Detection of mild cognitive Impairment from gait using Adaptive Neuro-Fuzzy Inference system

Seifallahi, Mahmoud; Mehraban, Afsoon Hassani; Khamseh, Fatemeh; Salehi, Masoumeh; Hajati, Gholamreza; Khansari, Mehrdad; Ahmadzadeh, Mina; Babaie, Mehrzad

doi:10.1016/j.bspc.2021.103195

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…Several studies included in the review (n=10) reported machine learning classifier outcomes for identifying people living with dementia, MCI or PD from control [19,25,[29][30][31]33,[35][36][37]46] whereas several others (n=8) reported models that computed clinical assessment scores [17,18,24,34,[38][39][40][41]. Although these are useful outcomes, it is important to note that models that help detect gait impairment and predict falls (n=5) [14][15][16]27,32] could potentially be more useful in practical applications for assessing functional performance.…”

Section: Discussionmentioning

confidence: 99%

“…In dementia inpatients with drug-induced parkinsonism, the Kinect was used to capture parkinsonian traits [17] , quantify parkinsonian gait [24] and along with pose estimation of recordings from a video camera estimate parkinsonian severity [18]. It was also used in the MCI studies reviewed (n=2), as a tool to detect mild cognitive impairment (MCI) from gait features [19,25] .…”

Section: Patient Groupsmentioning

confidence: 99%

“…Several studies reported excellent classification accuracy, with some achieving 100% accuracy [31,36,37] . For instance, Seifallahi et al [25] achieved an accuracy of over 90% for differentiating between people with mild cognitive impairment and controls using an Adaptive Neuro-fuzzy Inference System classifier. Khan et al [36] reported a 70.83% accuracy in predicting UPDRS-gait scores using a SVM model, with an area under the ROC curve of 80.88%.…”

Section: Feature Categories [Tablementioning

confidence: 99%

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Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review (Preprint)

Jeyasingh-Jacob,

Crook-Rumsey,

Shah

et al. 2023

Preprint

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BACKGROUND Markerless motion capture (MMC) uses video cameras and/or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia. OBJECTIVE The primary objective of our systematic review was to investigate the application of MMC using full-body tracking to quantify functional performance in people with dementia, mild cognitive impairment (MCI) and Parkinson’s disease (PD) METHODS We systematically searched for relevant articles which yielded a total of 1595 results. Inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full article screening out of which 26 eligible studies that met the selection criteria were included in the review.  RESULTS Primarily, the selected studies focused on gait analysis, while other functional tasks, such as sit-to-stand and stepping in place, were also explored. However, activities of daily living were not evaluated in any of the studies. MMC models varied across the studies encompassing depth cameras vs standard video cameras or mobile phone cameras with postprocessing using deep-learning model. However, only a few studies conducted rigorous comparisons with established ground-truths. CONCLUSIONS Despite its potential as an effective tool for analysing movement and posture in individuals with dementia, MCI, and PD, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real-world.

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Section: Discussionmentioning

confidence: 99%

Section: Patient Groupsmentioning

confidence: 99%

Section: Feature Categories [Tablementioning

confidence: 99%

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Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review (Preprint)

Jeyasingh-Jacob,

Crook-Rumsey,

Shah

et al. 2023

Preprint

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“…The ANFIS is a hybrid model that combines an adaptive artificial neural network (ANN) and the FIS [ 81 , 82 ]. Since it combines both an adaptive ANN and the FIS, it has the benefits of both.…”

Section: Materials and Methodsmentioning

confidence: 99%

ETMT: A Tool for Eye-Tracking-Based Trail-Making Test to Detect Cognitive Impairment

Chandrasekharan,

Joseph,

Ram

et al. 2023

Sensors

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The growing number of people with cognitive impairment will significantly increase healthcare demand. Screening tools are crucial for detecting cognitive impairment due to a shortage of mental health experts aiming to improve the quality of life for those living with this condition. Eye tracking is a powerful tool that can provide deeper insights into human behavior and inner cognitive processes. The proposed Eye-Tracking-Based Trail-Making Test, ETMT, is a screening tool for monitoring a person’s cognitive function. The proposed system utilizes a fuzzy-inference system as an integral part of its framework to calculate comprehensive scores assessing visual search speed and focused attention. By employing an adaptive neuro-fuzzy-inference system, the tool provides an overall cognitive-impairment score, allowing psychologists to assess and quantify the extent of cognitive decline or impairment in their patients. The ETMT model offers a comprehensive understanding of cognitive abilities and identifies potential deficits in various domains. The results indicate that the ETMT model is a potential tool for evaluating cognitive impairment and can capture significant changes in eye movement behavior associated with cognitive impairment. It provides a convenient and affordable diagnosis, prioritizing healthcare resources for severe conditions while enhancing feedback to practitioners.

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“…Alharthi et al [16] review research works that apply artificial intelligence techniques for human gait analysis and conclude that deep learning CNNs usually outperform shallow learning models. Seifallahi et al [17] carried out a study in which 70 elderly subjects with mild cognitive impairment participate along with 80 elderly control control subjects with good health. This study collects data from a Kinect device and uses an adaptive Neuro-Fuzzy Inference System to identify mild cognitive impairment with values of more than 90% for accuracy, sensitivity, and F-score.…”

Section: B Deep Learning and Alzheimer's Diseasementioning

confidence: 99%

CLADSI: Deep Continual Learning for Alzheimer's Disease Stage Identification Using Accelerometer Data

Bringas,

Duque,

Lage

et al. 2024

IEEE J. Biomed. Health Inform.

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Alzheimer's disease (AD) is a neurodegenerative disorder that can cause a significant impairment in physical and cognitive functions. Gait disturbances are also reported as a symptom of AD. Previous works have used Convolutional Neural Networks (CNNs) to analyze data provided by motion sensors that monitor Alzheimer's patients. However, these works have not explored continual learning algorithms that allow the CNN to configure itself as it receives new data from these sensors. This work proposes a method aimed at enabling CNNs to learn from a continuous stream of data from motion sensors without having full access to previous data. The CNN identifies the stage of AD from the analysis of data provided by motion sensors. The work includes an experimentation with data captured by accelerometers that monitored the activity of 35 Alzheimer's patients for a week in a daycare center. The CNN achieves an accuracy of 86,94%, 86,48% and 84,37% for 2, 3 and 4 experiences respectively. The proposal provides advantages to working with a continuous stream of data so that the CNN are constantly selfconfiguring without the intervention of a human. The work can be considered as promising and helpful in finding deep learning solutions in medical cases in which patients are constantly monitored.

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Detection of mild cognitive Impairment from gait using Adaptive Neuro-Fuzzy Inference system

Cited by 6 publications

References 31 publications

Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review (Preprint)

Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review (Preprint)

ETMT: A Tool for Eye-Tracking-Based Trail-Making Test to Detect Cognitive Impairment

CLADSI: Deep Continual Learning for Alzheimer's Disease Stage Identification Using Accelerometer Data

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