A mobile application to report and detect 3D body emotional poses

García‐Magariño, Iván; Cerezo, Eva; Plaza, Inmaculada; Chittaro, Luca

doi:10.1016/j.eswa.2019.01.021

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…The classification metrics in average of 81.67% was relatively high if compared to the recent works in the field of emotion detection, such as the ones about emotion detection from touch interactions when typing on smartphones [18] and emotion detection from body poses [19]. Notice that in the context of our case study, emotions are very subjective as well as the opinions about what meals are usually eaten or recommended in certain emotional states.…”

Section: Discussionmentioning

confidence: 76%

Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons

2019

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Internet of Things (IoT) widely use analysis of data with artificial intelligence (AI) techniques in order to learn from user actions, support decisions, track relevant aspects of the user, and notify certain events when appropriate. However, most AI techniques are based on mathematical models that are difficult to understand by the general public, so most people use AI-based technology as a black box that they eventually start to trust based on their personal experience. This article proposes to go a step forward in the use of AI in IoT, and proposes a novel approach within the Human-centric AI field for generating explanations about the knowledge learned by a neural network (in particular a multilayer perceptron) from IoT environments. More concretely, this work proposes two techniques based on the analysis of artificial neuron weights, and another technique aimed at explaining each estimation based on the analysis of training cases. This approach has been illustrated in the context of a smart IoT kitchen that detects the user depression based on the food used for each meal, using a simulator for this purpose. The results revealed that most auto-generated explanations made sense in this context (i.e. 97.0%), and the execution times were low (i.e. 1.5 ms or lower) even considering the common configurations varying independently the number of neurons per hidden layer (up to 20), the number of hidden layers (up to 20) and the number of training cases (up to 4,000). INDEX TERMS Explainable artificial intelligence, human-centric artificial intelligence, Internet of Things, multilayer perceptron, smart kitchen, emotion detection.

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Section: Discussionmentioning

confidence: 76%

Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons

2019

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Your Smartphone Knows you Better than you May Think: Emotional Assessment ‘on the Go’ Via TapSense

Ghosh

Löchner

Mitra

et al. 2022

Quantifying Quality of Life

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Quality of life (QoL) is a subjective term often determined by various aspects of living, such as personal well-being, health, family, and safety. QoL is challenging to capture objectively but can be anticipated through a person’s emotional state; especially positive emotions indicate an increased QoL and may be a potential indicator for other QoL aspects (such as health, safety). Affective computing is the study of technologies that can quantitatively assess human emotions from external clues. It can leverage different modalities including facial expression, physiological responses, or smartphone usage patterns and correlate them with the person’s life quality assessments. Smartphones are emerging as a main modality, mostly because of their ubiquitous availability and use throughout daily life activities. They include a plethora of onboard sensors (e.g., accelerometer, gyroscope, GPS) and can sense different user activities passively (e.g., mobility, app usage history). This chapter presents a research study (here referred to as the TapSense study) that focuses on assessing the individual’s emotional state from the smartphone usage patterns. In this TapSense study, the keyboard interaction of n = 22 participants was unobtrusively monitored for 3 weeks to determine the users’ emotional state (i.e., happy, sad, stressed, relaxed) using a personalized machine learning model. TapSense can assess emotions with an average AUCROC of 78%(±7% std). We summarize the findings and reflect upon these in the context of the potential developments within affective computing at large, in the long term, indicating a person’s quality of life.

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Feature-Based Understanding of Human Emotions

Moody

Jeong

2019

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)

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A mobile application to report and detect 3D body emotional poses

Cited by 8 publications

References 29 publications

Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons

Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons

Your Smartphone Knows you Better than you May Think: Emotional Assessment ‘on the Go’ Via TapSense

Feature-Based Understanding of Human Emotions

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