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## Fall prevention and detection

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In both research and clinical settings a fall incident is commonly defined as unintentionally coming to rest on the ground, floor, or other lower level. Such a fall incident is experienced at least once a year by approximately one in three adults older than 65. This number increases with age and frailty level. Yearly, 37.3 million fall incidents have physiological consequences. Additionally, psychological consequences, such as loss of mobility and independence often restrict the ability of older adults to perform daily activities. By detecting when a person has an elevated fall risk, and taking preventive measures when necessary, the number of fall incidents can be reduced. The first part of this chapter will therefore focus on automatic fall risk assessment techniques. Following this, the second part focuses on fall detection systems. As not all fall incidents can be prevented these systems can reduce the consequences of a fall incident by ensuring that timely aid is given.

Chapter Contents:

• 11.1 Introduction
• 11.2 Fall risk estimation
• 11.2.1 Using gait parameters to automatically assess the fall risk of a person
• 11.2.2 Sensors which measure fall risk-related parameters
• 11.2.2.1 Monitoring gait velocity using video cameras
• 11.2.2.2 Assessing temporal and spatial gait parameters through footstep detection algorithms using audio nodes
• 11.2.2.3 Measuring activity levels with PIR sensors
• 11.2.2.4 Other sensors
• 11.2.3 Closing the loop
• 11.2.3.1 Data visualisation
• 11.2.3.2 Automatic data interpretation
• 11.3 Fall detection
• 11.3.1 Accelerometer-based fall detection systems
• 11.3.3 Video-based fall detection
• 11.3.4 Kinect-based fall detection
• 11.4 Conclusion
• References

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