How It Works: Merging AI with Compassion Nyamathi and her team are focusing on monitoring walking patterns, tracking emotional states, and utilizing machine learning to predict agitation. The system collects and stores real-time data, analyzing a patient’s gait, movements and emotional state. Through a combination of visual and audio sensing technologies, they are evaluating the CCR’s ability to assess a patient’s well-being and provide caregivers with timely alerts when interventions may be needed. In addition, Nyamathi’s team has designed nine personas and nine daily activity maps that includes detailed information of a representative person with dementia’s background, personal preferences, cultural aspects and mobility issues. These tools will help the engineers, computer scientists and linguistic experts develop the models necessary to allow AI-driven CCRs to engage with dementia patients in a highly personalized way, ensuring that interactions are empathetic, human-like and tailored to individual needs. “The goal is not to replace human interaction, but to add the care-companion robot to the care team for a person with dementia” Nyamathi said. “And for those who do not have a care team available because of socioeconomic reasons or geographic limitations such as family not living nearby, our CCR system can provide them with an option for care.” A Transformative Impact on Dementia Care This project is shifting the paradigm by introducing proactive, AI-assisted interventions that enhance both patient safety and emotional well-being. Through rigorous testing in state-of-the-art simulation labs and real-world settings, the CCR system is showing promising results in development of the models and algorithms needed to potentially reduce agitation and fall risk. By assessing emotional states in real time, the CCR may provide a new layer of personalized, around-the-clock support for individuals with moderate to severe dementia. “The goal is to create a system that can forecast and recognize agitation, then intervene using empathetic communication,” Nyamathi explained. “By leveraging computational models and linguistic theories, we aim to test the ability of the CCR to reduce falls, and thus prevent unnecessary injuries and hospitalizations, as well as alleviating the burden on caregivers.”
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