Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device

In this paper, a novel approach for abnormal gait and tremor detection using smart walking cane is introduced. Periodic muscle movement associated with Parkinson’s disease, such as arm shaking, vibrating arm, trembling fingers, rhythmic wrist movements, normal pattern, was learned classified linear discriminant analysis. Although detecting symptoms related to disease sticks might look trivial at first, throughout history, or stick has been used an assistive device aid in ambulating, especially the elderly disabled, so embedding devices (that can learn ambulating pattern detect anomalies it) will help early of diseases facilitate intervention. This non-intrusive, privacy issues being experienced visual models do not arise, users need wear any special bracelet monitoring, they only pick up when wish move. The simplicity efficient usage technique ambulatory also demonstrated research. We extracted step counts, fall data other valuable features from cane, detected by isolation forest one-class support vector machine (SVM) methods. Falls were easily naturally which had different alert modes (a soft lost equilibrium picked within 15 s, strong otherwise). Intervention systems are proposed forestall limit possibility type 2 error.