Abstract:
Falling is the main cause of disability and fatality of elderly. In this work, 24 GHz
continuous wave Doppler radar is used to develop a low price fall detection system.
Radar sensor is selected due to its capability of tracking human motions, passing
through covers and walls, its low cost, low power, and small size. Designed system is
further improved to detect and monitor the human’s vital signs to analyze the status of
the fallen person and reduce the consequences of the fall by providing a general idea
about the persons’ situation to the concerned authorities. First, considering all possible
daily activities and fall cases, a dataset with 121 fall and 117 non-fall signatures
are collected. Then, features from both time and frequency domains are extracted
and examined to select the ones that contribute most to distinguish between fall and
non-fall samples. Finally, different machine learning techniques including support
vector machine, naive Bayes, k nearest neighbor, linear discriminant analysis and
decision tree are evaluated to build the most accurate classification model. Proposed
system performed activity classification and fall detection with 88% average accuracy,
heart rate monitoring with 95% average accuracy, and respiration rate monitoring
with 85% average accuracy.
