ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment Recent studies show that the elderly population has increased considerably in European society in recent years. This fact has led the European Union and many countries to propose new policies for caring services directed to this group. The current trend is to promote the care of the elderly in their own homes, thus avoiding inverting resources on residences. With this in mind, there are now new solutions in this direction, which try to make use of the continuous advances in computer science. This paper tries to advance in this area by proposing the use of a personal assistant to help older people at home while carrying out their daily activities. The proposed personal assistant is called ME3CA, and can be described as a cognitive assistant that offers users a personalised exercise plan for their rehabilitation. The system consists of a sensorisation platform along with decisionmaking algorithms paired with emotion detection models. ME3CA detects the usersxe2x80x99 emotions, which are used in the decisionmaking process allowing for more precise suggestions and an accurate (and unbiased) knowledge about the usersxe2x80x99 opinion towards each exercise.

Learning Perceived Emotion Using Affective and Deep Features for Mental Health Applications Virtual agents are being increasingly used in the areas of healthcare, treatments, and therapy. Virtual agents with emotional intelligence have shown the potential to be applicable to deliver mental health intervention and facilitate therapies for children on the autism spectrum. To build an emotionally intelligent agent, automatic recognition of emotions is an essential building block. For mental health therapies and treatments, detecting rapidly changing emotions of individuals can be useful to know whether the patients are showing appropriate emotional response or not. To this end, we present a new datadriven approach to identify the perceived emotions of individuals based on their walking styles. We extract an individualxe2x80x99s walking gait in the form of a sequence of 3D poses given an RGB video of himher walking. We leverage the gait features to classify the perceived emotional state of the individual into one of four categories: happy, sad, angry, or neutral. First, we use an LSTM network to extract deep features of the gait using labeled emotion datasets. Next, we compute the affective features of the gaits using posture and movement cues. We combine these affective features with the deep features and classify them using a Random Forest Classifier. We observe that this approach provides an accuracy of 80:07% in identifying the perceived emotions. Additionally, we present a new dataset consisting of videos of walking individuals, their extracted gaits, and perceived emotion labels associated with each gait. We refer to this dataset as the xe2x80x98EWalk (Emotion Walk)xe2x80x9d dataset.