New Wi-Fi technology promises to revolutionize detection of depression in the elderly. Researchers have developed a system that uses motion sensors, distributing portable devices. The method is non-invasive and takes advantage of the existing Wi-Fi infrastructure.
The artificial intelligence model, called Hope, analyzes users of user movement and sleep. With an accuracy rate of more than 87%, it identifies the first signs of depression. This allows early interventions and continuous monitoring of the mental health of the elderly.
Professor Samira A. Rhaimi, McGill University and the Mila-Quebec AI Institute, led the study. She underlines that this approach offers an alternative to traditional methods, which often require the active participation of patients. Thus, the system offers more comfortable and effective monitoring for old age.
Technology makes it possible to identify depression without efforts
Depression reaches up to 40% of the elderly in long -term institutions, but almost half of cases are not diagnosed. The impact goes beyond emotion, affecting physical health and increasing hospitalizations. According to MedicalxpressTraditional methods, such as medical interviews and portable sensors, are invasive and inexpensive for this age group.
The new approach uses Wi-Fi networks to identify movement and sleep models without body devices. Monitoring occurs continuously and discreetly, taking advantage of the existing infrastructure. Thus, doctors can follow the early signs of the disease without depending on the active participation of patients.
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In addition to practicality, technology also increases the accuracy of the diagnosis. The system uses explainable artificial intelligence (XAI), an approach that allows us to understand how the analysis is carried out. Instead of simply indicating a risk, AI shows what factors were decisive in detection, such as changes in the sleep model or mobility.
The emotional impact of chatbots on health
Making chatbots more stable emotionally can improve support for patients with mental disorders. These systems treat delicate interactions and must respond in a balanced way. The adjustment of your stability without complex reprogramming makes the AI more reliable in these scenarios.
The solution is accessible and efficient. According to Spiller, the method reduces costs when avoiding retouching in the formation of models. This guarantees more consistent responses without compromising the agility of the systems.
There are still questions to explore. Researchers study how AI reacts in long dialogues and adapts to different contexts. In the future, the creation of automated therapeutic interventions can extend its role in mental health care.
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