An estimated 350 million people worldwide are affected by depression. Using affective sensing technology, our long-term goal is to develop an objective multimodal system that augments clinical opinion during the diagnosis and monitoring of clinical depression. This paper steps towards developing a classification system-oriented approach, where feature selection, classification and fusion-based experiments are conducted to infer which types of behaviour (verbal and nonverbal) and behaviour combinations can best discriminate between depression and non-depression. Using statistical features extracted from speaking behaviour, eye activity, and head pose, we characterise the behaviour associated with major depression and examine the performance of the classification of individual modalities and when fused. Using a real-world, clinically validated dataset of 30 severely depressed patients and 30 healthy control subjects, a Support Vector Machine is used for classification with several feature selection techniques. Given the statistical nature of the extracted features, feature selection based on T-tests performed better than other methods. Individual modality classification results were considerably higher than chance level (83 percent for speech, 73 percent for eye, and 63 percent for head). Fusing all modalities shows a remarkable improvement compared to unimodal systems, which demonstrates the complementary nature of the modalities. Among the different fusion approaches used here, feature fusion performed best with up to 88 percent average accuracy. We believe that is due to the compatible nature of the extracted statistical features.

Depression is a common and disabling mental health disorder, which impacts not only on the sufferer but also on their families, friends and the economy overall. Despite its high prevalence, current diagnosis relies almost exclusively on patient self-report and clinical opinion, leading to a number of subjective biases. Our aim is to develop an objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. In this paper, we analyse the performance of eye movement features extracted from face videos using Active Appearance Models for a binary classification task (depressed vs. non-depressed). We find that eye movement low-level features gave 70% accuracy using a hybrid classifier of Gaussian Mixture Models and Support Vector Machines, and 75% accuracy when using statistical measures with SVM classifiers over the entire interview. We also investigate differences while expressing positive and negative emotions, as well as the classification performance in gender-dependent versus gender-independent modes. Interestingly, even though the blinking rate was not significantly different between depressed and healthy controls, we find that the average distance between the eyelids (`eye opening') was significantly smaller and the average duration of blinks significantly longer in depressed subjects, which might be an indication of fatigue or eye contact avoidance.

Depression is a common and disabling mental health disorder, which impacts not only on the sufferer but also their families, friends and the economy overall. Our ultimate aim is to develop an automatic objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. Here, we analyse the performance of head pose and movement features extracted from face videos using a 3D face model projected on a 2D Active Appearance Model (AAM). In a binary classification task (depressed vs. non-depressed), we modelled low-level and statistical functional features for an SVM classifier using real-world clinically validated data. Although the head pose and movement would be used as a complementary cue in detecting depression in practice, their recognition rate was impressive on its own, giving 71.2% on average, which illustrates that head pose and movement hold effective cues in diagnosing depression. When expressing positive and negative emotions, recognising depression using positive emotions was more accurate than using negative emotions. We conclude that positive emotions are expressed less in depressed subjects at all times, and that negative emotions have less discriminatory power than positive emotions in detecting depression. Analysing the functional features statistically illustrates several behaviour patterns for depressed subjects: (1) slower head movements, (2) less change of head position, (3) longer duration of looking to the right, (4) longer duration of looking down, which may indicate fatigue and eye contact avoidance. We conclude that head movements are significantly different between depressed patients and healthy subjects, and could be used as a complementary cue.

Major depressive disorders are mental disorders of high prevalence, leading to a high impact on individuals, their families, society and the economy. In order to assist clinicians to better diagnose depression, we investigate an objective diagnostic aid using affective sensing technology with a focus on acoustic features. In this paper, we hypothesise that (1) classifying the general characteristics of clinical depression using spontaneous speech will give better results than using read speech, (2) that there are some acoustic features that are robust and would give good classification results in both spontaneous and read, and (3) that a `thin-slicing' approach using smaller parts of the speech data will perform similarly if not better than using the whole speech data. By examining and comparing recognition results for acoustic features on a real-world clinical dataset of 30 depressed and 30 control subjects using SVM for classification and a leave-one-out cross-validation scheme, we found that spontaneous speech has more variability, which increases the recognition rate of depression. We also found that jitter, shimmer, energy and loudness feature groups are robust in characterising both read and spontaneous depressive speech. Remarkably, thin-slicing the read speech, using either the beginning of each sentence or the first few sentences performs better than using all reading task data.

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatically detect and assess depression. However, interpretation of these models and cues are rarely discussed in detail in the AI community, but have received increased attention lately. In this article, we aim to analyse the commonly selected features using a proposed framework of several feature selection methods and their effect on the classification results, which will provide an interpretation of the depression detection model. The developed framework aggregates and selects the most promising features for modelling depression detection from 38 feature selection algorithms of different categories. Using three real-world depression datasets, 902 behavioural cues were extracted from speech behaviour, speech prosody, eye movement and head pose. To verify the generalisability of the proposed framework, we applied the entire process to depression datasets individually and when combined. The results from the proposed framework showed that speech behaviour features (e.g. pauses) are the most distinctive features of the depression detection model. From the speech prosody modality, the strongest feature groups were F0, HNR, formants, and MFCC, while for the eye activity modality they were left-right eye movement and gaze direction, and for the head modality it was yaw head movement. Modelling depression detection using the selected features (even though there are only 9 features) outperformed using all features in all the individual and combined datasets. Our feature selection framework did not only provide an interpretation of the model, but was also able to produce a higher accuracy of depression detection with a small number of features in varied datasets. This could help to reduce the processing time needed to extract features and creating the model.