Empathic chatbot response for medical assistance

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)

Emotional paraphrasing using pre-trained language models

Emotion style transfer is a recent and challenging problem in Natural Language Processing (NLP). Transformer-based language models are becoming extremely powerful, so one wonders if it would be possible to leverage them to perform emotion style transfer. So far, previous work has not used transformer-based models for this task. To address this task, we fine-tune a GPT-2 model with corrupted emotional data. This will train the model to increase the emotional intensity of the input sentence. Coupled with a paraphrasing model, we develop a system capable of transferring an emotion into a paraphrase. We conducted a qualitative study with human judges, as well as a quantitative evaluation. Although the paraphrase metrics show poor performance compared to the state of the art, the transfer of emotion proved to be effective, especially for the emotions fear, sadness, and disgust. The perception of these emotions were improved both in the automatic and human evaluations. Such technology can significantly facilitate the automatic creation of training sentences for natural language understanding (NLU) systems, but it can also be integrated into an emotional or empathic dialogue architecture

Empathic and empathetic systematic review to standardize the development of reliable and sustainable empathic systems

Empathy plays a crucial role in human life, and the evolution of technology is affecting the way humans interact with machines. The area of affective computing is attracting considerable interest within the human–computer interaction community. However, the area of empathic interactions has not been explored in depth. This systematic review explores the latest advances in empathic interactions and behaviour. We provide key insights into the exploration, design, implementation, and evaluation of empathic interactions. Data were collected from the CHI conference between 2011 and 2021 to provide an overview of all studies covering empathic and empathetic interactions. Two authors screened and extracted data from a total of 59 articles relevant to this review. The features extracted cover interaction modalities, context understanding, usage fields, goals, and evaluation. The results reported here can be used as a foundation for the future research and development of empathic systems and interfaces and as a starting point for the gaps found

Empathic response generation in chatbots

Recent years show an increasing popularity of chatbots, with latest efforts aiming to make them more empathic and humanlike, finding application for example in customer service or in treating mental illnesses. Thereby, emphatic chatbots can understand the user’s emotional state and respond to it on an appropriate emotional level. This survey provides an overview of existing approaches used for emotion detection and empathic response generation. These approaches raise at least one of the following profound challenges: the lack of quality training data, balancing emotion and content level information, considering the full end-to-end experience and modelling emotions throughout conversations. Furthermore, only few approaches actually cover response generation. We state that these approaches are not yet empathic in that they either mirror the user’s emotional state or leave it up to the user to decide the emotion category of the response. Empathic response generation should select appropriate emotional responses more dynamically and express them accordingly, for example using emojis

Bobby ::un chatbot qui utilise la musique pour améliorer vos émotions

La musique utilisée en thérapie présente de nombreux avantages. En effet c’est une activité quotidienne qui ne nécessite aucune formation et a de nombreux impacts sur nos émotions. Elle peut les changer, les améliorer et les influencer. Un autre avantage en ce qui concerne l’accessibilité et le coût des séances thérapeutiques sont les chatbots proposant une thérapie. Récemment, les chatbots ont commencé non seulement à être utilisés en thérapie mais également à prouver leur efficacité. Sur ces bases, nous avons conçu Bobby, notre chatbot proposant de la musique pour améliorer les émotions. Le dialogue est basé sur le déroulement de la thérapie et suit donc les trois phases : exploration, insight et action. La structure de ce dialogue suit la logique d’un chatbot, proposant l’art thérapie, déjà créée : SERMO. Bobby pose des questions sur l’humeur, l’état émotionnel, la musique et enfin, passe à l’activité musicale. La musique proposée par le chatbot est tirée d’une base de données contenant des morceaux émotionnellement connotés et empiriquement validés. Les musiques sont proposées par rapport à l’état émotionnel de la personne et selon qu’elle souhaite le changer ou le renforcer. Quatorze participant-e-s ont testé le chatbot. Aucune analyse statistique n’est encore effectuée, mais les résultats préliminaires montrent un effet positif sur le groupe souhaitant renforcer son émotion ou état émotionnel. Bien qu’aucun effet n’ait été observé dans le groupe souhaitant changer leur émotion ou état émotionnel, dire que le chatbot n’est pas efficace n’est pas possible. En effet il faudra tester une plus grande population afin de pouvoir mener de réelles analyses statistiques et pouvoir tirer les conclusions des résultats obtenus

Natural Language Understanding (NLU) on the Edge

Today, chatbots have evolved to include artificial intelligence and machine learning, such as Natural Language Understanding (NLU). NLU models are trained and run on remote servers because the resource requirements are large and must be scalable. However, people are increasingly concerned about protecting their data. To be efficient, the current NLU models use the latest technologies, which are increasingly large and resource-intensive. These models must therefore run on powerful servers to function. The solution would therefore be to perform the inference part of the NLU model directly on edge, on the client’s browser. We used a pre-trained TensorFlow.js model, which allows us to embed this model in the client’s browser and run the NLU. The model achieved an accuracy of more than 80%. The primary outcomes of NLU on edge show an effective and possible foundation for further development

Acromioclavicular joint dislocation and concomitant labral lesions: a systematic review

Acromioclavicular (AC) joint dislocations frequently co-occur with intra-articular glenohumeral pathologies. Few comprehensive studies have focused on labral tears specifically associated with AC joint trauma. This systematic review will address this gap. A comprehensive electronic search was conducted across PubMed, Cochrane Library, and Google Scholar (pages 1–20) spanning from 1976 to May 19, 2023. Seven studies met the inclusion criteria for this systematic review, consisting of three retrospective studies and four case series. These studies collectively involved 1,044 patients, of whom 282 had concomitant labral lesions. The pooled prevalence of intra-articular labral injuries associated with acute AC joint dislocation was 27%. The prevalence of these labral lesions varied significantly between studies, ranging from 13.9% to 84.0% of patients, depending on the study and the grade of AC joint dislocation. Various types of labral tears were reported, with superior labrum anterior to posterior (SLAP) lesions being the most common. The prevalence of SLAP lesions ranged from 7.2% to 77.4%, with higher grades of AC joint dislocations often associated with a higher prevalence of SLAP tears. Moreover, grade V dislocations exhibited a complete correlation with SLAP tears. The studies yielded contradictory findings regarding older age and higher grades of AC joint dislocation as risk factors for concurrent labral lesions. This review underscores the frequent association between labral lesions and AC joint dislocations, particularly in cases of lower-grade injuries. Notably, SLAP lesions emerged as the predominant type of labral tear

The effect of music and light-color as a machine empathic response on stress in occupational health

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

Embodied conversational agent for emotional recognition training

Avatars were known in the world of video games, where heroes with specific characters, attributes and powers are assigned to players. However, avatars are evolving and reaching domains like companions, assistants and tutors. These avatars now use speech, facial expression, body language or text to interact with humans. When we say interaction, we say emotional expression and empathy. Avatars are still short in the emotional and empathic world; it cannot express nor share emotions. In this paper, we research the emotional avatar world, and we present the Anthropomorphic Chatbot for Emotion Recognition (ACER), an empathic friend companion designed for children. The goal of ACER is to teach children about emotions by expressing it through facial expressions and body language while texting through a chat. An experiment was held to test the avatar effect. Qualitative and quantitative results show users positive emotions tending towards having a chat with ACER with facial and body expressions instead of only ACERs chatbot