Wearable sensor with Artificial Intelligence for prevention of falls in elderly people
ABSTARCT :
Background: As per the census 2011, Disability is more common in elderly people. One of the major reasons for disability among the elderly people is falls. Every year, one-third of community-dwelling older adults (adults aged 65 and older) experience a fall.
Falls, defined as “unexpected event[s] in which the participant comes to rest on the ground, floor, or lower level,” are responsible for a wide range of negative health outcomes. Falls are the leading cause of injury-related deaths among older adults, and the age-adjusted fall death rate (64 deaths per 100,000 older adults) increased by 30%.
Additionally, the psychological impact of falling can cause older adults and their caregivers significant fear about the risk of falling again. This fear of falling can have an accumulating effect whereby the fear of falling causes individuals to limit their everyday physical activities, which in turn makes them weaker and more susceptible to future falls. In fact, studies have shown that falling once doubles the chances of falling again.
Many falls, however, can be prevented. One of the most effective ways to reduce fall risk is through targeted exercise that improves an individual’s strength, balance, and mobility Description: Exercise-based programs, such as the Otago Exercise Program and Tai Ji Quan: Moving for Better Balance, have been shown to reduce falls by up to 35% and 55%, respectively.
Until recently, however, the vast majority of fall prevention programs were only offered in small, in-person classes hosted in local senior centres or gyms.
Although this has been the standard dissemination method for decades, it comes with significant barriers to participation. Common barriers to in-person programs include a lack of programs in rural or under resourced communities; limited or no access to transportation; scheduling conflicts; cost of getting to and using facilities; interpersonal barriers, such as finding other participants’ presence intimidating; and physical environmental barriers, such as bad weather, stairs, uneven ground, difficult parking, and more The recent developments in Wearable technology with Artificial intelligence may help elderly people in preventing falls by giving tactile/visual/auditory feedback to users regarding risks of falling.
This software may be developed with sensors that could able to sense whenever there is risk of Line of Gravity (LOG) goes outside the Base of Support (BOS) during day-to-day activities for elderly people as this is the commonest reason for elderly people.
Expected Solution: These technologies will help elderly people to prevent falling by which Disability may be prevented. This digital delivery of a fall prevention program may be feasible.
This suggests that the digital program addresses a previously unmet need in the community and its delivery would be sufficient to engage the community dwellers.
EXISTING SYSTEM :
There is many sensor-based monitoring (nonwearable) or wearable devices available to detect falls. These devices are also capable of monitoring the health of the elderly and tracking their location, which can be identified by their family members, but one disadvantage is that they need to be worn all the time [9].
Other IoT-based devices, such as motion sensors, cameras, and so on, help in continuous monitoring but cannot predict a future fall.
Several methods for predicting and preventing falls in the elderly have been developed, and while preventing a fall is quite complicated, it is possible with long-term monitoring.
In general, falls in the elderly can be monitored under four categories, namely, health-based, behavioral-based, posturebased, and emotion-based.
DISADVANTAGE :
Accuracy and Reliability: Sensors may produce false positives or negatives, leading to unnecessary alarms or missed falls. Environmental factors can also affect their accuracy.
Privacy Concerns: Continuous monitoring can raise privacy issues, as individuals may feel uncomfortable with constant surveillance.
Technology Adoption: Many elderly individuals may struggle with new technology, leading to difficulties in using or trusting the device.
Cost: High-quality sensors and the associated AI technology can be expensive, making them less accessible for some users or healthcare systems.
Dependency: Relying too heavily on technology for fall prevention may reduce personal awareness and physical activity, potentially leading to increased risk over time.
PROPOSED SYSTEM :
This innovative solution comprises a lightweight, comfortable device equipped with accelerometers, gyroscopes, and heart rate monitors to continuously track the user's movements, posture, and vital signs.
The data collected is processed in real time using sophisticated algorithms to detect deviations indicative of a potential fall, allowing for immediate alerts to caregivers or emergency contacts. Additionally, the system incorporates a user-friendly mobile app that provides personalized feedback, exercise recommendations, and health insights based on the user’s activity patterns.
With built-in communication capabilities, the device seamlessly connects to a cloud platform for secure data storage and analysis, enabling ongoing monitoring and predictive analytics. By combining cutting-edge technology with a focus on user comfort and usability, this system aims to significantly reduce the risk of falls, ultimately enhancing the quality of life for elderly individuals while fostering their independence.
ADVANTAGE :
Real-time Monitoring: Continuous tracking of movement and posture can help detect potential falls before they happen, allowing for timely intervention.
data Analysis: AI can analyze patterns in activity and gait, providing insights that can lead to personalized fall prevention strategies.
Alerts and Notifications: Immediate alerts can be sent to caregivers or family members if a fall is detected, ensuring quick response and assistance.
Improved Mobility: Encouraging safe movement through feedback and reminders can help elderly individuals maintain their independence and confidence.
Customization: Wearable devices can be tailored to individual needs, taking into account specific health conditions, mobility levels, and environmental factors.
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