Unobtrusive Technologies for Fall Prediction and Prevention
The Need
Falls are a leading cause of injury and loss of independence among older adults. In aged care facilities, fall-related injuries can result in severe complications, prolonged hospitalisation, and diminished quality of life. The challenge in preventing falls lies in detecting early signs of instability without compromising the comfort and dignity of residents. Traditional fall detection systems, such as wearable devices or cameras, often face resistance due to privacy concerns and user discomfort.
A more effective approach is needed—one that ensures safety while remaining unobtrusive and seamlessly integrated into daily life.
The Solution
Researchers at Â鶹Éçmadou Sydney, including Dr Argha and Prof Lovell, are pioneering the use of millimetre-wave (mmWave) radar technology to develop a discreet and highly accurate fall prediction and prevention system. Unlike conventional methods, mmWave radar operates without the need for wearables or intrusive cameras. This advanced system continuously monitors movement patterns, identifying gait changes that indicate an increased risk of falling. By leveraging advanced deep learning models for human pose estimation, the system enables comprehensive gait analysis, allowing for early fall prediction.
How It Works
mmWave radar emits high-frequency radio waves that reflect off a person’s body, capturing movement patterns in real-time. The system then analyses these reflections using machine learning algorithms to detect deviations in walking patterns, balance issues, or sudden posture shifts. Over time, the data is used to create a personalised mobility profile for each resident, allowing caregivers to predict and prevent falls before they occur.
A Model for Intelligent Care
The research integrates several key components:
- Human Pose Estimation and Gait Analysis: The system tracks walking speed, step length, and posture changes.
- Person Reidentification: The system distinguishes between different individuals to maintain personalised monitoring.
- Activity and Human-to-Human Interaction Recognition: Understanding interactions and activities helps detect potential risks.
- Behavioural Trends: Longitudinal data is used to detect gradual declines in mobility
- Real-Time Alerts: Caregivers receive notifications when a resident exhibits signs of instability
- Privacy-Preserving Design: No cameras or personal identifiers are used, ensuring resident comfort and compliance.
Bridging the Gap Between Research and Real-World Implementation
The adoption of mmWave radar technology in aged care settings presents significant advantages. Unlike traditional monitoring systems, this approach is non-invasive and works in all lighting conditions, making it ideal for around-the-clock monitoring.
As part of ongoing research efforts, Â鶹Éçmadou Sydney is collaborating with aged care facilities to pilot the technology and assess its real-world effectiveness. Early trials indicate that mmWave-based monitoring can significantly reduce fall incidents by providing early warning indicators that enable timely intervention.
Towards a Safer Future
Falls among older adults are a major public health concern, and the need for proactive, technology-driven solutions is more urgent than ever. By harnessing the power of mmWave radar, AI, and biomechanics, this research is paving the way for safer, more responsive aged care environments.
Â鶹Éçmadou Sydney’s commitment to leveraging advanced sensing technologies ensures that aged care facilities can move towards predictive, rather than reactive, fall management—enhancing both safety and quality of life for residents worldwide.