Where did the original idea for this project come from? What led you to it?
Adrika Gupta: NeuroSense emerged from an observation that initial symptoms of mental decline usually start with behavioral differences in speaking, moving, and everyday routine; however, this type of monitoring is hardly done constantly in real-life settings. We were curious to find out if AI can make early detection possible while providing sufficient privacy for the patients. Thus, we created a privacy-oriented edge AI solution based on EEG, speech, and mobility features aimed at detecting behavioral changes compared to individual patient’s behavior pattern. The idea behind our invention wasn’t to diagnose anything, but to do research and raise awareness about the necessity to seek medical advice.
What is a digital twin? How would you describe it, and how does it work?
Sangyan Hari Pushkar: Digital twin refers to a model of a physical entity, which is updated based on data from the real world. Digital twins can be utilized in health care applications for modeling behavioral or physiological patterns, which can be tracked for change. In NeuroSense, the digital twin refers to the customized behavioral model, where information such as gait and speech is obtained alongside EEG readings to learn the individual's baseline patterns, which can be compared in the future to detect behavioral changes. The use of digital twins enables tracking of changes that are unique to an individual as opposed to generalizations that may exist among people within the entire population.
What exactly does your project detect, and what kind of data does it work with? What can it capture, and over what time frame?
Sangyan Hari Pushkar: The NeuroSense system is built to track incremental drifts in behaviour patterns that might indicate an early onset of cognitive impairment. NeuroSense uses multimodal data which include EEG waves, speech characteristics and gait/movement patterns. When analyzing speech characteristics, NeuroSense considers pause patterns, speech rate, and fluency levels. When considering gait and movement patterns, NeuroSense looks at movement stability and consistency. In terms of EEG analysis, NeuroSense is able to detect any changes in patterns in the form of brainwave patterns in alpha, theta, and delta bands. Unlike the single snapshot approach, NeuroSense is meant to be used for a prolonged period of time, typically lasting for weeks or even months. The purpose is to observe subtle differences from individual behavior patterns.
How long before the onset of Alzheimer’s disease do certain types of symptoms appear in a person’s speech or movements? Can such symptoms be recognized by their close ones? Can an average person notice them?
Aadrika Gupta: Neuroscience and research on brain health reveal that certain behavioral changes that are caused by Alzheimer's disease tend to manifest themselves several years before the official diagnosis of the disease. Such behavioral changes might be reflected in more frequent pauses while speaking, decreased fluency, problems with finding appropriate words, changes in balance during walking, slower patterns of movement, and other behavioral changes. However, it should be acknowledged that such symptoms tend to emerge gradually and not abruptly; hence, they might be mistaken for the effects of aging, stress, tiredness, and other temporary factors. At times, family members and close friends might detect the emergence of the mentioned symptoms, but there would be no systematic evidence that could require immediate medical attention. As a result, a common individual will most likely miss those signs of the onset of the disease in the first place; hence, one of the reasons behind our project was to try to figure out whether continuous behavioral monitoring via speech, movement, and brain activity would help detect any gradual drift in time in an efficient and anonymous manner.
In what ways are current hospital approaches inadequate? Why are they insufficient?
Aadrika Gupta: The current hospital strategies may prove to be inadequate as patients are usually observed in clinical sessions which are relatively short. Cognitive problems are known to evolve slowly for months or years together; hence, certain behavioural patterns can remain unobservable within the short duration of an observation session. Apart from this, continuous surveillance away from hospitals is still not common, and some systems require cloudbased processing, which may pose certain privacy issues. Our concept behind NeuroSense involved exploring the possibility of using edge AI that ensures the privacy of the users
What would this look like in practice? How could a person use or apply the device? Who would evaluate the results, and how? Or would the device itself notify the person that they should see a doctor?
Aadrika Gupta:
- You mention in the project that the system also aims to work with EEG. Will the person’s brain activity actually be measured? For how long and how often would they need to be “connected” in order to obtain valid and reliable data?
- How financially demanding would this continuous “monitoring” be for the patient? Is it expected that the tool would be covered by insurance, or would patients have to pay for it themselves? And if so, what price range are we talking about?
- The data is divided into three categories—the first serving the patient and their family, the second intended for doctors, and the third for scientific purposes. How could this data be used in the future? Could it contribute to further research on Alzheimer’s disease?
- The project also includes a dashboard that helps the individual maintain a routine. Are these recommendations based on measured data and personalized for the specific user, or are they general recommendations for maintaining good health and improving cognitive reserve?
- In your prototype, you also include a Cognitive Score Trend, and in the section for researchers even a live EEG stream. Is it possible, for example, to study how different activities affect brain activity— which ones are beneficial and which ones may place unnecessary strain on it?
- Could the application potentially function as a kind of diary that a patient keeps, from which data could then be analyzed to determine what is “good” for the brain?
- And if such data were collected on a larger scale and in greater volume, could it then be evaluated collectively and used, for example, to derive recommendations for the prevention of neurodegenerative diseases, or recommendations on what to avoid?
- Could the system’s use be expanded in the future to help prevent other neurological conditions—not only neurodegenerative ones, but also autoimmune diseases (such as early-stage multiple sclerosis), or even to help predict the timing and intensity of epileptic seizures?
- How do you plan to develop the project in the future? Will the financial support associated with winning the CLARA YOUNG INNOVATOR AWARD 2026 help in its implementation?