References
The serious nature of the problem specific to acquired disabilities and TBI:
“There appears to be about a three to fourfold increased risk of suicide after TBI.” The neuropsychiatry of depression after brain injury. Neuropsychological Rehabilitation, 2003 Jan-Mar;13(1-2):65-87.
“People with traumatic brain injury (TBI) have an increased risk of suicide, suicide attempts and suicide ideation compared with the general population.” Preventing suicide after traumatic brain injury: implications for general practice. Med. J. Australia, 2007 Aug 20;187(4):229-32
“Research among a wide range of cohorts (e.g. civilian, military) has increasingly highlighted traumatic brain injury (TBI) as a risk factor for suicidal thoughts and behaviors, including death by suicide.” Suicide and traumatic brain injury: a review by clinical researchers from the National Institute for Disability and Independent Living Rehabilitation Research (NIDILRR) and Veterans Health Administration Traumatic Brain Injury Model Systems. Current Opinion in Psychology, Vol 22, August 2018, Pages 73-78.
“Recent research indicates a heightened risk of suicide in this population… The suicide rate after acquiring a physical disability, such as a spinal cord injury, and the greater odds of suicide after reporting having a disability further support the association between physical disability and suicide.” Physical disability and suicide: recent advancements in understanding and future directions for consideration, Physical disability and suicide: recent advancements in understanding and future directions for consideration. Current Opinion in Psychology, Vol 22, August 2018, Pages 18-22.
“Recently developed real-time monitoring methods are creating new opportunities for scientific and clinical advances. For instance, recent real-time monitoring studies of suicidal thoughts show that they typically are episodic, with quick onset and short duration. Many known risk factors that predict changes in suicidal thoughts over months/years (e.g. hopelessness) do not predict changes over hours/days — highlighting the gap in our abilities for short-term prediction. Current and future studies using newer streams of data from smartphone sensors (e.g. GPS) and wearables (e.g. heart rate) are further expanding knowledge and clinical possibilities.” Real-time assessment of suicidal thoughts and behaviors. Current Opinion in Psychology, Vol 22, August 2018, Pages 33-37.
“Patients with traumatic brain injuries may be at increased risk for suicide. In comparison to the general population TBI survivors are at increased risk for suicide ideation (Simpson and Tate, 2002), suicide attempts (Silver et al. 2001) and suicide completions (Teasdale and Engberg, 2001). TBI-related sequelae can be enduring and may include motor disturbances, sensory deficits, and psychiatric symptoms (such as depression, anxiety, psychosis, and personality changes) as well as cognitive dysfunction. These cognitive impairments include impaired attention, concentration, processing speed, memory, language and communication, problem solving, concept formation, judgment, and initiation. Another important TBI sequelae that contributes to suicidal risk is the frequent increase in impulsivity. These impairments may lead to a life-long increased suicide risk which requires constant attention.” Suicidality after traumatic brain injury: demographic, injury and clinical correlates. Psychological Medicine, 32, 687-697.
“Individuals with a history of traumatic brain injury have significantly higher occurrence for psychiatric disorders and suicide attempts in comparison with those without head injury and have a poorer quality of life.” The association between head injuries and psychiatric disorders: findings from the New Haven NIMH Epidemiological Catchment Area Study. Brain Injury, 15, 11, 935-945.
“The increased risk of suicide among patients who had a mild traumatic brain injury may result from concomitant risk factors such as psychiatric conditions and psychosocial disadvantage. The greater risk among the more serious cases implicates additionally the physical, psychological, and social consequences of the injuries as directly contributing to the suicides.” Suicide after traumatic brain injury: A population study. The Journal of Neurology, Neurosurgery, and Psychiatry, 71, (4), 436-440.
Machine Learning Techniques:
Gene Selection for Cancer Classification using Support Vector Machines. https://link.springer.com/article/10.1023/A:1012487302797
A Network of Localized Linear Discriminants. https://papers.nips.cc/paper/525-a-network-of-localized-linear-discriminants
Localized Support Vector Machines for Classification. http://ieeexplore.ieee.org/document/1716177/
Large Margin Classification Using the Perceptron Algorithm. https://link.springer.com/article/10.1023/A:1007662407062
Local linear perceptrons for classification. http://ieeexplore.ieee.org/document/501737/
A Survey on Concept Drift Adaptation. http://eprints.bournemouth.ac.uk/22491/1/ACM%20computing%20surveys.pdf
Handling Concept Drift in Medical Applications: Importance, Challenges, and Solutions. http://www.win.tue.nl/~mpechen/talks/cbms2010_Tutorial3.pdf
Using unsupervised incremental learning to cope with gradual concept drift. http://www.math.tau.ac.il/~nin/papers/HadasConnSci2011.pdf
Twenty Years of Mixture of Experts. http://ieeexplore.ieee.org/abstract/document/6215056/
Multi-view classification of psychiatric conditions based on saccades. https://www.sciencedirect.com/science/article/pii/S1568494615001398
Notes on “Deep Scattering Spectrum”. https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/ListenSemester2201314/ghenter_deep_scattering_slides_rev.pdf
Invariant Scattering Convolution Networks. https://arxiv.org/pdf/1706.08818.pdf
Deep Roto-Translation Scattering for Object Classification. https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oyallon_Deep_Roto-Translation_Scattering_2015_CVPR_paper.pdf
DI ENS deep scattering transform research: http://www.di.ens.fr/data/scattering/
Multi-modality, smartphones, and therapy:
A Systematic Assessment of Smartphone Tools for Suicide Prevention. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830444/
A Software Shrink: Apps and Wearables Could Usher In an Era of Digital Psychiatry. https://spectrum.ieee.org/consumer-electronics/portable-devices/a-software-shrink-apps-and-wearables-could-usher-in-an-era-of-digital-psychiatry
Electronic self-monitoring of mood using IT platforms in adult patients with bipolar disorder: A systematic review of the validity and evidence. https://www.ncbi.nlm.nih.gov/pubmed/26769120
Emotion recognition using mobile phones. 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)
Empath: a continuous remote emotional health monitoring system for depressive illness. Proceedings of the 2nd Conference on Wireless Health (WH ‘11). ACM, New York, NY, USA, Article 5
Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4997004/
Moodscope: building a mood sensor from smartphone usage patterns. http://www.ruf.rice.edu/~mobile/publications/likamwa2013mobisys2.pdf Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services; 25-28 June 2013; Taipei, Taiwan
“According to the 7-38-55 Rule of Personal Communication, words influence only 7% of our perception of affective state. Tone of voice and body language contribute to 38% and 55% of personal communication, respectively.” https://www.affectiva.com/emotion-ai-overview/
Consideration of Multiple Components of Emotions in Human-Technology Interaction. Affect and Emotion in Human-Computer Interaction. Lecture Notes in Computer Science, vol 4868. Springer-Verlag
Emotion Recognition through Multiple Modalities: Face, Body Gesture, Speech. Affect and Emotion in Human-Computer Interaction. Lecture Notes in Computer Science, vol 4868. Springer-Verlag
The Composite Sensing of Affect. Affect and Emotion in Human-Computer Interaction. Lecture Notes in Computer Science, vol 4868. Springer-Verlag
Emotion recognition in human–computer interaction. Neural Networks, Volume 18, Issue 4, 2005, Pages 389-405.
Sentiment Analysis. https://nlp.stanford.edu/sentiment
What’s the difference between apps we cherish vs. regret? http://www.timewellspent.io/app-ratings
Emotion recognition from speech:
A review of depression and suicide risk assessment using speech analysis. http://www.sciencedirect.com/science/article/pii/S0167639315000369
https://www.affectiva.com/product/emotion-api-speech/
Automatic Recognition of Emotions from Speech: A Review of the Literature and Recommendations for Practical Realisation. Affect and Emotion in Human-Computer Interaction Lecture Notes in Computer Science, vol 4868. Springer-Verlag
Ensemble methods for spoken emotion recognition in call-centres. Speech Communication, Volume 49, Issue 2, 2007, Pages 98-112
Emotion recognition from speech: a review. Int J Speech Technol (2012) 15: 99
Human affective (emotion) behaviour analysis using speech signals: A review. 2012 International Conference on Biomedical Engineering (ICoBE)
Recognizing emotion in speech. Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference
Classical and Novel Discriminant Features for Affect Recognition from Speech. http://affect.media.mit.edu/pdfs/05.fernandez-picard.pdf
Emotion recognition from facial expression:
Investigating Facial Behavior Indicators of Suicidal Ideation. https://www.cl.cam.ac.uk/~tb346/pub/papers/fg2017_suicid.pdf
Technology for Just-In-Time In-Situ Learning of Facial Affect for Persons Diagnosed with an Autism Spectrum Disorder.
http://affect.media.mit.edu/pdfs/08.Madsen-etal-ASSETS.pdf
Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures. http://www.cl.cam.ac.uk/~pr10/publications/rtv4hci05.pdf
An intelligent system for facial emotion recognition. 2005 IEEE International Conference on Multimedia and Expo, 2005
Other modalities:
The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129056
Heart Rate Variability Using the Phone’s Camera. https://www.marcoaltini.com/blog/heart-rate-variability-using-the-phones-camera
Detecting Human Emotions Using Smartphone Accelerometer Data. http://www.mn.uio.no/ifi/studier/masteroppgaver/robin/andreasferovigolsen-masteroppgave.pdf
Emotion-Recognition Using Smart Watch Accelerometer Data: Preliminary Findings. https://arxiv.org/pdf/1709.09148.pdf