Activity

Fall detection devices have seen much attention over time, yet creating reliable yet non-intrusive detection systems is still challenging.

Movement samples among elderly individuals vary based on personal factors like height, weight and gender – which affects FDS performance significantly.

Conclusions

This paper offers an exhaustive taxonomy and comparative analysis of existing FDSs to assist researchers, medical professionals, designers and developers of new FDSs as well as users in selecting an ideal system that best meets their needs. By doing so, this will facilitate research as well as design of future systems. Alarm for women In addition to these benefits, comparing performance metrics between systems will also assist users in selecting an ideal FDS for them.

No matter their architecture, sensors or wireless protocol technology employed, most of the systems analyzed in this article share similar features: they monitor subject movements and posture to detect any indications of falls, then trigger a notification mechanism to alert caregivers or relevant individuals of such events. Performance was evaluated through metrics like sensitivity/specificity ratio and power consumption/weight ratio ratio evaluation.

Studies revealed that FDSs vary depending on a subject’s height and weight, especially those using inertial sensor data for analysis. gps tracker for dementia patients This is likely because body movements during a fall differ depending on height; thus resulting in changes to acceleration peak levels as well as sensor positions.

Despite these challenges, most of the devices analyzed were shown to successfully detect and respond to falls; however, more real world testing should be completed to ensure accuracy of these systems.