Dealing with a Missing Sensor in a Multilabel and Multimodal Automatic Affective States Recognition System

Abstract

Data from multiple sensors can boost the automatic recognition of multiple affective states in a multilabel and multimodal recognition system. At any time, the streaming from any of the contributing sensors can be missing. This work proposes a method for dealing with a missing sensor in a multilabel and multimodal automatic affective states recognition system. The proposed method, called Hot Deck using Conditional Probability Tables (HD-CPT), is incorporated into a multimodal affective state recognition system for compensating the loss of a sensor using the recorded historical information of the sensor and its interaction with the other available sensors. In this work, we consider a multilabel classifier, named Circular Classifier Chain, for the automatic recognition of four states: tiredness, anxiety, pain, and engagement; combined with a multimodal classifier based on three sensors: fingers pressure, hand movements, and facial expressions; which was adapted for coping with the problem of a missing sensor in a virtual rehabilitation platform for post-stroke patients. A dataset of five post-stroke patients who attended ten longitudinal rehabilitation sessions was used for the evaluation. The inclusion of HD-CPT compensated for the loss of one sensor with results above those obtained with only the remaining sensors available. HD-CPT prevents the system from collapsing when a sensor fails, providing continuity of operation with results that attenuate the loss of the sensor. The proposed method HD-CPT can provide robustness for the naturalistic everyday use of an affective states recognition system.