Comparison of Subjective Facial Emotion Recognition and "Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection" between Patients with Schizophrenia and Healthy Participants

Patients with schizophrenia may exhibit a flat affect and poor facial expressions. This study aimed to compare subjective facial emotion recognition (FER) and FER based on multi-task cascaded convolutional network (MTCNN) face detection in 31 patients with schizophrenia (patient group) and 40 healthy participants (healthy participant group). A Pepper Robot was used to converse with the 71 aforementioned participants; these conversations were recorded on video. Subjective FER (assigned by medical experts based on video recordings) and FER based on MTCNN face detection was used to understand facial expressions during conversations. This study confirmed the discriminant accuracy of the FER based on MTCNN face detection. The analysis of the smiles of healthy participants revealed that the kappa coefficients of subjective FER (by six examiners) and FER based on MTCNN face detection concurred (κ = 0.63). The perfect agreement rate between the subjective FER (by three medical experts) and FER based on MTCNN face detection in the patient, and healthy participant groups were analyzed using Fisher's exact probability test where no significant difference was observed (p = 0.72). The validity and reliability were assessed by comparing the subjective FER and FER based on MTCNN face detection. The reliability coefficient of FER based on MTCNN face detection was low for both the patient and healthy participant groups.

Robots and Robotics in Nursing

Technological advancements have led to the use of robots as prospective partners to complement understaffing and deliver effective care to patients. This article discusses relevant concepts on robots from the perspective of nursing theories and robotics in nursing and examines the distinctions between human beings and healthcare robots as partners and robot development examples and challenges. Robotics in nursing is an interdisciplinary discipline that studies methodologies, technologies, and ethics for developing robots that support and collaborate with physicians, nurses, and other healthcare workers in practice. Robotics in nursing is geared toward learning the knowledge of robots for better nursing care, and for this purpose, it is also to propose the necessary robots and develop them in collaboration with engineers. Two points were highlighted regarding the use of robots in health care practice: issues of replacing humans because of human resource understaffing and concerns about robot capabilities to engage in nursing practice grounded in caring science. This article stresses that technology and artificial intelligence are useful and practical for patients. However, further research is required that considers what robotics in nursing means and the use of robotics in nursing.

Intelligent humanoid robots expressing artificial humanlike empathy in nursing situations

Intelligent humanoid robots (IHRs) are becoming likely to be integrated into nursing practice. However, a proper integration of IHRs requires a detailed description and explanation of their essential capabilities, particularly regarding their competencies in replicating and portraying emotive functions such as empathy. Existing humanoid robots can exhibit rudimentary forms of empathy; as these machines slowly become commonplace in healthcare settings, they will be expected to express empathy as a natural function, rather than merely to portray artificial empathy as a replication of human empathy. This article works with a twofold purpose: firstly, to consider the impact of artificial empathy in nursing and, secondly, to describe the influence of Affective Developmental Robotics (ADR) in anticipation of the empathic behaviour presented by artificial humanoid robots. The ADR has demonstrated that it can be one means by which humanoid nurse robots can achieve expressions of more relatable artificial empathy. This will be one of the vital models for intelligent humanoid robots currently in nurse robot development for the healthcare industry. A discussion of IHRs demonstrating artificial empathy is critical to nursing practice today, particularly in healthcare settings dense with technology.