Machine Learning-based Classification of Active Walking Tasks in Older Adults using fNIRS

• URL: http://arxiv.org/abs/2102.03987v2
• Date: Wed, 10 Feb 2021 12:21:46 GMT
• Title: Machine Learning-based Classification of Active Walking Tasks in Older Adults using fNIRS
• Authors: Dongning Ma, Meltem Izzetoglu, Roee Holtzer, Xun Jiao
• Abstract summary: Cortical control of gait, specifically in the pre-frontal cortex as measured by functional near infrared spectroscopy (fNIRS), has shown to be moderated by age, gender, cognitive status, and various age-related disease conditions. We develop classification models using machine learning methods to classify active walking tasks in older adults based on fNIRS signals.
• Score: 2.0953361712358025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decline in gait features is common in older adults and an indicator of disability and mortality. Cortical control of gait, specifically in the pre-frontal cortex as measured by functional near infrared spectroscopy (fNIRS), during dual task walking has shown to be moderated by age, gender, cognitive status, and various age-related disease conditions. In this study, we develop classification models using machine learning methods to classify active walking tasks in older adults based on fNIRS signals into either Single-Task-Walk (STW) or Dual-Task-Walk (DTW) conditions. In this study, we develop classification models using machine learning methods to classify active walking tasks in older adults based on fNIRS signals into either single-task walking (STW) or dual-task walking (DTW). The fNIRS measurements included oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) signals obtained from prefrontal cortex (PFC) of the subject performing on the ground active walking tasks with or without a secondary cognitive task. We extract the fNIRS-related features by calculating the minimum, maximum, mean, skewness and kurtosis values of Hb and Hbo2 signals. We then use feature encoding to map the values into binary space. Using these features, we apply and evaluate various machine learning methods including logistic regression (LR), decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), multilayer perceptron (MLP), and Random Forest (RF). Results showed that the machine learning models can achieve around 97\% classification accuracy.

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