See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data

Abstract

As the challenge of mental health problems such as anxiety and depression increasing today, more convenient, objective, real-time assessing techniques of mental state are in need. The Microsoft Kinect camera is a possible option for contactlessly capturing human gait, which could reflect the walkers’ mental state. So we tried to propose a novel method for monitoring individual’s anxiety and depression based on the Kinect-recorded gait pattern. In this study, after finishing the 7-item Generalized Anxiety Disorder Scale (GAD-7) and the 9-item Patient Health Questionnaire (PHQ-9), 179 participants were required to walked on the footpath naturally while shot by the Kinect cameras. Fast Fourier Transforms (FFT) were conducted to extract features from the Kinect-captured gait data after preprocessing, and different machine learning algorithms were used to train the regression models recognizing anxiety and depression levels, and the classification models detecting the cases with specific depressive symptoms. The predictive accuracies of the regression models achieved medium to large level: The correlation coefficient between predicted and questionnaire scores reached 0.51 on anxiety (by epsilon-Support Vector Regression, e-SVR) and 0.51 on depression (by Gaussian Processes, GP). The predictive accuracies could be even higher, 0.74 on anxiety (by GP) and 0.64 on depression (by GP), while training and testing the models on the female sample. The classification models also showed effectiveness on detecting the cases with some symptoms. These results demonstrate the possibility to recognize individual’s questionnaire measured anxiety/depression levels and some depressive symptoms based on Kinect-recorded gait data through machine learning method. This approach shows the potential to develop non-intrusive, low-cost methods for monitoring individuals’ mental health in real time.

Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality

“…The methods of gait based depression detection have not been well established yet. Here we make a comparative analysis of current performance with a similar approach, where they used Fast Fourier Transforms for feature extraction on skeleton data of gait [46]. For the comparison, we transplanted their methods into our dataset.…”

A Survey of Human Gait-Based Artificial Intelligence Applications

“…AI is well suited at learning patterns and detecting an anomaly in the data based on a pre-defined abnormal event (supervised learning) or a clustering algorithm (unsupervised learning), or a combination of the two. A very wide range of human diseases and conditions can affect the way a person walks such as Parkinson’s ( Flagg et al, 2021 ), ( Wahid et al, 2015 ), Huntington’s ( Acosta-Escalante et al, 2018 ), ALS ( Aich et al, 2018 ), idiopathic normal-pressure hydrocephalus (iNPH) ( Ishikawa et al, 2019 ), ASD ( Hasan et al, 2018 ) neuromuscular disease ( Gotlin et al, 2018 ), pediatric hereditary spastic paraplegia (HSP) ( Pulido-Valdeolivas et al, 2018 ), aging ( Strath et al, 2015 ), ( Costilla-Reyes et al, 2021 ), dementia ( Kenney et al, 2018 ), ( Arifoglu and Bouchachia, 2017 ), fatigue ( Zhang J. et al, 2014 ), depression ( Fang et al, 2019 ), anxiety ( Zhao et al, 2019 ), emotional state ( Bhattacharya et al, 2020 ), dual task, or walking while performing a cognitive task ( Costilla-Reyes et al, 2021 ), knee osteoarthritis, ( Kotti et al, 2017 ), stroke (PSH gait), ( Cui et al, 2018 ), ( Clark et al, 2015 ), diabetes ( Sutkowska et al, 2019 ), COVID-19 ( Maghded et al, 2020 ), inflammation ( Lasselin et al, 2020 ), ( Renner et al, 2021 ), level of physical activity ( Renner et al, 2021 ), kidney disease ( Yadollahpour et al, 2018 ), vertigo ( Cao et al, 2021 ), sleep quality ( Liu X. et al, 2019 ), Trendelenburg ( Michalopoulos et al, 2016 ), arthritis ( Karg et al, 2015 ), ( Struss et al, 2018 ), idiopathic toe walking ( Kim et al, 2019 ), drunkenness ( Arnold et al, 2015 ) and influence of marijuana ( Li et al, 2019 ). Thus, monitoring human gait can provide key insight into a person’s health.…”

Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study

“…For selecting the best classifier that is also relevant to the proposed input and output dataset, we considered the traditional ML methods that were already used for classifying skeletal data in previous research such as, Support Vector Machine (SVM) [ 39 ], Random Forest (RF) [ 40 ], K-Nearest Neighbors (kNN) [ 39 ], Linear Regressor [ 41 ], Logistic Regressor [ 40 ], and Artificial Neural Network (ANN) [ 25 ]. After implementing the mentioned classifiers for our data, we selected the results of RF and ANN classifiers to report in detail.…”