This study examined the effect of temporal changes in corticospinal excitability in motor imagery (MI) and the effect of real‐time guides for MI on excitability changes. The MI task involved wrist flexion and motor evoked potentials using transcranial magnetic stimulation were recorded and examined from the flexor carpi radialis. Ballistic (momentary MI) and tonic (continuous MI) conditions were used, and the duration of each MI was different. In Experiment 1, each MI task was performed using an acoustic trigger. In Experiment 2, a real‐time guide was presented on a computer screen, which provided a visual indication of the onset and duration of the MI task through via moving dots on the screen. The results indicate that the corticospinal excitability changed differently, depending on the duration of MI. Additionally, with real‐time guides, the change in corticospinal excitability became clearer. Thus, corticospinal excitability changes due to the temporal specificities of MI, as well as with actual motor output. Moreover, if MI is actively performed without a guide, it is likely to show an unintended change in corticospinal excitability. It is suggested that when MI is performed with visual guide, the excitatory changes of the corticospinal tract might be different from the actual motor output. Therefore, when using MI for mental practices, it is possible to improve the effect of a guide for MI, such as a visual indicator for motor output. Additionally, when examining neural activities in MI, it may be necessary to consider the characteristics of motion performed by MI.

The development of welfare assistive devices for frail elderly people has attracted significant attention for its effort to improve the quality of life and reduce the burden on caregivers. However, it is challenging to conduct multiple user tests because of the significant burden on the elderly; thus, we need efficient ways to extract insight through different approaches. In this study, we aim to elucidate real-time transitions in users’ emotions and achievement motivation while using such a device. We synthesize an utterance analysis method based on attribution theory, in which all users’ utterances are attributed to four categories (ability, effort, task difficulty, and luck) that follow the developed coding rules. Knowing the transitions in causal attribution allows us to extract salient experiences for users, especially by extracting shifts from them and analyzing why the shift occurred and what exactly was happening before and after the shift. If only salient user experiences can be referenced from the aggregate data, useful information can be extracted in a short time to improve system characteristics and the environment. We discussed the validity and reliability of the proposed method by conducting a user test of an electric-assisted four-wheeled cycle for frail elderly people in Kakegawa city in Shizuoka, Japan. We also succeeded in marking the points that need attention, which is about 33% of the total amount of utterance data (1626 utterances), and thus confirmed the potential of the proposed method. Future research should examine how the developed methodology can help designers improve assistive device development, as well as how it can contribute to other fields such as education and social assistance.

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