Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry

• URL: http://arxiv.org/abs/2207.04724v1
• Date: Mon, 11 Jul 2022 09:07:08 GMT
• Title: Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry
• Authors: Flavia D. Frumosu, Nicole N. L{\o}nfeldt, A.-R. Cecilie Mora-Jensen, Sneha Das, Nicklas Leander Lund, A. Katrine Pagsberg, Line K. H. Clemmensen
• Abstract summary: We use machine learning to derive behavioral codes or concepts of a gold standard behavioral rating system. Our ratings were comparable to human expert ratings for negative emotions, activity-level/arousal and anxiety.
• Score: 3.975358343371988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Observation is an essential tool for understanding and studying human behavior and mental states. However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk. Machine learning (ML) methods offer ways to improve reliability, decrease cost, and scale up behavioral coding for application in clinical and research settings. Here, we use computer vision to derive behavioral codes or concepts of a gold standard behavioral rating system, offering familiar interpretation for mental health professionals. Features were extracted from videos of clinical diagnostic interviews of children and adolescents with and without obsessive-compulsive disorder. Our computationally-derived ratings were comparable to human expert ratings for negative emotions, activity-level/arousal and anxiety. For the attention and positive affect concepts, our ML ratings performed reasonably. However, results for gaze and vocalization indicate a need for improved data quality or additional data modalities.

Related papers

• Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
  We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
  arXiv  Detail & Related papers  (2024-05-15T20:41:46Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping [0.966840768820136]
  We propose a machine learning system capable of predicting, detecting, and explaining individual changes in symptoms of patients with Schizophrenia. The system detects decreases in symptoms using changepoint algorithms and uses counterfactual explanations as a recourse in a simulated continuous monitoring scenario in healthcare.
  arXiv  Detail & Related papers  (2023-06-06T19:33:03Z)
• Language-Assisted Deep Learning for Autistic Behaviors Recognition [13.200025637384897]
  We show that a vision-based problem behaviors recognition system can achieve high accuracy and outperform the previous methods by a large margin. We propose a two-branch multimodal deep learning framework by incorporating the "freely available" language description for each type of problem behavior. Experimental results demonstrate that incorporating additional language supervision can bring an obvious performance boost for the autism problem behaviors recognition task.
  arXiv  Detail & Related papers  (2022-11-17T02:58:55Z)
• What's on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving [55.41644538483948]
  We analyze the effects of mental workload and perceptual load on psychophysiological dimensions. We classify the mental and perceptual load levels through the fusion of these measurements. We report up to 89% mental workload classification accuracy and provide a real-time minimally-intrusive solution.
  arXiv  Detail & Related papers  (2022-08-10T21:19:49Z)
• Vision-Based Activity Recognition in Children with Autism-Related Behaviors [15.915410623440874]
  We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames.
  arXiv  Detail & Related papers  (2022-08-08T15:12:27Z)
• Computational behavior recognition in child and adolescent psychiatry: A statistical and machine learning analysis plan [3.975358343371988]
  We aim to automate coding of human behavior for use in psychotherapy and research with the help of artificial intelligence (AI) tools. Videos of a gold-standard semi-structured diagnostic interview of 25 youth with obsessive-compulsive disorder (OCD) and 12 youth without a psychiatric diagnosis (no-OCD) will be analyzed.
  arXiv  Detail & Related papers  (2022-05-11T19:12:15Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)
• Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning [5.101123537955207]
  Motor behaviour analysis provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. We introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement.
  arXiv  Detail & Related papers  (2020-12-16T20:07:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.