The decrease in physical abilities is a natural aspect among the elderly, leading to an increased likelihood of falling. However, it is our responsibility to improve elderly care and safety by leveraging advanced technology. In this blog, we will discuss fall detection using sensors and analyzing the results through deep learning while delving into radar technology.

Introduction

The global population of people aged 60 and above is rapidly growing. As reported by the World Health Organization (WHO), approximately 28-35% of the elderly aged 65 or over fall each year leading to severe injuries, fractured bones, and even loss of life. Smart health has identified a significant requirement for addressing the absence of support following accidental falls.

Leveraging the power of deep learning solutions that incorporate a range of sensors, including wearables, RGB cameras, and radar, can detect accidental falls with remarkable speed and accuracy. Once a fall is detected, these systems can quickly provide assistance and address any potential life-threatening situations, making them an invaluable tool for ensuring safety and wellbeing.

We at MulticoreWare provide this technological advantage to innovate and develop health-monitoring software products. We help to create a software product by leveraging sensors to deep learning journeys.

Challenges in Elderly Care

Detecting falls in the elderly is vital, but numerous challenges arise:

• Technological issues: These include the accuracy of wearable sensors, compliance among the elderly, and device costs.
• Privacy concerns: Surveillance and data security, often deter the adoption of monitoring systems.
• Environmental factors: Varied fall locations, nature of falls, and cluttered spaces can hinder detection.
• Communication barriers: Communication obstacles among the elderly individuals, less familiar with technology, lead to delayed responses after a detected fall. Over-reliance on technology without holistic preventive measures can also be counterproductive.

To ensure elderly safety, it’s crucial to employ a comprehensive approach that integrates technology to improve the accuracy of fall detection and prevention, ultimately raising the standard of care for seniors.

Importance of Sensors

In order to detect falls effectively, sensors that are capable of detecting changes in movement and acceleration are employed. Each sensor has its unique capabilities, and they are used in various scenarios to ensure accurate detection and quick response.

• Wearable sensors: Wearable devices are designed to be worn on a daily basis either on the wrist or as a waistband to monitor health in real-time. These devices employ sensors such as accelerometers and gyroscopes which measure motion and orientation changes, with a focus on acceleration analysis. They employ machine learning algorithms to detect falls and send alerts, while offering GPS for fall location tracking. It seamlessly adapts to outdoor use in any environment and accommodates a wide range of activities. However, it can be inconvenient for elderly individuals to constantly wear or carry these electronic devices at all times.
• Vision sensors: The computer vision-based fall detection system uses various image processing methods to analyze live video frames. These video features play a crucial role in shaping the model for falls. Additionally, deep learning algorithms improve precision. Vision sensors offer non-intrusive, highly accurate fall detection without requiring users to use wearable devices constantly. Nevertheless, the system may not be suitable for low-light conditions, such as dark rooms, and could raise privacy concerns in certain environments, like bathrooms. Also, the fixed camera placement limits the extent of the monitored area.
• Audio sensors: The fall detection system based on environmental equipment mainly uses environmental data, such as sound, vibration, and infrared signals, caused by human falls to detect the risk of falling. Audio sensors constantly monitor and analyze sound patterns associated with falls, using deep learning algorithms to differentiate between falls and routine sounds, triggering alerts when fall-like sounds are detected. These sensors are cost-effective, cover a broad detection area, and can be combined with other sensors to improve accuracy. However, challenges arise from environmental factors, including non-human objects causing similar data, necessitating the integration of these sensors with others to enhance monitoring effectiveness.
• Wi-Fi sensing: The sensor constantly emits wireless electromagnetic waves and detects movements through reflected signal distortion. It’s useful in a wide range of applications, including human activity recognition, effective monitoring of vital signs such as breathing and heart rate effective in fall prevention, outperforming cameras and infrared sensors. It works in any lighting and can even detect falls through walls, making it ideal for smart homes with tiny microchips. A key challenge is discerning signals from multiple individuals when employing Wi-Fi sensing. The reliability of Wi-Fi sensing cannot be assured across various environments, and there exists a potential risk of privacy breaches due to the portability and widespread use of Wi-Fi equipment.

• Radar technology: Radar has become increasingly popular in recent years since it addresses privacy issues of video-based techniques and eliminates the requirement to wear a device. A radar sensor can track human movements, penetrate obstacles, and remain unaffected by variations in lighting conditions. By analyzing the frequency of reflected radio signals and tracking movement patterns, radar technology maintains a person’s position history and detects falls, triggering alerts to caregivers.

  It can extract vital signs from multiple individuals simultaneously, making it an efficient tool for proactive measures aimed at fall prevention and monitoring changes in vital signs. Moreover, radar technology is adept at spotting deviations in behavior, such as extended inactivity or unusual movements, which can indicate potential health issues.

  However, the radar signals tend to be noisy, necessitating extensive pre-processing for noise removal. Once cleaned, they can be fed into a deep learning algorithm. Although it may encounter challenges in peak detection, these issues can be enhanced through sensor fusion techniques. Overall, this technology has the potential for enhancing healthcare outcomes.

McW’s Expertise: Sensor Fusion for Improved Accuracy

At MulticoreWare, our team of radar technology specialists excel in developing advanced fall detection algorithms. We have leveraged sensor fusion to enhance the capabilities of our fall detection systems ensuring more accuracy and reliability in health monitoring solutions.

This approach entails integrating data from various sensors or different sources to reduce uncertainty in the resulting information compared to using these sources individually. For example, our method involves merging data from radar sensors, lidar sensors, and RGB cameras to achieve a more accurate estimate of indoor object locations, aiming to improve the efficiency of a fall detection system and minimize false alarms.

Conclusion

As we grow older, our need for support and care increases. Sensors play a critical role in ensuring the well-being of the elderly. However, there is a lot of scope for maximizing their efficiency. Various parts of the technology require accuracy, efficiency, and production. At McW, we are dedicated to advancing these technologies for our partners and our own initiatives, with the ultimate aim to deliver the highest quality care to the elderly population.