Emotion Aware Mobile Application

Nielek, Radosław; Wierzbicki, Adam

doi:10.1007/978-3-642-16732-4_14

Cited by 9 publications

(2 citation statements)

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“…The predictive accuracy of our system (86.17%) is comparative to previous research using mobile phone usage patterns to infer human emotions, however, the training time required (2 weeks) is substantially shorter than other studies (3 weeks to 3 months) [ 11 , 20 , 37 ]. We have observed that participants' usage behaviors changed substantially over time.…”

Section: Discussionmentioning

confidence: 68%

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Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Hung¹,

Yang²,

Chang³

et al. 2016

JMIR Res Protoc

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BackgroundPrompt recognition and intervention of negative emotions is crucial for patients with depression. Mobile phones and mobile apps are suitable technologies that can be used to recognize negative emotions and intervene if necessary.ObjectiveMobile phone usage patterns can be associated with concurrent emotional states. The objective of this study is to adapt machine-learning methods to analyze such patterns for the prediction of negative emotion.MethodsWe developed an Android-based app to capture emotional states and mobile phone usage patterns, which included call logs (and use of apps). Visual analog scales (VASs) were used to report negative emotions in dimensions of depression, anxiety, and stress. In the system-training phase, participants were requested to tag their emotions for 14 consecutive days. Five feature-selection methods were used to determine individual usage patterns and four machine-learning methods were tested. Finally, rank product scoring was used to select the best combination to construct the prediction model. In the system evaluation phase, participants were then requested to verify the predicted negative emotions for at least 5 days.ResultsOut of 40 enrolled healthy participants, we analyzed data from 28 participants, including 30% (9/28) women with a mean (SD) age of 29.2 (5.1) years with sufficient emotion tags. The combination of time slots of 2 hours, greedy forward selection, and Naïve Bayes method was chosen for the prediction model. We further validated the personalized models in 18 participants who performed at least 5 days of model evaluation. Overall, the predictive accuracy for negative emotions was 86.17%.ConclusionWe developed a system capable of predicting negative emotions based on mobile phone usage patterns. This system has potential for ecological momentary intervention (EMI) for depressive disorders by automatically recognizing negative emotions and providing people with preventive treatments before it escalates to clinical depression.

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Section: Discussionmentioning

confidence: 68%

“…Affective computing aims to automatically detect emotions when they arise [ 19 , 20 ]. Prior studies have attempted to infer emotions with various sensors.…”

Section: Introductionmentioning

confidence: 99%