Assessing your mood online: acceptability and use of Moodscope

Drake, Gabrielle; Csipke, Emese; Wykes, Til

doi:10.1017/s0033291712002280

Cited by 40 publications

(48 citation statements)

References 17 publications

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“…Prior work on emotion detection from mobile phone data includes analysis of typing behavior on a mobile phone [5,6] and mobile phone usage [7,8]. The EmotionSense system performed emotion detection directly on mobile phones via analysis of speech with additional sensors collecting information about the user and the environment [9].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

2018

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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.

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Section: Introductionmentioning

confidence: 99%

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

2018

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“…In contrast to emotion prediction based on typing behavior (5,6), smartphone usage (7,8), and smartphone speech recordings (9), we focus on movement data and heart rate data. The EmotionSense system does use accelerometer data, but not for emotion recognition (9).…”

Section: Comparison With Prior Workmentioning

confidence: 99%

“…Prior work on emotion detection from smartphone data includes the analysis of typing behavior on a smartphone (5,6) and smartphone usage (7,8). The EmotionSense system performed emotion detection directly on mobile phones via the analysis of speech, with additional sensors collecting information about the user and his environment including a Bluetooth sensor as a proxy for proximity information, GPS location, and accelerometer for inferring movement vs nonmovement (9).…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

Quiroz¹,

Geangu²,

Yong³

2018

Preprint

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Background: Research in psychology has shown that the way a person walks reflects that person's current mood (or emotional state). Recent studies have started using smartphones to detect emotional states from movement data. Objective: This study investigates the use of movement sensor data from a smart watch to infer an individual's emotional state. We present our findings on a user study with 50 participants. Methods: The experimental design is a mixed-design study; within-subjects (emotions; happy, sad, neutral) and between-subjects (stimulus type: audio visual "movie clips", audio "music clips"). Each participant experienced both emotions in a single stimulus type. All participants walked 250m while wearing a smart watch on one wrist and a heart rate monitor strap on their chest. They also had to answer a short questionnaire (20 items; PANAS) before and after experiencing each emotion. The heart rate monitor serves as a supplementary information to our data. We performed time-series analysis on the data from the smart watch and a t-test on the questionnaire items to measure the change in emotional state. The heart rate data was analyzed using one-way ANOVA. We extracted features from the time-series using sliding windows and used the features to train and validate classifiers that determine an individual's emotion. Results: We had 50 young adults participate in our study, with 49 included for the affective PANAS questionnaire and all for the feature extraction. Participants reported feeling less negative affect after watching sad videos or after listening to the sad music, P < .006. For the task of emotion recognition using classifiers, our results show that the personal models outperformed personal baselines, and achieve median accuracies higher than 78% for all conditions of the design study for the binary classification of happiness vs sadness. Conclusions: Our findings show that we are able to detect the changes in emotional state with data obtained from the smartwatch as well as behavioral responses. Together with the high accuracies achieved across all users for the classification of happy vs sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.

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“…There has already been a modest clinical uptake of digital self-report programs, for example, MoodScope42 which has the ability to send data to family members or healthcare professionals. However, many other programs are yet to be administered systematically outside of research studies 43.…”

Section: Potential Directionsmentioning

confidence: 99%

The promise of digital mood tracking technologies: are we heading on the right track?

Malhi¹,

Hamilton²,

Morris³

et al. 2017

Evid Based Mental Health

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The growing understanding that mood disorders are dynamic in nature and fluctuate over variable epochs of time has compelled researchers to develop innovative methods of monitoring mood. Technological advancement now allows for the detection of minute-to-minute changes while also capturing a longitudinal perspective of an individual's illness. Traditionally, assessments of mood have been conducted by means of clinical interviews and paper surveys. However, these methods are often inaccurate due to recall bias and compliance issues, and are limited in their capacity to collect and process data over long periods of time. The increased capability, availability and affordability of digital technologies in recent decades has offered a novel, non-invasive alternative to monitoring mood and emotion in daily life. This paper reviews the emerging literature addressing the use of digital mood tracking technologies, primarily focusing on the strengths and inherent limitations of using these new methods including electronic self-report, behavioural data collection and wearable physiological biosensors. This developing field holds great promise in generating novel insights into the mechanistic processes of mood disorders and improving personalised clinical care. However, further research is needed to validate many of these novel approaches to ensure that these devices are indeed achieving their purpose of capturing changes in mood. IntroduCtIonOver the last two decades the technological advances in personal devices have been unprecedented. Due to the ability for immediate communication and access to information, we now live in an era in which for the first time 'bodily functions' can be tracked in real time by individuals themselves. This data can then be simultaneously communicated in real time to systems for storage and analysis, allowing for the development of databases sufficiently large and detailed to meaningfully make sense of biometric data. Yet, in practice, few of these technologies are used, especially by clinicians, either for research or day-to-day clinical care. In psychiatric practice, these technologies have the potential to track the symptoms of mood disorders as devices can log objective measures of mood, arousal, activity and sleep, and allow users to input subjective experiences. Additionally, devices have the potential to be part of daily personal care, providing alerts for pharmacological and psychological treatment adherence and undertaking physical activity. Promisingly, with further research, there is the potential for these devices to be embedded within routine clinical management, as algorithms for predicting the dynamic nature of mood disorder presentations and treatment responses are developed. In order to progress to this stage, it is necessary to examine the nature of mood disorders and how existing technologies are being employed to measure this. This review examines the digital techniques that have been used by researchers to capture the dynamic nature of mood disorders and points to ...

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Assessing your mood online: acceptability and use of Moodscope

Cited by 40 publications

References 17 publications

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

Emotion Recognition Using Smart Watch Sensor Data: A Mixed-Design Study (Preprint)

The promise of digital mood tracking technologies: are we heading on the right track?

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