Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from healthy controls was developed based on linear and nonlinear heart rate variability (HRV), which reflects the autonomic cardiovascular regulation. HRV data were collected from 37 MDD patients and 41 healthy controls during five 5-min experimental phases: the baseline, a mental stress task, stress recovery, a relaxation task, and relaxation task recovery. The experimental protocol was designed to assess the autonomic responses to stress and recovery. Twenty HRV indices were extracted from each phase, and a total of 100 features were used for classification using a support vector machine (SVM). SVM-recursive feature elimination (RFE) and statistical filter were employed to perform feature selection. We achieved 74.4% accuracy, 73% sensitivity, and 75.6% specificity with two optimal features selected by SVM-RFE, which were extracted from the stress task recovery and mental stress phases. Classification performance worsened when individual phases were used separately as input data, compared to when all phases were included. The SVM-RFE using nonlinear and Poincaré plot HRV features performed better than that using the linear indices and matched the best performance achieved by using all features. We demonstrated the machine learning-based diagnosis of MDD using HRV analysis. Monitoring the changes in linear and nonlinear HRV features for various autonomic nervous system states can facilitate the more objective identification of MDD patients.

Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.

Background: Although emotion-specific autonomic responses based on the discrete theory of emotion have been widely studied, studies on the reliability of physiological responses to emotional stimuli are limited. In this study, we aimed to assess the reliability of physiological changes induced by the six basic emotions (happiness, sadness, anger, fear, disgust, and surprise) that were measured during 10 weekly repeated experiments. Methods: Twelve college students participated, and in each experiment, physiological signals were collected before and while participants were watching emotion-provoking film clips. Additionally, the participants self-evaluated the emotions that they experienced during the film presentation at the end of each emotional stimulus. To avoid adaptation of participants to identical stimuli during repeated measurements, we used 10 different film clips for each emotion, and thus a total of 60 film clips over 10 weeks were used. Physiological features, such as skin conductance level (SCL), fingertip temperature (FT), heart rate (HR), and blood volume pulse (BVP), were extracted from the physiological signals. Two reliability indices, Cronbach’s alpha and intraclass correlation coefficient, were calculated from the physiological features to assess internal consistency and interrater reliability, respectively. Results: We found that SCL, HR, and BVP measured during the emotion-provoking phase over the 10 weekly sessions were more reliable than those assessed at baseline. Furthermore, SCL, HR, and BVP from the emotion-provoking phase exhibited excellent internal consistency and interrater reliability. Conclusions: Our findings suggest that these features can be used as reliable physiological indices in emotion studies. The results also support the significance of physiological signals as meaningful indicators for emotion recognition in HCI (human computer interface) area.

Kang N, et al. Allergy Asthma Immunol Res. 2024 Jan;16(1):42-54. https://doi.org/10.4168/aair.2024.16.1.42