According to a report by the World Health Organization (WHO), around one in four people in the world is affected by a form of mental disorder. These disorders include depression, schizophrenia and, increasingly, dementia. While most of these conditions can be treated, a large number of people do not have access to any form of medical care. New technology developments are promising, however, as in many cases they can allow people to receive that care in a “decentralized” or virtual way. Virtual reality (VR) is already used to treat post-traumatic stress disorder (PTSD) and paranoia syndromes as well as depression. These treatments remain expensive, for the time being. As standalone headsets become available at a lower cost, however, there is scope for using these therapies at home.
One of the successful ways of treating PTSD is by using VR to make people relive their traumatic experiences time and time again, until they no longer feel any form of stress or anxiety. This technology has been used for war veterans who, equipped with headsets, revisit the dangerous zones they were deployed in, for instance. VR can also be used to treat depression and anxiety by enabling patients to experience a particularly pleasant and relaxing environment. For example, a VR programme allowing people to virtually swim in the sea with dolphins has been successfully implemented in some Californian hospitals.
In China, as well, there are plans for using VR to overcome a number of ailments. The technology is viewed as a way to overcome the strong cultural stigma attached to mental illness. A number of Chinese technology companies have launched programmes focused on psychiatric treatment in liaison with universities.
IEC has formed a joint technical committee with ISO, JTC 1, which prepares standards for information technology. One of its subcommittees publishes documents which specify the requirements for augmented reality (AR) and VR. IEC TC 110 publishes standards for electronic displays. One of its working groups has developed the first edition of IEC 63145-20-20, which establishes the measurement conditions for determining the image quality of eyewear displays.
To be able to treat mental illness, doctors need to first diagnose the condition. In many countries around the world, the stigma attached to mental ailments can make people unwilling to consult. Moreover, the lack of available healthcare in many areas of the globe means that potential patients are unable to consult, even if they want to. One of the ways to overcome both the stigma and the lack of healthcare specialists is by implementing video consulting, a form of telemedicine that uses technology to provide real-time visual and audio patient assessment at a distance. Consultations can take place over Skype or any other type of real time video link.
IEC TC 100 develops standards for audio, video and multimedia systems. The TC set up a technical area (TA) to address aspects of active assisted living (AAL), notably accessibility and usability and specific user interfaces related to audio, video and multimedia systems and equipment. Active assisted living technologies include systems and devices for supporting the well-being, health and care of disabled and older people. The TA liaises with the IEC System Committee on Active Assisted Living (SyC AAL), which focuses on the standardization of AAL products, services and systems to enable independent living for AAL users.
High-powered machine learning algorithms can be used to detect patterns of behaviour revealing depression and suicidal tendencies. A report, produced by scientists at USC, Carnegie Mellon University, and Cincinnati Children’s Hospital Medical Center, investigates non-verbal facial expressions in order to detect suicidal risks. It claims to have found a pattern that differentiates depressed and suicidal patients. The dataset used in the research comprises interviews with subjects from the Cincinnati Children’s Hospital Medical Center, the University of Cincinnati Medical Center and the Princeton Community Hospital.
The research analyses four critical facial expressions and behaviours : smiling, frowning, head movement and the raising of one’s eyebrows. The patients’ reactions were recorded on video and audio and then fed into a machine-learning algorithm, a supervised learning model known as support vector machine or SVM. Through the SVM prediction models, the research study revealed that smiling was the most vital feature for predicting suicidal cases when compared to frowning, eyebrow raising and head velocity. Patients who had less intense smiles – with no crinkling of the eyes for instance – were more likely to have suicidal tendencies. One of the lead authors of the study is John Pestian, professor in the divisions of Biomedical Informatics and Psychiatry at Cincinnati Children’s Hospital Medical Center. "The technology is not going to stop the suicide, the technology can only say: 'We have an issue over here'." Then we have to intervene and get a path to get to care, he is quoted as saying.
ISO/IEC JTC 1/SC 42 prepares standards for AI. IEC is also a co-founder of the Open Community for Ethics in Autonomous and Intelligent Systems (OCEANIS), which deals with key ethical issue relating to AI. While we are not quite there yet, vocal assistants in our mobile phones are expected, at some point in the near future, to detect suicidal tendencies by the sound of our voices or what we say, even in a cryptic fashion. And once they have detected these tendencies, they should give the right sort of advice. We all know how useful smartphones have become in our daily lives. In a few years from now, they might even play an active part in saving peoples’ lives.