Managing pain through federated affective computing

Abstract

For a long time, the primary approach to control pain in patients involved using specially designed drugs. While these drugs have often proved to be sufficient to reduce pain perception in patients of all ages, they do not come without any potential side effects. The prolonged use of such drugs can have adverse effects on a patient's health, namely through the development of tolerance and resistance to medication. This research explores a shift away from such practices. It proposes using technology as a safe adjunct tool that, together with a reduced need for specialized medication, helps patients cope with pain. This study explores the adoption of Affective Computing as a crucial element in administering effective Distraction Therapy to shift patients’ attention away from pain and onto more pleasant thoughts. Described as the study of applying human-like capabilities to machines, affect-enabled software can elicit, understand, and, more importantly, react to human emotion. While interest in the field of Affective Computing has grown considerably over the years, the implementation of such software is often faced with obstacles that threaten the successful creation of this kind of software. Affect-enabled software, as with any other kind of software requiring a degree of Machine learning, can be considered highly data-hungry. In closed-loop Affective Computing implementations, significantly large bodies of data train accurate models upon which they make informed decisions. Among the list of issues that have for long hampered the development of intelligent but data-hungry software is the lack of sufficiently large data collections. In addition, given that one is dealing with affect, training one static model to be used by multiple individuals might not return the ideal results because, as humans, we react in different ways when faced with different scenarios. This research presents the Federated Affective Computing concept. Federated Affective Computing brings together Affective Computing and Federated Learning to overcome the lack of data hampering the development of affect-enabled platforms. The result is a framework that, while overcoming such issues, makes possible the creation of affect enabled platforms that can autonomously continuously retrain their models to remain relevant. By introducing Federated Learning using Evolutionary Aggregation (FLEA) approach, this research shows that Federated Affective Computing makes it possible for affect-enabled systems to learn quickly and perform accurate decision-making when limited data is available for training. More importantly, however, this research shows that by adopting Federated Affective Computing, affect-enabled platforms can be effectively deployed in sensitive scenarios, such as pain management. Through the adoption of Federated Affective Computing as part of the Morpheus case study, this research shows the adoption of Federated Affective Computing to be an effective tool that can take the administration of Distraction Therapy using technology-based approaches to the next level. The current state-of-the-art approach, involving non-affect-enabled software to distract patients from pain, recorded a drop of 50% in pain scores amongst a test population. This research shows that the adoption of the presented Federated Affective Computing approach led to an average 17% improvement in patient pain tolerance over the current state of the art and, in some cases, even reaching up to a 30% increase.