In this paper, we describe an actuator-based EMDR (eye movement desensitization and reprocessing) virtual assistant system that can be used for the treatment of participants with traumatic memories. EMDR is a psychological therapy designed to treat emotional distress caused by a traumatic event from the past, most frequently in post-traumatic stress disorder treatment. We implemented a system based on video, tactile, and audio actuators which includes an artificial intelligence chatbot, making the system capable of acting autonomously. We tested the system on a sample of 31 participants. Our results showed the efficiency of the EMDR virtual assistant system in reducing anxiety, distress, and negative cognitions and emotions associated with the traumatic memory. There are no such systems reported in the existing literature. Through the present research, we fill this gap by describing a system that can be used by patients with traumatic memories.
As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried’s phenotype that is capable of detecting frailty. To determine user requirements, the system’s architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty.
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