Published papers

© 2017 IPSJ. Mental health has received increasing attention in recent years, and a correlation between social interaction during eating and mental health noted. The purpose of this research is to determine if wearable devices can be used to recognize whether a person is eating alone or has company. Using a watch-type device and smartphone, the data, while eating in daily life, was collected. Using this data, we were able to recognize whether a person is eating alone or has company with an accuracy of 96.3%.

© 2018 Association for Computing Machinery. Although mental illness is one of the most serious social problems, stress does not necessarily have a negative effect, on the contrary, an optimal amount of stress can result in individuals being able to perform better in tasks. While there have been several studies on estimating stress level from smartphone usage logs and several effective features were revealed as a result, there are few studies on estimating cognitive performance. Thus, in this paper, we explore several factors affecting the estimation of cognitive performance from smartphone logs. To conduct the analysis, we collected smartphone usage logs and Go/No-Go task data for 6 weeks from 39 participants in the wild. We found that the time to find intended app is related to cognitive performance. This result suggests that measuring the time to find intended app can be the effective feature of estimating cognitive performance from smartphone logs.

© 2018 IPSJ. Recently, inferring the state of people's mental health via passive mobile sensing has attracted significant attention. Previous studies have used the self-assessed stress levels as the ground truth; however, these are subjective measures. In this study, we use a physiologically-assessed stress metric to minimize the effect of participant subjectivity and further estimate it using behavioral features based on the smartphone usage logs. We initially requested the study participants (39 participants) to attach heart rate sensors for 8 hours per day and simultaneously collected continuous heart rate data and smartphone logs for 42 days. Further, we divided the participants into four types via clustering using the behavioral features derived from their smartphone sensor logs and trained each model via supervised learning using the heart rate data as the ground truth. Our results exhibit that the proposed method is more accurate (71%) as compared to the baseline method (54%). This demonstrates that physiologically-assessed stress levels can be estimated based on the implicit features that are gathered from the smartphone logs.

© 2019 Association for Computing Machinery. Recognizing human cognitive performance is important for preserving working efficiency and preventing human error. This paper presents a method for estimating cognitive performance by leveraging multiple information available in a smartphone. The method employs the Go-NoGo task to measure cognitive performance, and fuses contextual and behavioral features to identify the level of performance. It was confirmed that the proposed method could recognize whether cognitive performance was high or low with an average accuracy of 71%, even when only referring to inertial sensor logs. Combining sensing modalities improved the accuracy up to 74%.

Limb apraxia is a cognitive-motor disorder typically resulting from left hemisphere stroke, characterized by an inability to perform skilled limb movements despite intact motor and sensory functions. Previous studies suggest that individuals with apraxia exhibit deficits in sensorimotor integration, particularly in detecting temporal discrepancies between movement and sensory feedback. However, whether these deficits affect explicit sense of agency (SoA) remains unclear. This study investigated the time window for sensorimotor integration and explicit SoA in post-stroke patients with and without apraxia. Twenty patients with left hemisphere stroke participated in a delay detection task assessing sensory-sensory and sensorimotor integration and an agency attribution task measuring explicit SoA. The results demonstrated that apraxic patients had a significantly prolonged delay detection threshold and reduced steepness in the active movement condition, indicating an altered time window for sensorimotor integration. In contrast, there were no significant differences between apraxic and non-apraxic patients in the time window for explicit SoA. These findings suggest that while apraxic patients exhibit deficits in sensorimotor integration, their explicit SoA remains preserved. This dissociation supports the notion that explicit SoA may be maintained through compensatory cognitive processes despite impairments at the sensorimotor level. Further research is needed, considering the limitations of this study, to achieve a more comprehensive understanding of SoA in apraxia.

This study investigates the short-term effects of biofeedback rehabilitation on postural balance performance, addressing a significant gap in existing research that has focused primarily on long-term outcomes. The present study aims to test the following hypothesis: Changes in the way sensory information is used through biofeedback training will lead to changes in postural balance performance in the short term. Experiments were conducted with five young, healthy individuals. Participants underwent biofeedback training sessions involving tasks such as maintaining the center of pressure of the feet within specific targets, and performed quiet standing tasks and standing tasks with both open and closed eyes before and after the training sessions.The results showed suggestive correlations between changes in sway during quiet standing and changes in sway with eyes open and closed before and after the training session, which differed between the training and control groups. This supports the hypothesis and suggests that biofeedback training may indirectly affect postural balance ability by altering the way sensory information is used and the existence of diverse strategies.Clinical Relevance - This leads to more effective biofeedback training designs based on reweighting in the use of sensory information

The sense of agency refers to the feeling of initiating and controlling one’s actions and their resulting effects on the external environment. Previous studies have uncovered behavioral evidence of excessive self-attribution and, conversely, a reduction in the sense of agency in patients with schizophrenia. We hypothesize that this apparent paradox is likely to result from impairment in lower-level processes underlying the sense of agency, combined with a higher-level compensational bias. The present study employed three behavioral tasks utilizing the same stimuli and experimental design to systematically evaluate multiple factors that influence the sense of agency, including motor control, sensorimotor processing, and self-attribution. Participants’ real-time mouse movements were combined with prerecorded motions of others in ratios of 30/70, 55/45, or 80/20, with an additional angular bias of either 0° or 90°. Twenty-six patients with schizophrenia and 27 health control volunteers participated in the three tasks. Patients with schizophrenia performed significantly worse in the reaching and control detection tasks than healthy controls. However, their self-attribution in the control judgment task was comparable to that of the healthy controls. Patients with schizophrenia were impaired in motor control components and in the detection of control using sensorimotor information, but their evaluation of agency remained relatively less affected. This underscores the importance of distinguishing between different subcomponents when addressing the abnormal sense of agency in patients with schizophrenia. Subsequent cluster analysis revealed that the combined task performance accurately distinguished between the patients and healthy control participants.

Sense of agency (SoA) is the subjective experience of individuals that they are initiators and controllers of their own actions, but it is not known how the time window for SoA changes developmentally over the lifetime. The present study examined developmental changes in the time window for SoA in school-age children (6–12 years), adolescents (16–18 years), young adults (20–25 years), adults (28–64 years), and older adults (65–83 years). The current results show that the time window for SoA is longer in young adults than in school-age children, adolescents, adults, and older adults, suggesting that young adulthood may be an important period of developmental change in the time window for SoA across the lifespan.

Aberrant sense of agency (SoA, a feeling of control over one’s own actions and their subsequent events) has been considered key to understanding the pathology of schizophrenia. Behavioral studies have demonstrated that a bidirectional (i.e., excessive and diminished) SoA is observed in schizophrenia. Several neurophysiological and theoretical studies have suggested that aberrancy may be due to temporal delays (TDs) in sensory-motor prediction signals. Here, we examined this hypothesis via computational modeling using a recurrent neural network (RNN) expressing the sensory-motor prediction process. The proposed model successfully reproduced the behavioral features of SoA in healthy controls. In addition, simulation of delayed prediction signals reproduced the bidirectional schizophrenia-pattern SoA, whereas three control experiments (random noise addition, TDs in outputs, and TDs in inputs) demonstrated no schizophrenia-pattern SoA. These results support the TD hypothesis and provide a mechanistic understanding of the pathology underlying aberrant SoA in schizophrenia.

Background: There is increasing evidence that the stochastic resonance (SR) phenomenon provided by subthreshold mechanical noise stimulation improves the sensory-motor system. However, the effect of SR on children with developmental coordination disorder (DCD) is unclear. The purpose of this study was to assess whether SR activated by subthreshold vibrotactile noise stimulation of the wrist influences manual dexterity in children with DCD. Methods: A double-blind interventional study was conducted. Participants were 30 children (age: 9.3 ± 1.44 years, range 6–11 years; 27 male, three female; 25 right-handed, five left-handed) meeting DCD diagnostic criteria in DSM-5. The manual dexterity test was administered the day before SR intervention (baseline-data). SR was elicited using subthreshold vibrotactile noise stimulation at 60% of the vibrotactile threshold measured at the wrist. SR was delivered two times and the manual dexterity test was administered during each SR stimulation block (SR-on condition) and after each SR stimulation block (SR-off), for a total of four measurements. Target outcomes were the component score, the standard score, and the percentile score of the manual dexterity test. Results: The manual dexterity test scores in the SR-on condition were significantly improved compared to scores at the baseline and in the SR-off condition (p < 0.001). Conclusions: The present study showed that subthreshold noise stimulation eliciting SR significantly improved manual dexterity outcomes in children with DCD during stimulation but not after stimulation. Future studies will need to investigate the carry-over effects of SR stimulation.

Research on mental health states involves paying increasing attention to changes in daily life. Researchers have attempted to understand such daily changes by relying on self-reporting through frequent assessment using devices (smartphones); however, they are mostly focused on a single aspect of mental health. Assessing the mental health of a person from various perspectives may help in the primary prevention of mental illness and the comprehensive measurement of mental health. In this study, we used users' smartphone logs to build a model to estimate whether the scores on three types of questionnaires related to quality of life and well-being would increase compared to the previous week (fluctuation model) and whether they would be higher compared to the average for that user (interval model). Sixteen participants completed three questionnaires once per week, and their smartphone logs were recorded over the same period. Based on the results, estimation models were built, and the F-score ranged from 0.739 to 0.818. We also analyzed the features that the estimation model emphasized. Information related to “physical activity,” such as acceleration and tilt of the smartphone, and “environment,” such as atmospheric pressure and illumination, were given more weight in the estimation than information related to “cyber activity,” such as usage of smartphone applications. In particular, in the Positive and Negative Affect Schedule (PANAS), 9 out of 10 top features in the fluctuation model and 7 out of 10 top features in the interval model were related to activities in the physical world, suggesting that short-term mood may be particularly heavily influenced by subjective activities in the human physical world.

© Copyright © 2020 Yano, Hayashi, Murata, Imamizu, Maeda and Kondo. A sense of agency (SoA) is the experience of subjective awareness regarding the control of one's actions. Humans have a natural tendency to generate prediction models of the environment and adapt their models according to changes in the environment. The SoA is associated with the degree of the adaptation of the prediction models, e.g., insufficient adaptation causes low predictability and lowers the SoA over the environment. Thus, identifying the mechanisms behind the adaptation process of a prediction model related to the SoA is essential for understanding the generative process of the SoA. In the first half of the current study, we constructed a mathematical model in which the SoA represents a likelihood value for a given observation (sensory feedback) in a prediction model of the environment and in which the prediction model is updated according to the likelihood value. From our mathematical model, we theoretically derived a testable hypothesis that the prediction model is updated according to a Bayesian rule or a stochastic gradient. In the second half of our study, we focused on the experimental examination of this hypothesis. In our experiment, human subjects were repeatedly asked to observe a moving square on a computer screen and press a button after a beep sound. The button press resulted in an abrupt jump of the moving square on the screen. Experiencing the various stochastic time intervals between the action execution (button-press) and the consequent event (square jumping) caused gradual changes in the subjects' degree of their SoA. By comparing the above theoretical hypothesis with the experimental results, we concluded that the update (adaptation) rule of the prediction model based on the SoA is better described by a Bayesian update than by a stochastic gradient descent.

© 2018, The Author(s). Humans are capable of associating actions with their respective consequences if there is reliable contingency between them. The present study examined the link between the reliability of action consequence and the readiness potential (RP), which is a negative potential observed from about 1–2 s prior to the onset of an action with electroencephalography. In a condition of constant outcome, the participants’ voluntary action always triggered beep sounds; thus, they were able to perceive the contingency between their action and the sound. In contrast, in a condition of inconstant outcome, the participants’ actions only triggered the sound in half the trials. We found that both the early and late RPs were larger in the condition of constant compared to the condition of inconstant outcome. Our results showed that the RPs preceding the voluntary action reflected the reliability of action consequence. In other words, the action-effect contingency enhanced neural activities prior to the action.