Fall Detection System for the Elderly:
Objective (why?)
Falling is a natural part of life. However, as you get older or if you have certain conditions, falling can unfortunately cause life altering injuries with continuing after effects. There are many reasons for a person to fall, such as aging, neurological problems, or physical problems. People prone to frequent falls or injuring themselves severely from falling are usually very cautious when going about their daily lives, and it can restrict their quality of life. A possible solution to this would be to design a sensor system that can detect a person's current risk from falling (taken from motion data and biometric measurements/vital signs), detect possible falls, and send alerts to either the individual or a caregiver.
Background (who? where?)
The main subject would be the elderly as more than one of four elderly are likely to fall according to the CDC. The focus is specified on those living alone as well as those with caregivers. This covers a majority of people who may experience frequent falls and it is in order to monitor the health as well as provide immediate help when needed. Monitoring the physical activity of the patient allows them to address their needs and prevent further damage. Falling can lead to incapabilities of receiving the necessary help and leave a person stranded for too long. It is essential that the elderly prone to falling can have additional resources in their day-to-day life to improve their quality of life.
Methodology (how? when?)
First, we'd have to place sensors on key parts of the body that can be used to indicate falling, whether through vitals or motion. After that, we'd have to record the patient's vitals and range of motion in their day-to-day life (to create a standard reading for the sensors). Then, we'd use these readings to categorize movements as either normal or abnormal. If abnormal motion is detected, it sends an alert to a server (and potentially, a mobile app). The app then notifies caregivers or family members via Wi-Fi or a beacon signal.
Expected Results (what?)
The detection system should be able to detect both movement and biometric data from the individual wearing it. For example, issues with balance and gait, possible drops of blood pressure leading to dizziness or fainting, blood sugar issues, heart rate or rhythm problems, and other abnormal movements would be detected, classified as a possible pre-fall risk, and a notification would be sent to the appropriate parties. If a fall were to occur, the pre-fall symptoms would be documented and sent alongside an alert.
Cost (how much will it cost)?
Assuming 100 units, the estimated costs of the design would be $16,340 for one year.
9-Axis Sensor Module (consists of an accelerometer, gyroscope, and a geomagnetic sensor): $13.90
Spark-Fun ESP-32 Thing (supports Wi-Fi and Bluetooth low energy communications): $23.50/unit
Server (must support large amount of users, store and process large volumes of data, send real time alerts): $1000/month
Mobile Application Hosting: $50/month
Research Sources: