Deep Models for Engagement Assessment With Scarce Label Information

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled 20 min of the EEG data for each pilot. The EEG signals were preprocessed and power spectral features were extracted. The deep models were pretrained by the unlabeled data and were fine-tuned by a different proportion of the labeled data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for engagement assessment. The models were then tested on the remaining labeled data. We compared performances of the new data representations with the original EEG features for engagement assessment. Experimental results show that the representations learned by the deep models yielded better accuracies for the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based on different proportions of the labeled data for training, as compared with the corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%) achieved by the original EEG features. Deep models are effective for engagement assessment especially when less label information was used for training

Improving Engagement Assessment by Model Individualization and Deep Learning

This dissertation studies methods that improve engagement assessment for pilots. The major work addresses two challenging problems involved in the assessment: individual variation among pilots and the lack of labeled data for training assessment models. Task engagement is usually assessed by analyzing physiological measurements collected from subjects who are performing a task. However, physiological measurements such as Electroencephalography (EEG) vary from subject to subject. An assessment model trained for one subject may not be applicable to other subjects. We proposed a dynamic classifier selection algorithm for model individualization and compared it to other two methods: base line normalization and similarity-based model replacement. Experimental results showed that baseline normalization and dynamic classifier selection can significantly improve cross-subject engagement assessment. For complex tasks such as piloting an air plane, labeling engagement levels for pilots is challenging. Without enough labeled data, it is very difficult for traditional methods to train valid models for effective engagement assessment. This dissertation proposed to utilize deep learning models to address this challenge. Deep learning models are capable of learning valuable feature hierarchies by taking advantage of both labeled and unlabeled data. Our results showed that deep models are better tools for engagement assessment when label information is scarce. To further verify the power of deep learning techniques for scarce labeled data, we applied the deep learning algorithm to another small size data set, the ADNI data set. The ADNI data set is a public data set containing MRI and PET scans of Alzheimer\u27s Disease (AD) patients for AD diagnosis. We developed a robust deep learning system incorporating dropout and stability selection techniques to identify the different progression stages of AD patients. The experimental results showed that deep learning is very effective in AD diagnosis. In addition, we studied several imbalance learning techniques that are useful when data is highly unbalanced, i.e., when majority classes have many more training samples than minority classes. Conventional machine learning techniques usually tend to classify all data samples into majority classes and to perform poorly for minority classes. Unbalanced learning techniques can balance data sets before training and can improve learning performance

Consumer Engagement: Helping People Want What They Need

Developing or delivering a product, tool, or service that meets consumers' needs and leads to impactful behavior change is a significant challenge. Simply creating tools to foster financial security has not been enough to ensure that consumers will use them, much less benefit from them. Consumer engagement is an approach to tackling these key challenges that focuses on the needs, expectations, and realities of those being served by financial empowerment practitioners.Consumer Engagement: Helping People Want What They Need describes both a philosophy and a process for developing and delivering financial products and services. At the core is the consumer, who is the intended target of financial empowerment efforts and the key stakeholder; he/ she is the actor who ultimately decides what tools to use and is an indispensable source of intelligence about his/her needs and wants.Three pillars define consumer engagement, each of which informs and relies on the others: Demand Focus, Deep Connection, and Enthusiastic Use

Living Labs as a navigation system for innovative business models in the music industry

Media industries and other rapidly evolving, complex, uncertain markets have a hard time to survive if they do not optimize or radically change their business models. This paper analyses the potential of involving all relevant stakeholders of the value network in the development of a business model by means of a panel based multi-method Living Lab approach. Using an in-depth case study analysis, a critical analysis of both the potential value and the weaknesses of such an approach are being assessed. Although some difficulties exist, opening this innovation process and involving external actors in a structural way has the potential to increase the value creation and sustainability of the business model. This paper also stresses the importance of multidisciplinary research on multi-stakeholder involvement in business model innovation