Sun Fibo Technology
Private Limited

Case Studies - TumbleSense

Background

Falls among elderly individuals are a major cause of injury and hospitalization, often leading to long-term health complications and even death. According to the World Health Organization (WHO), approximately 28-35% of people over 65 years of age experience a fall each year, with severe consequences. For elderly care facilities, where residents are often frail, immobile, or suffering from cognitive decline, preventing and responding to falls in real-time is a critical challenge.

A large assisted living facility in the U.S. housing over 200 elderly residents with varying degrees of mobility and cognitive abilities faced increasing concerns about fall-related injuries. Despite having manual monitoring systems and emergency call buttons, there were gaps in ensuring rapid fall detection, particularly for residents who were unable to call for help after falling.

The facility wanted to improve the safety and well-being of its residents by adopting a more automated, responsive fall detection solution. They sought a solution that would ensure immediate intervention to prevent serious injuries and provide better monitoring and data collection for healthcare providers.

Objective

The goal was to implement an AI-powered fall detection system that:

• Provided immediate detection of falls
• Ensured rapid response from healthcare staff
• Reduced false alarms
• Improved overall quality of care and patient safety

Solution

After evaluating several technologies, the facility decided to implement an AI-powered fall detection system integrated with smart sensors and wearables. The solution included AI algorithms capable of analyzing data from motion sensors, cameras, and accelerometers to detect sudden changes in a person's posture or movement indicative of a fall. Key components of the solution were:

1. Wearable Sensors & Smart Devices:
   1. Each resident was equipped with a smart wristband or pendant with an accelerometer that could detect movement patterns. The sensor was lightweight, non-intrusive, and designed to be worn at all times.
   2. In addition to detecting falls, the wearables could monitor activity levels, send emergency alerts, and track health data such as heart rate, which could also signal an impending fall due to dizziness or other health issues.
2. AI-Powered Fall Detection Algorithm:
   1. The AI algorithm continuously processed data from the sensors in real-time to determine if a fall occurred. By analyzing movement data (e.g., rapid acceleration, sudden changes in posture, and impact force), the system could distinguish between regular activities (e.g., sitting down or bending over) and actual falls.
   2. The system used machine learning to improve detection accuracy over time, learning each resident’s normal activity patterns to minimize false positives and negatives.
3. Camera-Based Monitoring:
   1. In high-risk areas such as hallways, bathrooms, and common rooms, the system used AI-powered cameras equipped with computer vision to monitor residents' movements. These cameras could analyze posture and detect whether a resident was on the ground after an abrupt fall.
   2. The cameras only monitored movement and posture for fall detection purposes, respecting privacy and avoiding surveillance beyond the fall detection scope.
4. Centralized Monitoring System:
   1. The AI-powered system was connected to a central monitoring platform that provided healthcare staff with real-time alerts, including the exact location of the fall and the resident involved. This enabled staff to respond immediately, often within seconds of the fall.
   2. The system also created detailed reports, allowing caregivers to track incidents over time, analyze trends, and make data-driven decisions about each resident’s care needs.
5. Integration with Emergency Response:
   1. In the event of a fall, the system was integrated with the facility’s emergency response system to alert caregivers and nurses instantly, allowing them to reach the resident without delay.
   2. The system could also alert family members or emergency contacts if the fall was particularly severe or resulted in a loss of consciousness.

Results

The implementation of the AI-powered fall detection system produced significant improvements in resident safety and overall care. The results included:

1. Faster Response Times and Immediate Assistance:
   1. The AI system significantly reduced response times by providing immediate notifications to caregivers in case of a fall. Response time was cut by 70%, with healthcare staff reaching residents in less than 2 minutes on average, compared to the typical response time of several minutes with manual systems.
   2. Real-time location tracking allowed staff to know exactly where the fall occurred, eliminating delays caused by confusion or miscommunication.
2. Reduced Falls and Injuries:
   1. The AI system not only detected falls faster but also helped prevent falls by providing data that allowed healthcare staff to adjust care plans. For example, it identified residents at higher risk of falling, prompting early interventions such as increased monitoring or the use of mobility aids.
   2. The overall fall-related injury rate in the facility decreased by 35% within the first six months of deployment. Residents who fell were able to receive help more quickly, reducing the severity of injuries and preventing long-term complications.
3. Improved Detection Accuracy:
   1. The AI algorithms became increasingly accurate over time, learning the individual patterns of movement for each resident. The false alarm rate was reduced by 50% compared to previous systems that relied on manual monitoring or basic motion sensors.
   2. The integration of camera-based monitoring also ensured that no falls went unnoticed, particularly in areas where residents might not have been able to call for help.
4. Better Data for Healthcare Providers:
   1. The system’s data analytics provided valuable insights for healthcare providers. It allowed them to identify patterns in falls and near-misses, leading to better risk assessment and the adjustment of care plans.
   2. The system’s data analytics provided valuable insights for healthcare providers. It allowed them to identify patterns in falls and near-misses, leading to better risk assessment and the adjustment of care plans.
5. Increased Resident Satisfaction and Peace of Mind:
   1. Residents felt safer knowing that they had a fall detection system in place that would quickly alert caregivers in case of an emergency. Families of residents reported increased satisfaction, as they were assured that their loved ones were being monitored effectively, even if they couldn’t call for help themselves.
   2. Residents also appreciated the non-intrusive nature of the wearable devices, which provided a sense of security without causing discomfort or privacy concerns.

Challenges

Despite the significant success of the AI-powered fall detection system, there were some challenges:

• Initial Resistance to Change: Some residents were initially hesitant to wear the wearable devices. However, the facility addressed this by explaining the safety benefits and ensuring that the devices were comfortable and unobtrusive.
• System Calibration: Initial calibration of the AI algorithms took time to ensure high accuracy. The system was fine-tuned over several weeks to reduce false alarms and optimize detection for different types of falls..

Conclusion

The implementation of the AI-powered fall detection system marked a significant leap forward in elderly care, improving both the safety and quality of life for residents. By leveraging artificial intelligence and wearable technology, the facility was able to detect falls faster, reduce injuries, and provide caregivers with more valuable insights to optimize care plans.

This case study demonstrates the potential of AI to not only detect falls but also to predict risks and provide proactive interventions, transforming elderly care and improving health outcomes. As AI technology continues to evolve, fall detection systems will become even more precise and integral to maintaining the well-being of elderly residents in care facilities.