"37.3 million falls that are severe enough to require medical attention occur each year."
- WHO
"Around one in four adults above the age of 65 have experienced a fall in the United States."
- CDC
"14% of the elderly population in 10 states in India have experienced falls"
- WHO
It is evident from the above statistics that falls are a very common issue in the elderly that is not often spoken about. However, elderly patients often find themselves unassisted for long period of times, which further exacerbates their conditions. Prolonged wait times to receive assistance can lead to dehydration, further injury which can lead to long recovery times, and even to pneumonia and death. Additionally, some users may have pre-existing conditions such as heart problems which may worsen due to lack of medical attention. Such disablity could also prevent a patient from manually setting off an alarm (like a pendant alarm, for instance).
Another interesting piece of information is this:
"Most falls, however, do not cause sufficient injury to receive medical attention. Only 3 to 5 percent of falls in elderly persons who reside in the community and in nursing homes result in fractures, with fewer than 1 percent of falls causing hip fractures. Only about 5 to 10 percent of falls cause other serious injuries requiring medical care. Between 30 and 50 percent of falls result in a variety of minor soft tissue injuries that do not receive medical attention; the remainder cause no injury or only trivial damage."
This means that there is a growing problem of individuals older than 65 with a high fall risk and not receiving the required help on time.
O.L.I. is a medical assistance robot that monitors the user's movements and
calls for help if a fall is detected. The system consists of two components:
O.L.I. is capable of mapping the user's home and localising the user in the event of a fall. The robot is fitted with a circular steel bar that will assist the user in getting up in the event of a minor fall. O.L.I. has a flat platform on top that allows the user to place objects on it. Further extensions that is being considered here is fitting it with a first aid box and a light misting spray to wake the user in case they have fainted.
O.L.I. also comes with a mobile application. The primary purpose of the app is to monitor the sensors present on a smartphone and determine if the user has fallen over. The app also comes with a machine learning model that classifies sensor data into falls and normal activities to provide a higher level of accuracy to the predictions. The app comes with three main features - Call O.L.I., Set up and Fall detection demo.
Call O.L.I. is a button that summons the robot to the user. This function does not wait for a fall event to be detected, but rather allows the user to call the robot if they require it.
Set up allows the user to enter their contact details, set up their WiFi and Bluetooth settings on their smart phone, and configure O.L.I. to map their home. This is the ideal first step the user should take after installing the app for the first time.
Fall detection demo is a prototype button. In our final version, this button would not exist as the fall detection process would run in the background of the app. This button is present to highlight the different steps present in the fall-detection procedure.
Try out our Figma prototype of the app below:
Fall detection systems for the elderly is an common area of research, but there are not a lot of
products in the market with respect to this.
We believe that a robot can provide minimal help to the elderly people with a high fall risk,
providing them with a sense of independence. This additionally releases the stress of the nurse and doctors from these tasks in care homes, as well
as provides the family of such patients with some relief.
We currently have robots that navigate through campuses, roads and restaurants already. This technology can be extended to hospitals or nursing homes and
bring about real change in the lives of many.
O.L.I. is currently not designed for a hospital setting, since we need more professional advice to deploy the robot in such sophisticated environment as well as immense
amounts of user testing. However, we wish for it to be a robot that can be used by everyone, so we need to control the cost to make it affordable.
This affects how we choose our material and parts to build the robot.
After hours of brainstorming, our robot comes in a medium size, so it won’t affect people walking in the corridor, and have also decided to have extensible legs to support the human’s weight when lifting them up.
Most people in today's world use a smartphone, so an app that detects falls would be convenient and does not need extra hardware to achieve that.
University of Edinburgh
group1@ed.ac.uk
© 2021 SDP Group 1 - O.L.I