OBJECTIVE: Aimless movement or wandering may be a symptom of mild cognitive impairment (MCI) that arises as a consequence of confusion and forgetfulness. This paper presents a support vector machine (SVM) framework based on movement analysis for the prediction of the onset and progression of MCI. METHODS: Movement data of 22 subjects with MCI, and 22 other healthy subjects, living independently in smart homes were collected for ten years using motion sensors. Features were extracted from the sensor data using movement metrics, including cyclomatic complexity, detrended fluctuation analysis, fractal index, entropy, and room transitions. Two different SVM classification algorithms were trained using the features, first to predict the progression of MCI in the post-transition period, and second to predict the onset of MCI in the pre-transition phase. RESULTS: The two SVMs were able to detect the onset six months earlier than the clinical diagnosis. The model accuracy in classifying MCI increased monotonically from the onset month and reached maximum (81%) at the 11th post-transition month. The features of cyclomatic complexity contributed significantly to the prediction results. CONCLUSION: Findings support the use of movement complexity measures and machine learning for monitoring cognitive behavior in an independent living environment.
OBJECTIVE: Aimless movement or wandering may be a symptom of mild cognitive impairment (MCI) that arises as a consequence of confusion and forgetfulness. This paper presents a support vector machine (SVM) framework based on movement analysis for the prediction of the onset and progression of MCI. METHODS: Movement data of 22 subjects with MCI, and 22 other healthy subjects, living independently in smart homes were collected for ten years using motion sensors. Features were extracted from the sensor data using movement metrics, including cyclomatic complexity, detrended fluctuation analysis, fractal index, entropy, and room transitions. Two different SVM classification algorithms were trained using the features, first to predict the progression of MCI in the post-transition period, and second to predict the onset of MCI in the pre-transition phase. RESULTS: The two SVMs were able to detect the onset six months earlier than the clinical diagnosis. The model accuracy in classifying MCI increased monotonically from the onset month and reached maximum (81%) at the 11th post-transition month. The features of cyclomatic complexity contributed significantly to the prediction results. CONCLUSION: Findings support the use of movement complexity measures and machine learning for monitoring cognitive behavior in an independent living environment.
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