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