JMIR Publications

ABSTRACT

Background:

The rising number of dementia patients has become a serious social problem worldwide. To detect dementia in patients at an early stage, many studies have been made to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they are focusing examinations for cognitive functions or conversational speech during the examinations. On the other hand, conversational humanoid robots are expected to be used in elderly care to help to reduce the work of care and monitoring through interaction.

Objective:

For early detection of mild cognitive impairment (MCI) through conversations between elderly people and humanoid robots without specific examinations such as neuropsychological examination.

Methods:

We collected the conversation data during neuropsychological examination (MMSE) and daily conversation with a humanoid robot from a total of 94 participants (47 cognitively normal and 47 patients with MCI). We extracted 17 types of prosodic and acoustic features such as duration of response time and jitter from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into cognitively normal and MCI patients. Furthermore, we conducted an automatic classification experiment using SVM to verify whether possibility of automatically classify these two group from conversation with humanoid robot by the features identified in statistical significance test.

Results:

We obtained significant differences in 5 out of 17 types of features from the MMSE conversational speech. Duration of response time, duration of silence periods and proportion of silent periods were shown significant difference (p<.001) and met the reference value r=0.1 (Small) of the effect size. Additionally, filler periods(p<.01) and proportion of fillers (p=0.02) were shown significant difference, however these were not met the reference value of the effect size. In contrast, we obtained significant differences in 16 out of 17 types from the speech in the everyday conversations with the humanoid robot. Duration of response time, duration of speech periods, jitter (local, rap, ppq5, ddp), shimmer (local, apq3, apq5, apq11, dda) and F0cov (Coefficient of variation of the fundamental frequency) were shown significant difference(p<.001). In addition, duration of response time, duration of silence periods, filler period and proportion of fillers were significant difference(p<.05). However, only jitter(local) was met the reference value r=0.1 (Small) of effect size. In automatic classification experiment for classification of MCI patients and cognitively normal elderly adults, the results were shown 66.0% accuracy when MMSE conversational speech and 68.1% in daily conversation with humanoid robot.

Conclusions:

This study suggests the possibility of early and simple screening for patients with MCI by using prosodic and acoustic features from daily conversation with a humanoid robot with the same level of accuracy as human conversations in MMSE tests.