These robots can clean, exercise - and care for your elderly parents. Would you trust them to? Hidden away in a lab in north-west London three black metal robotic hands move eerily on an engineering work bench. We're not trying to build Terminator, jokes Rich Walker, director of Shadow Robot, the firm that made them. Bespectacled, with long hair and a beard and moustache, he seems more like a latter-day hippy than a tech whizz, and he is clearly proud as he shows me around his firm.

Mild-stage dementia patients primarily experience two critical symptoms: severe memory loss and emotional instability. To address these challenges, we propose DEMENTIA-PLAN, an innovative retrieval-augmented generation framework that leverages large language models to enhance conversational support. Our model employs a multiple knowledge graph architecture, integrating various dimensional knowledge representations including daily routine graphs and life memory graphs. Through this multi-graph architecture, DEMENTIA-PLAN comprehensively addresses both immediate care needs and facilitates deeper emotional resonance through personal memories, helping stabilize patient mood while providing reliable memory support. Our notable innovation is the self-reflection planning agent, which systematically coordinates knowledge retrieval and semantic integration across multiple knowledge graphs, while scoring retrieved content from daily routine and life memory graphs to dynamically adjust their retrieval weights for optimized response generation. DEMENTIA-PLAN represents a significant advancement in the clinical application of large language models for dementia care, bridging the gap between AI tools and caregivers interventions.

The close link between cognitive decline and language has fostered long-standing collaboration between the NLP and medical communities in dementia research. To examine this, we reviewed over 200 papers applying NLP to dementia related efforts, drawing from medical, technological, and NLP-focused literature. We identify key research areas, including dementia detection, linguistic biomarker extraction, caregiver support, and patient assistance, showing that half of all papers focus solely on dementia detection using clinical data. However, many directions remain unexplored: artificially degraded language models, synthetic data, digital twins, and more. We highlight gaps and opportunities around trust, scientific rigor, applicability, and cross-community collaboration, and showcase the diverse datasets encountered throughout our review: recorded, written, structured, spontaneous, synthetic, clinical, social media based, and more. This review aims to inspire more creative approaches to dementia research within the medical and NLP communities.

Doctors believe artificial intelligence is now saving lives after a major advancement in breast cancer screenings. AI is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. People suffering from dementia could live more independently thanks to a pair of AI-powered socks that can track everything from a patient's heart rate to movement. Called "SmartSocks," the AI-powered apparel was created in partnership between the University of Exeter and researchers at the start-up company Milbotix, according to SWNS. The socks can monitor a patient's heart rate, sweat levels and motion to prevent falls while also promoting independence for those with dementia. "I came up with the idea for SmartSocks while volunteering in a dementia care home," SmartSocks creator Zeke Steer, CEO of Milbotix, told SWNS.

Vik Chandra, CEO of uMETHOD, talked with Fox News Digital about how his company's AI tool, RestoreU, is helping physicians pinpoint better treatment plans for their dementia patients. More than six million Americans are living with Alzheimer's disease -- and one in three seniors dies with the disease, according to statistics from the Alzheimer's Association. With so many different factors -- genetics, lifestyle and environment -- influencing a person's risk of developing Alzheimer's, many doctors are moving away from one-size-fits-all approaches and calling for more individualized treatments. It's a concept known as precision medicine. And it's what inspired a company called uMETHOD to create RestoreU, a tool that uses artificial intelligence to help physicians create personalized care plans for patients with Alzheimer's and other types of dementia.

Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment. The feasibility of deploying automated tools that can classify language samples obtained from speech in large-scale clinical settings depends on the ability to capture and automatically transcribe the speech for subsequent analysis. However, the impressive performance of self-supervised learning (SSL) automatic speech recognition (ASR) models with curated speech data is not apparent with challenging speech samples from clinical settings. One of the key questions for successfully applying ASR models for clinical applications is whether imperfect transcripts they generate provide sufficient information for downstream tasks to operate at an acceptable level of accuracy. In this study, we examine the relationship between the errors produced by several deep learning ASR systems and their impact on the downstream task of dementia classification. One of our key findings is that, paradoxically, ASR systems with relatively high error rates can produce transcripts that result in better downstream classification accuracy than classification based on verbatim transcripts.

Background: The dementia epidemic is progressing fast. As the world's older population keeps skyrocketing, the traditional incompetent, time-consuming, and laborious interventions are becoming increasingly insufficient to address dementia patients' health care needs. This is particularly true amid COVID-19. Instead, efficient, cost-effective, and technology-based strategies, such as sixth-generation communication solutions (6G) and artificial intelligence (AI)-empowered health solutions, might be the key to successfully managing the dementia epidemic until a cure becomes available. However, while 6G and AI technologies hold great promise, no research has examined how 6G and AI applications can effectively and efficiently address dementia patients' health care needs and improve their quality of life.

Artificial intelligence (AI) could diagnose a suspected dementia patient the day they are assessed. The disease currently has no set test, with medics generally relying on cognitive assessments and brain scans. With it sometimes taking years to reach a diagnosis, scientists from the University of Cambridge are developing an AI system that could spot signs of the disease after a single brain scan. The system is "trained" to compare a suspected patient's brain scan against thousands of confirmed cases, with the algorithm potentially identifying signs of the disease that a neurologist could not spot. Although the technology is still in a trial stage, it could diagnose dementia years before symptoms emerge.

Now working as a professor with Mechanical Aerospace and Biomedical Engineering at the University of Tennessee, Zhao is looking to develop a way for artificial intelligence in robots to assist dementia patients with everyday activities. "Suppose a dementia patient tried to make a cup of coffee, they may forget what they're doing or may forget what to do next," said Zhao. Zhao and graduate research assistant Fengpei Yuan have been programming the robots to help slow the progression of Alzheimer's by keeping patients engaged. "We really want to use our robots to engage people in social activities. I think the big challenge for people with dementia is they will gradually forget who they are and forget very important persons," said Fengpei.

Access to informative databases is a crucial part of notable research developments. In the field of domestic audio classification, there have been significant advances in recent years. Although several audio databases exist, these can be limited in terms of the amount of information they provide, such as the exact location of the sound sources, and the associated noise levels. In this work, we detail our approach on generating an unbiased synthetic domestic audio database, consisting of sound scenes and events, emulated in both quiet and noisy environments. Data is carefully curated such that it reflects issues commonly faced in a dementia patients environment, and recreate scenarios that could occur in real-world settings. Similarly, the room impulse response generated is based on a typical one-bedroom apartment at Hebrew SeniorLife Facility. As a result, we present an 11-class database containing excerpts of clean and noisy signals at 5-seconds duration each, uniformly sampled at 16 kHz. Using our baseline model using Continues Wavelet Transform Scalograms and AlexNet, this yielded a weighted F1-score of 86.24 percent.