The dual-task paradigm is a promising procedure for estimating cognitive status and may also be collaterally used to reduce cognitive decline and prevent dementia. In this paper, we use the minimental state exam (MMSE) to the assess cognitive status in the elderly as a reference and investigate the potential of using machine learning for early detecting cognitive impairment in the elderly. Although many studies have suggested that dual-task performance, in which participants perform a cognitive task while walking, is associated with cognition, they only considered the correlation between cognitive parameters and simple gait feature, such as gait speed, through the statistical analysis. We instead use a Kinect sensor to capture participants' whole-body movements and extract a rich gait feature that has the ability to exhibit different tendencies of movements between healthy and cognitive-impaired elderlies. In our experiments, a classifier based on the dual-task gait feature achieved a higher performance than the one based on the single-task feature; the performance of the rich gait feature was better than that of a simple one, and; an optimal detection performance was achieved with an MMSE cutoff score of 25. We positively validated that the proposed method could early detect elderly with lower MMSE scores based on dual-task gait feature with a promising performance. Our approach can support early and automated diagnosis of cognitive impairment. INDEX TERMS Cognitive impairment, dual-task, elderly, machine learning, signal processing.

Gait is a commonly used biometric for human recognition. Its main advantage relies on its ability to identify people at distances at which other biometrics fail. In this paper, we develop a new approach for gait recognition that combines the distance transform with curvatures of local contours. We call our gait feature template the normal distance map. Our method encodes both body shapes and boundary curvatures into a novel feature descriptor that is more robust than existing gait representations. We evaluate our approach on the widely used and challenging USF and CASIA-B datasets. Furthermore, we evaluate it on the OU-ISIR gait dataset, the largest one available in the literature, to obtain statistically reliable results. We verify our approach is significantly superior to the current state-of-the-art under most conditions.

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Traditional approaches for the screening of cognitive function are often based on paper tests, such as Mini-Mental State Examination (MMSE), that evaluate the degree of cognitive impairment and provide a score of patient’s mental ability. Procedures for conducting paper tests require time investment involving a questioner and not suitable to be carried out frequently. Previous studies showed that dementia impaired patients are not capable of multi-tasking efficiently. Based on this observation an automated system utilizing Kinect device for collecting primarily patient’s gait data who carry out locomotion and calculus tasks individually (i.e., single-tasks) and then simultaneously (i.e., dual-task) was introduced. We installed this system in three elderly facilities and collected 10,833 behavior data from 90 subjects. We conducted analyses of the acquired information extracting 12 features of single- and dual-task performance developed a method for automatic dementia score estimation to investigate determined which characteristics are the most important. In result, a machine learning algorithm using single and dual-task performance classified subjects with an MMSE score of 23 or lower with a recall 0.753 and a specificity 0.799. We found the gait characteristics were important features in the score estimation, and referring to both single and dual-task features was effective.