New telecare approach based on 3D convolutional neural network for estimating quality of life

Abstract

Quality of life (QoL) is an effective index of well-being, including physical health, aspect of social activity, and mental state of individuals. A new approach that uses a deep-learning architecture to estimate the score of a user's QoL is presented. This system was built using a combination of a 3D convolutional neural network and a support vector machine for multimodal data. In order to evaluate the accuracy of the estimation system, three experiments were conducted. Before these experiments, ten hours of audio and video data were collected from healthy participants during a natural-language conversation with a conversational agent we implemented. In the first experiment, the QoL question-answer estimation experiment, the accuracy of “Physical functioning,” which is one of the eight scales that constitute QoL, reached 84.0%. In the second experiment, the QoL-score-regression experiment, in which the scores of each scale were directly estimated, the distribution of the difference between the actual score and the estimated results, known as error, was investigated. These results imply that the features necessary for QoL estimation can be extracted from audio and video data, except for the “Mental Health” domain. One of the reasons why it was difficult to estimate the “Mental Health” scale may be that the learning framework could not extract an appropriate feature for estimation. Therefore, we estimated “Mental Health” by focusing on eye movement. From the result, it was proven that estimation is possible, and the proposed system using multimodal data demonstrated its effectiveness for estimation for all eight scales that constitute QoL and for extracting high-dimensional information regarding the QoL of a human, including their satisfaction level towards daily life and social activities. Finally, suggestions and discussions regarding the plausible behavior of the estimation results were made from the viewpoint of human–agent interaction in the field of elderly welfare.