Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

VOCAL BIOMARKERS OF CLINICAL DEPRESSION: WORKING TOWARDS AN INTEGRATED MODEL OF DEPRESSION AND SPEECH

Speech output has long been considered a sensitive marker of a person’s mental state. It has been previously examined as a possible biomarker for diagnosis and treatment response for certain mental health conditions, including clinical depression. To date, it has been difficult to draw robust conclusions from past results due to diversity in samples, speech material, investigated parameters, and analytical methods. Within this exploratory study of speech in clinically depressed individuals, articulatory and phonatory behaviours are examined in relation to psychomotor symptom profiles and overall symptom severity. A systematic review provided context from the existing body of knowledge on the effects of depression on speech, and provided context for experimental setup within this body of work. Examinations of vowel space, monophthong, and diphthong productions as well as a multivariate acoustic analysis of other speech parameters (e.g., F0 range, perturbation measures, composite measures, etc.) are undertaken with the goal of creating a working model of the effects of depression on speech. Initial results demonstrate that overall vowel space area was not different between depressed and healthy speakers, but on closer inspection, this was due to more specific deficits seen in depressed patients along the first formant (F1) axis. Speakers with depression were more likely to produce centralised vowels along F1, as compared to F2—and this was more pronounced for low-front vowels, which are more complex given the degree of tongue-jaw coupling required for production. This pattern was seen in both monophthong and diphthong productions. Other articulatory and phonatory measures were inspected in a factor analysis as well, suggesting additional vocal biomarkers for consideration in diagnosis and treatment assessment of depression—including aperiodicity measures (e.g., higher shimmer and jitter), changes in spectral slope and tilt, and additive noise measures such as increased harmonics-to-noise ratio. Intonation was also affected by diagnostic status, but only for specific speech tasks. These results suggest that laryngeal and articulatory control is reduced by depression. Findings support the clinical utility of combining Ellgring and Scherer’s (1996) psychomotor retardation and social-emotional hypotheses to explain the effects of depression on speech, which suggest observed changes are due to a combination of cognitive, psycho-physiological and motoric mechanisms. Ultimately, depressive speech is able to be modelled along a continuum of hypo- to hyper-speech, where depressed individuals are able to assess communicative situations, assess speech requirements, and then engage in the minimum amount of motoric output necessary to convey their message. As speakers fluctuate with depressive symptoms throughout the course of their disorder, they move along the hypo-hyper-speech continuum and their speech is impacted accordingly. Recommendations for future clinical investigations of the effects of depression on speech are also presented, including suggestions for recording and reporting standards. Results contribute towards cross-disciplinary research into speech analysis between the fields of psychiatry, computer science, and speech science

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer\u27s disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed

Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech

ABSTRACT Speech patterns are modulated by the emotional and neurophysiological state of the speaker. There exists a growing body of work that computationally examines this modulation in patients suffering from depression, autism, and post-traumatic stress disorder. However, the majority of the work in this area focuses on the analysis of structured speech collected in controlled environments. Here we expand on the existing literature by examining bipolar disorder (BP). BP is characterized by mood transitions, varying from a healthy euthymic state to states characterized by mania or depression. The speech patterns associated with these mood states provide a unique opportunity to study the modulations characteristic of mood variation. We describe methodology to collect unstructured speech continuously and unobtrusively via the recording of day-to-day cellular phone conversations. Our pilot investigation suggests that manic and depressive mood states can be recognized from this speech data, providing new insight into the feasibility of unobtrusive, unstructured, and continuous speech-based wellness monitoring for individuals with BP

THE SEMANTIC AND ACOUSTIC VOICE FEATURES DIFFERENTIATING NEUTRAL AND TRAUMATIC NARRATIVES

This dissertation is a quantitative and qualitative exploration of how one linguistically communicates emotions through an autobiographical narrative. Psycholinguistic research has affirmed that linguistic features of a narrative, including semantic and acoustic features, indicate a narrator’s emotions and physiological. This study investigated whether these linguistic features could help differentiate between trauma and neutral narratives and if they can predict autobiographical narratives’ subjective trauma ratings (STR). Qualitative analyses of the positive and negative evaluative statements were also conducted, which indicated the narrators’ thought processes during recall. Twenty-two Spanish-English college students participated in this study and narrated both traumatic and neutral narratives. We measured the narratives’ proportions of anger, fear, sadness, and joy emotion-related words and referential language. For acoustic analyses, we extracted narratives’ prosodic features, including, pitch, jitter, speaking speed, and acoustic energy, and cepstral features (I.e., MFCCs). Positive and negative evaluative statements were reliably coded and extracted from the narratives. Student’s T-tests showed that neutral and trauma narratives differed significantly in emotion-related semantic and MFCC-3. We tested the linguistic features\u27 ability to predict participants’ STR for both narrative types through separate Leave One Out Cross-Validation linear regressions, which can be used efficaciously on small sample-sizes. Several semantic and acoustic features predicted the neutral narratives’ STRs. In contrast, we could not produce a statistically viable model for predicting the trauma narratives’ STR. Analyses of the evaluative statements suggest that the trauma narratives had a unique signature of negative and positive statements – in addition to trauma statements having more negative evaluations. Limitations of this dissertation suggest that future research should use a more regimented methodology if aiming to analyze acoustic features. Nevertheless, these results, although tentative due to the small sample size, reinforce the importance of psycholinguistic analyses of narratives and have implications on how to assess people\u27s emotional states during psychotherapy. The dissertation finally encourages the broader use of narratives and linguistic analyses in clinical psychology to preserve, recognize, and ameliorate traumatic experiences

Depth of experiencing and use of emotion words in trauma narratives as performance indices of alexithymia.

The present study examined depth of experiencing and use of emotion words in trauma narratives as performance indices of alexithymia. The construct of alexithymia represents deficits in emotional awareness and regulation. The study employed archival data (written trauma narratives and self-report measures) collected from 60 female, undergraduate psychology students. Alexithymia was assessed using the Toronto Alexithymia Scale-20 (TAS; Bagby, Parker, & Taylor, 1994a, 1994b). Depth of experiencing was rated using the Client Experiencing Scale (EXP; Klien, Mathieu-Coughlan, & Kiesler, 1986). The Emotions Library (Mind Reading Emotions Library, 2004), a computer software program, was used to define emotion word for the emotion word count. Results of correlational analyses revealed that depth of experiencing was associated with alexithymia, while emotion word count and general vocabulary were not. Results of a hierarchical multiple regression revealed that depth of experiencing significantly predicted alexithymia, while emotion word count, trauma severity and length of narrative did not. Together, the results suggest that depth of experiencing can be used as a performance index of alexithymia.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .L4. Source: Masters Abstracts International, Volume: 45-01, page: 0480. Thesis (M.A.)--University of Windsor (Canada), 2006

A study of children’s bullying victimization in Xi’an: prevalence, risk factors, and correlation with family violence

Session G4 - Panel 47: Family Violence Poly-Victimization in ChinaOBJECTIVE: Despite there is evidence supporting a substantial overlap between family violence and bullying victimization (Baldry, 2003; Shields & Cicchetti, 2001), few studies have been conducted to examine the co-occurrence or correlation between these two kinds of child victimization. This study aims to present an estimate of prevalence and risk factors of bullying victimization in a wide range of settings (school, street, and internet) and examine its association with family violence victimization in a cohort of Chinese children in Xi’an. METHOD: Using a three-stage stratified sampling design, a total of 2000 students were randomly selected from 24 schools in 3 districts (from both urban and rural areas) in Xi’an, China. Questionnaires were self-administrated by children subjects. The Juvenile Victimization Questionnaire (JVQ) and Relational Aggression Scale were used as measures for direct and relational bullying victimization. Family violence includes four types: child abuse, witnessing spouse violence, elder abuse, and in-law conflict. RESULTS: The prevalence rate and the pattern of child bullying victimization will be computed and reported. The correlation between bullying victimization and family violence will be examined by regression analyses. CONCLUSION: The patterns of child bullying victimization occurred in different settings will be compared and discussed.postprin

Child poly-victimization and Intimate Partner Violence in Wuhan, China

Session G4 - Panel 47: Family Violence Poly-Victimization in ChinaThis study is to examine the prevalence and correlates of child poly-victimization, and to investigate if IPV is a factor associated with child poly-victimization in a cohort of Chinese families. Using a multi-stage stratified sampling, about 1,000 households with children aged 0-17 were randomly sampled in the Wuhan city. Of each family, one of the parents or guardians will be invited to participate in the study. The Juvenile Victimization Questionnaire (JVQ) and the Revised Conflict Tactics Scales (CTS2) were employed as the major tools for measuring child victimization and IPV respectively. The data collection is still in progress and will be completed in June 2010. The prevalence rate of and the risk factors for child poly-victimization in Wuhan, China will be analyzed. Holding an assumption that inter-parental violence negatively impacts the family system, this study hypothesizes that children in families with IPV are more likely to experience poly-victimization compared with those whose parents are nonviolent. Thus, IPV as a factor associated with child polyvictimization will be tested using regression analyses. The findings will implicate that identification of child victims should be extended to cover multiple types of victimization, and thus screening of family violence should involve all family members.postprin

Child poly-victimization in Hong Kong

Session A1 - Panel 61: Youth Victimization in 3 Countries Using the NatSCEV Assessment ModelThis study is to examine the prevalence and correlates of child poly-victimization in a cohort of Chinese families in Hong Kong. The study is collecting epidemiological data through representative school and household surveys. From the school survey, a cohort of about 6,000 secondary students aged 15-17 will be interviewed in schools. From the household survey, a cohort of about 2,500 parents or guardians of children aged 0 – 17 and about 1,000 young persons aged 15 – 17 drawn from large representative samples from households in Hong Kong will be interviewed at home. The Juvenile Victimization Questionnaire (JVQ) is employed as the major tool for measuring child victimization. The data collection is still in progress and will be completed in June 2010.The prevalence rate of and the risk factors for child poly-victimization collected through the school and household surveys will be analyzed and compared. The findings will also be compared with data collected in US and UK. Universal and culture-specific factors will be identified. The findings will implicate that identification of child victims using JVQ in different cultures.postprin

Study of family violence poly-victimization in China: design and preliminary findings

Session G4 - Panel 47: Family Violence Poly-Victimization in ChinaThe population survey adopts a combined approach in using school and household-based approaches. The school-based approach is more appropriate to gather information from children aged 15 – 17 to estimate the prevalence of child victimization experienced by them. The household-based approach, with both children aged 15 – 17 and parents with children aged below 18 were interviewed, is adopted. The household approach is the only viable option for interviewing parents and to collect information on child victimization experienced by children under age 15. The primary objective of this approach is to collect data to better estimate of prevalence rates of CSA and child victimization experienced by children under age 15, and to test a comprehensive profile of individual and family risk factors correlating to CSA and child victimization. From the school survey, a cohort of about 13,000 secondary students aged 15-17 will be interviewed in schools. From the household survey, a cohort of about 5,000 parents or guardians of children aged 0 – 17 and about 2,000 young persons aged 15 – 17 drawn from large representative samples from households in Hong Kong and 5 Mainland provinces will be interviewed face-to-face by trained interviewers. Data collection will be completed in June, 2010. Prevalence rates of IPV, elder abuse, in-law violence and child poly-victimization will be presented.postprin