3 score (p = 0.019). Lastly, the SOT-composite and SOT5 scores were shown to be associated with longer inhibition latencies and errors (anti-saccade latency and error, p = 0.026 and p = 0.043 respectively).

Conclusions

These findings show that integration and re-weighting of sensory input when vision is occluded or corrupted is most related to EF. This indicates that combat-exposed Veterans and Service Members have greater problems when they need to differentiate between cues when vision is not a reliable input. In sum, these findings suggest that EF could be important for interpreting sensory information to identify balance challenges in chronic mTBI.

Single-Task or Dual-Task? Gait Assessment as a Potential Diagnostic Tool for Alzheimer's Dementia

Background:

A person’s gait performance requires the integration of sensorimotor and cognitive systems. Therefore, a person’s gait may be influenced by concurrent cognitive load such as simultaneous talking. Although it has been known that gait performance of people with Alzheimer’s dementia (AD) is compromised when they attempt a dual-task walking task, it is unclear if using a dual-task gait performance during an AD assessment yields higher diagnostic accuracy.

Objective:

This study was designed to compare the predictive power for AD of dual-task gait performance in an AD assessment to that of single-task gait performance.

Methods:

Participants (14 with AD and 15 healthy controls) walked across the GAITRite© Portable Walkway mat under three different cognitive load conditions: no simultaneous cognitive load, walking while counting numbers by ones, and walking while completing category naming.

Results:

Multiple logistic regression revealed that the gait performance under a dual-task condition (i.e., concurrent counting or category naming) increased the proportion of variance explained by the FAP, SL, and DST, of the incidence of AD.

Conclusion:

Dual-task walking and talking may be a more effective diagnostic feature than single-task walking in a comprehensive AD diagnostic assessment.