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New Tech Helps Monitor Dementia Agitation

Research combines AI and wearables to predict agitation in dementia patients.

― 5 min read


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Dementia is a condition that affects many people, mostly older adults. It messes with the brain, making it hard to think, remember, and do everyday tasks. It can also lead to other problems like Agitation and aggression, which can be tough for both the person with dementia and their caregivers. These behaviors can cause a lot of stress and even make it necessary for the person to stay in a hospital or a care facility longer than needed.

The Challenge of Agitation

People living with dementia often show signs of agitation. This can include pacing, shouting, or even acting aggressively. These behaviors are usually the result of unmet needs-maybe they’re feeling uncomfortable or confused. The tricky part is that caregivers often have to rely on their memories to report these behaviors, which can be biased and not always accurate.

A High-Tech Solution

Here’s where technology comes into play. Researchers are using smart tools like artificial intelligence (AI) to help monitor and predict when a person with dementia may act out. This can be done in real-time, using wearable devices and video Cameras. Imagine a world where a watch or a camera can signal to caregivers that someone might need help before they actually start acting out. It sounds like science fiction, but it's becoming a reality!

The Study Setup

For this research, a system was created that combines a wristband and videos from cameras. The wristband, called EmbracePlus, collects various health signals from the wearer, such as heart rate and skin temperature. The cameras are set up in the common areas of a facility to observe and record behaviors.

The researchers began by gathering a small group of Participants with severe dementia. They installed cameras and asked participants to wear the wristbands for 24 to 72 hours on different days. During this time, the caregivers would also watch for signs of agitation and note when these events happened.

How Does It Work?

The wristband collects different types of information, like how the person is moving and how their body responds in terms of stress. It sends these signals to a secure system where researchers can analyze the Data.

Collecting Data

The wristband tracks:

  • Skin Conductance: This shows how sweaty or stressed someone might be.
  • Heart Rate: Faster heart rates can indicate anxiety or agitation.
  • Movement: This can show if a person is more restless than usual.

Meanwhile, the cameras capture the visual behavior of the participants. When the cameras notice a certain behavior, they record the exact timing of those events. This combined information helps create a clearer picture of what is happening with the person at any given moment.

The Benefits of Combining Tools

When both the wristband data and video footage are analyzed together, researchers can identify patterns leading up to moments of agitation. For instance, if the wristband indicates a spike in heart rate a few minutes before the cameras catch the individual pacing, that could signal an upcoming episode of agitation.

Early Warning System

One of the exciting findings is that the system can spot signs of agitation up to six minutes before they occur. This gives caregivers time to intervene-perhaps by checking in on the individual or providing them with something that calms them down. It’s like having a crystal ball that tells you when someone is about to have a tough moment!

The Success of the Pilot Study

In a small trial, three participants wore the EmbracePlus wristband while researchers monitored their behaviors through the cameras. The results were encouraging. The “AI-powered” system was able to predict when agitation might occur, sometimes even before the behavior was visible.

What Did They Find?

The researchers learned that different participants showed different signs before they became agitated. For example, one person might show signs of stress through increased heart rates, while another might reveal agitation through sudden movements. The key was to connect those little signals to overarching behaviors.

Agitation Patterns

Overall, the researchers could identify and categorize behaviors in real-time. In some cases, when the wristband detected physical changes, the cameras would confirm those changes, confirming that the system was doing its job.

The Role of Technology

This project highlights the potential for technology to improve the lives of people living with dementia. Instead of waiting until a crisis occurs, caregivers can now receive alerts, enabling them to provide help before things escalate.

The Importance of Privacy

A major concern when using cameras in healthcare settings is privacy. The researchers took steps to ensure that the system followed guidelines to protect participants' identities. For instance, they blurred faces in video recording, ensuring that no personal information could be misused. This way, caregivers can monitor behaviors without violating anyone’s privacy.

Next Steps

The pilot study was promising. However, researchers know that there is still a lot of work to do. They plan to expand the study to include more participants. This will help ensure that the system works well in different situations and with various individuals. The more data they collect, the better they can fine-tune the technology.

Looking Toward the Future

Moving forward, the ultimate goal for researchers is to create a system that operates autonomously. This means that the technology would independently monitor and predict agitation without needing constant human oversight. Imagine a scenario where the system can alert caregivers to significant changes around the clock, allowing them to focus on other aspects of care.

Conclusion

In summary, this approach shows great promise in how we care for people with dementia. By using a combination of wearable technology and video monitoring, caregivers can potentially reduce the stress and danger associated with agitation in dementia patients. The blend of data from both sources allows for accurate predictions, which can lead to better care and improved quality of life for those affected by this challenging condition. It’s a step toward a more understanding and proactive way to help those living with dementia and their families.

Original Source

Title: A Novel Multimodal System to Predict Agitation in People with Dementia Within Clinical Settings: A Proof of Concept

Abstract: Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with a diminished quality of life. Agitation and aggression (AA) in people living with dementia (PwD) contribute to distress and increased healthcare demands. Current assessment methods rely on caregiver intervention and reporting of incidents, introducing subjectivity and bias. Artificial Intelligence (AI) and predictive algorithms offer a potential solution for detecting AA episodes in PwD when utilized in real-time. We present a 5-year study system that integrates a multimodal approach, utilizing the EmbracePlus wristband and a video detection system to predict AA in severe dementia patients. We conducted a pilot study with three participants at the Ontario Shores Mental Health Institute to validate the functionality of the system. The system collects and processes raw and digital biomarkers from the EmbracePlus wristband to accurately predict AA. The system also detected pre-agitation patterns at least six minutes before the AA event, which was not previously discovered from the EmbracePlus wristband. Furthermore, the privacy-preserving video system uses a masking tool to hide the features of the people in frames and employs a deep learning model for AA detection. The video system also helps identify the actual start and end time of the agitation events for labeling. The promising results of the preliminary data analysis underscore the ability of the system to predict AA events. The ability of the proposed system to run autonomously in real-time and identify AA and pre-agitation symptoms without external assistance represents a significant milestone in this research field.

Authors: Abeer Badawi, Somayya Elmoghazy, Samira Choudhury, Sara Elgazzar, Khalid Elgazzar, Amer Burhan

Last Update: Oct 25, 2024

Language: English

Source URL: https://arxiv.org/abs/2411.08882

Source PDF: https://arxiv.org/pdf/2411.08882

Licence: https://creativecommons.org/publicdomain/zero/1.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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