In a world where technological advancements are progressing at a vertiginous pace, social networks, online games, virtual worlds, streaming services, and remote work are part of everyday life. This is the case for the work environment, with the use of technological tools and home offices. In contrast, harmful aspects have been amplified, such as stress that affects occupational health. Lately, considerable interest has been gained in the affective domain in improving the occupational situation using empathic responses. In this work, we study the effect of machine empathic responses such as blue light, relaxing music, and the combination of light and music on people performing stressful tasks in an occupational environment. Thirty five participants tested different stimuli, eleven tested the music condition, twelve the light effect, and another twelve the combination of light and music. The monitoring of the heart rate variability along with psychological measures show that empathic responses can help reduce humans stress levels.

The communication between humans and artificial agents is becoming crucial and significant in daily life, especially with the advancements in the fields of human-robot and human-computer interaction. For these artificial agents to be recognized as social beings, they should exhibit emotional and empathic behaviors. However, there is no global agreement on measuring the empathic capabilities of these agents. For this reason, the scientific community has paid a significant focus on developing a standardized metric to perceive artificial agents' empathy. In this regard, this article provides a discussion on challenges in artificial empathy evaluation and researches the developments to discuss the factors and recommendations to design a globally accepted metric. It also discusses the qualities required for a globally accepted and standardized metric. Finally, an adaptation to an existing questionnaire is proposed for the evaluation of empathy in artificial agents.

Conversational agents are getting increasingly popular and find applications in health and customer services. Conversations in these fields are often emotionally charged. It is, therefore, necessary to handle the conversation with some degree of empathy to be effective. In this work, we leverage advances in the field of natural language processing to create a dialogue system that can convincingly generate empathic responses to text-based messages. To improve the system's ability to converse with empathy, we train the language model on empathic conversations and inject additional emotional information in the response generation. We propose two chatbots: a benchmark bot and an empathic bot. Additionally, we implement an emotion classifier that allows us to predict the emotional state of text-based messages. We evaluate both chatbots in quantitative studies and compare them with human responses in qualitative studies involving human judges. Our evaluation shows that our empathic chatbot outperforms the benchmark bot and even the human-generated responses in terms of perceived empathy. Additionally, we achieve state-of-the-art results in terms of response quality using transformer-based language models. Finally we report that we can double the initial performance of the emotion classifier using undersampling techniques, yielding a final F1-score of 0.81 in six basic emotions.

Humans nowadays are tending to spend too much time in front of their screens. Direct interaction between humans is falling in numbers and people are losing their empathic behaviour. By integrating empathy and emotions in everyday objects researchers can address this problem. In addition we can have a positive effect on our lives, from physical and mental health through tackling many issues, like productivity, time wasting, stress and other problems. In this article, we tackle the productivity problem by presenting the Empathic Flower Companion (EFC) that will be using the expression of emotions to help the human through their working day. It will be monitoring their time and at the same time analysing the websites they will be surfing. The concept proposed will reduce the time wasted on unproductive websites. The results show an increase of 15% in the productivity of the testers, which shows that EFC was effective in reducing the amount of time wasted.

Is it helpful for a medical physical health chatbot to show empathy? How can a chatbot show empathy only based on short-term text conversations? We have investigated these questions by building two different medical assistant chatbots with the goal of providing a diagnosis for physical health problem to the user based on a short conversation. One chatbot was advice-only and asked only the necessary questions for the diagnosis without responding to the user's emotions. Another chatbot, capable of showing empathy, responded in a more supportive manner by analyzing the user's emotions and generating appropriate responses with a high empathic accuracy. Using the RoPE scale questionnaire for empathy perception in a human-robot interaction, our empathic chatbot was rated significantly better in showing empathy and was preferred by a majority of the preliminary study participants (N=12).