Early Alzheimer's Disease Diagnosis Using Wearable Sensors and Multilevel Gait Assessment: A Machine Learning Ensemble Approach

Alzheimer's disease (AD) is a progressive neurological disorder, and mild cognitive impairment (MCI) is a stage between cognitive normal (CN) and AD. Although timely diagnosis is the key to treatment, the conventional diagnostic methods make periodic diagnosis impossible due to various issues, such as pain and cost. Therefore, we propose a method for early diagnosing by focusing on gait, which is safe and efficient. Seven wearable devices with a built-in inertial measurement unit were used to collect gait data from 145 subjects, and seven gait experiment paradigms, including multilevel subtasks, were developed to clarify the characteristics of gait of each severity. Based on the acquired gait datasets, we proposed a machine learning (ML)-based classification model-an elimination method-based ensemble and oversampling model-which is applied to our proposed method. Experimental results show that our proposed model is effective in detecting the early stages of AD and demonstrate the potential of using an auxiliary diagnostic tool.

Automated summary

Alzheimer's disease (AD) is a progressive neurological disorder, with mild cognitive impairment (MCI) being an intermediate stage. Conventional diagnostic methods for AD have limitations that prevent periodic diagnosis due to factors such as cost and discomfort. Therefore, a new method focusing on gait as an early diagnostic tool is proposed. The method involves collecting gait data using wearable devices and developing experimental paradigms to analyze gait characteristics at different severity levels. A machine learning-based classification model is then applied to the acquired data, showing effectiveness in detecting early stages of AD and potential as an auxiliary diagnostic tool.