Interpretation of Depression Detection Models via Feature Selection Methods.

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 study, 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.

Deploying a robotic positive psychology coach to improve college students' psychological well-being.

Despite the increase in awareness and support for mental health, college students' mental health is reported to decline every year in many countries. Several interactive technologies for mental health have been proposed and are aiming to make therapeutic service more accessible, but most of them only provide one-way passive contents for their users, such as psycho-education, health monitoring, and clinical assessment. We present a robotic coach that not only delivers interactive positive psychology interventions but also provides other useful skills to build rapport with college students. Results from our on-campus housing deployment feasibility study showed that the robotic intervention showed significant association with increases in students' psychological well-being, mood, and motivation to change. We further found that students' personality traits were associated with the intervention outcomes as well as their working alliance with the robot and their satisfaction with the interventions. Also, students' working alliance with the robot was shown to be associated with their pre-to-post change in motivation for better well-being. Analyses on students' behavioral cues showed that several verbal and nonverbal behaviors were associated with the change in self-reported intervention outcomes. The qualitative analyses on the post-study interview suggest that the robotic coach's companionship made a positive impression on students, but also revealed areas for improvement in the design of the robotic coach. Results from our feasibility study give insight into how learning users' traits and recognizing behavioral cues can help an AI agent provide personalized intervention experiences for better mental health outcomes.

A Robotic Companion for Psychological Well-being: A Long-term Investigation of Companionship and Therapeutic Alliance.

Social support plays a crucial role in managing and enhancing one's mental health and well-being. In order to explore the role of a robot's companion-like behavior on its therapeutic interventions, we conducted an eight-week-long deployment study with seventy participants to compare the impact of (1) a control robot with only assistant-like skills, (2) a coach-like robot with additional instructive positive psychology interventions, and (3) a companion-like robot that delivered the same interventions in a peer-like and supportive manner. The companion-like robot was shown to be the most effective in building a positive therapeutic alliance with people, enhancing participants' well-being and readiness for change. Our work offers valuable insights into how companion AI agents could further enhance the efficacy of the mental health interventions by strengthening their therapeutic alliance with people for long-term mental health support.

Evaluating and Validating Emotion Elicitation Using English and Arabic Movie Clips on a Saudi Sample.

With the advancement of technology in both hardware and software, estimating human affective states has become possible. Currently, movie clips are used as they are a widely-accepted method of eliciting emotions in a replicable way. However, cultural differences might influence the effectiveness of some video clips to elicit the target emotions. In this paper, we describe several sensors and techniques to measure, validate and investigate the relationship between cultural acceptance and eliciting universal expressions of affect using movie clips. For emotion elicitation, a standardised list of English language clips, as well as an initial set of Arabic video clips are used for comparison. For validation, bio-signal devices to measure physiological and behavioural responses associated with emotional stimuli are used. Physiological and behavioural responses are measured from 29 subjects of Arabic background while watching the selected clips. For the six emotions' classification, a multiclass SVM (six-class) classifier using the physiological and behavioural measures as input results in a higher recognition rate for elicited emotions from Arabic video clips (avg. 60%) compared to the English video clips (avg. 52%). These results might reflect that using video clips from the subjects' culture is more likely to elicit the target emotions. Besides measuring the physiological and behavioural responses, an online survey was carried out to evaluate the effectiveness of the selected video clips in eliciting the target emotions. The online survey, having on average 220 respondents for each clip, supported the findings.

The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras.

Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.

Cross-Cultural Detection of Depression from Nonverbal Behaviour.

Millions of people worldwide suffer from depression. Do commonalities exist in their nonverbal behavior that would enable cross-culturally viable screening and assessment of severity? We investigated the generalisability of an approach to detect depression severity cross-culturally using video-recorded clinical interviews from Australia, the USA and Germany. The material varied in type of interview, subtypes of depression and inclusion healthy control subjects, cultural background, and recording environment. The analysis focussed on temporal features of participants' eye gaze and head pose. Several approaches to training and testing within and between datasets were evaluated. The strongest results were found for training across all datasets and testing across datasets using leave-one-subject-out cross-validation. In contrast, generalisability was attenuated when training on only one or two of the three datasets and testing on subjects from the dataset(s) not used in training. These findings highlight the importance of using training data exhibiting the expected range of variability.