Sensor Based Human Gait Recognition for Drunk State
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 9
Abstract
Many research studies have examined human activity recognition and body posture and gait detection. Relatively fewer studies have investigated specifically into shaky, abnormal gait or how drunken person move in a low lit, uncontrolled setting. This research paper uses markers for tracking the body joints and legs, hands and face and used the position and movement of tracked features to train an SVM classifier for recognizing various abnormal actions while walking such as tripping, falling, shaking, walking side-ways, falling, dragging and walking with helps from others vs normal gait and standing posture. 5 subjects participated in script based simulated drunk actions, expressions and behaviour from a list of actions that represented an abnormal gait and drunk state of mind. The classification results showed 72.8% accuracy under controlled lighting. The accuracy was lower by 3.1% in dim indoor lighting and natural outdoor setup similar to those outside a bar or a low lit street at night.
Authors and Affiliations
Amol S Patwardhan
Keywords
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