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BackgroundResearch in psychology has shown that the way a person walks reflects that person’s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data.ObjectiveThe objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual’s emotional state. We present our findings of a user study with 50 participants.MethodsThe experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual “movie clips” and audio “music clips”). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual’s emotion.ResultsOverall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P<.006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness.ConclusionsOur findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.

Our aim was to examine the effect of a smartphone's presence on learning and memory among undergraduates. A total of 119 undergraduates completed a memory task and the Smartphone Addiction Scale (SAS). As predicted, those without smartphones had higher recall accuracy compared to those with smartphones. Results showed a significant negative relationship between phone conscious thought, "how often did you think about your phone", and memory recall but not for SAS and memory recall. Phone conscious thought significantly predicted memory accuracy. We found that the presence of a smartphone and high phone conscious thought affects one's memory learning and recall, indicating the negative effect of a smartphone proximity to our learning and memory.

This study investigates the use of accelerometer data from a smart watch to infer an individual's emotional state. We present our preliminary findings on a user study with 50 participants. Participants were primed either with audio-visual (movie clips) or audio (classical music) to elicit emotional responses. Participants then walked while wearing a smart watch on one wrist and a heart rate strap on their chest. Our hypothesis is that the accelerometer signal will exhibit different patterns for participants in response to different emotion priming. We divided the accelerometer data using sliding windows, extracted features from each window, and used the features to train supervised machine learning algorithms to infer an individual's emotion from their walking pattern. Our discussion includes a description of the methodology, data collected, and early results.

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Herpes simplex virus type 1 (HSV-1) as a possible infectious etiology in Alzheimer’s disease (AD) has been proposed since the 1980s. The accumulating research thus far continues to support the association and a possible causal role of HSV-1 in the development of AD. HSV-1 has been shown to induce neuropathological and behavioral changes of AD, such as amyloid-beta accumulation, tau hyperphosphorylation, as well as memory and learning impairments in experimental settings. However, a neuroanatomical standpoint of HSV-1 tropism in the brain has not been emphasized in detail. In this review, we propose that the hippocampal vulnerability to HSV-1 infection plays a part in the development of AD and amnestic mild cognitive impairment (aMCI). Henceforth, this review draws on human studies to bridge HSV-1 to hippocampal-related brain disorders, namely AD and aMCI/MCI. Next, experimental models and clinical observations supporting the neurotropism or predilection of HSV-1 to infect the hippocampus are examined. Following this, factors and mechanisms predisposing the hippocampus to HSV-1 infection are discussed. In brief, the hippocampus has high levels of viral cellular receptors, neural stem or progenitor cells (NSCs/NPCs), glucocorticoid receptors (GRs) and amyloid precursor protein (APP) that support HSV-1 infectivity, as well as inadequate antiviral immunity against HSV-1. Currently, the established diseases HSV-1 causes are mucocutaneous lesions and encephalitis; however, this review revises that HSV-1 may also induce and/or contribute to hippocampal-related brain disorders, especially AD and aMCI/MCI.

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