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- All Subjects: human-computer interaction
- Genre: Masters Thesis
- Creators: Eslamifar, Ali
Description
Study in user experience design states that there is a considerable gap between users and designers. Collaborative design and empathetic design methods attempt to make a strong relationship between these two. In participatory design activities, projective `make tools' are required for users to show their thoughts. This research is designed to apply an empathetic way of using `make tools' in user experience design for websites clients, users, and designers.
A magnetic wireframe tool has been used as a `make tool', and a sample project has been defined in order to see how the tool can create empathy among stakeholders. In this study fourth year graphic design students at Arizona State University (ASU), USA, are participating as users, faculty members have the role of clients, and Forty, Inc., a design firm in the Phoenix area, is the design team for the study. All of these three groups are cooperating on re-designing the homepage of the Design School in Herberger Institute for Design and Art (HIDA) at ASU.
A method for applying the magnetic tool was designed and used for each group. Results of users and clients' activities were shared with the design team, and they designed a final prototype for the wireframe of the sample project. Observation and interviews were done to see how participants work with the tool. Also, follow up questionnaires were used in order to evaluate all groups' experiences with the magnetic wireframe. Lastly, as a part of questionnaires, a sentence completion method has been used in order to collect the participants' exact thoughts about the magnetic tool.
Observations and results of data analysis in this research show that the tool was a helpful `make tool' for users and clients. They could talk about their ideas and also designers could learn more about people. The entire series of activities caused an empathetic relationship among stakeholders of the sample project. This method of using `make tools' in user experience design for web sites can be useful for collaborative UX design activities and further research in user experience design with empathy.
A magnetic wireframe tool has been used as a `make tool', and a sample project has been defined in order to see how the tool can create empathy among stakeholders. In this study fourth year graphic design students at Arizona State University (ASU), USA, are participating as users, faculty members have the role of clients, and Forty, Inc., a design firm in the Phoenix area, is the design team for the study. All of these three groups are cooperating on re-designing the homepage of the Design School in Herberger Institute for Design and Art (HIDA) at ASU.
A method for applying the magnetic tool was designed and used for each group. Results of users and clients' activities were shared with the design team, and they designed a final prototype for the wireframe of the sample project. Observation and interviews were done to see how participants work with the tool. Also, follow up questionnaires were used in order to evaluate all groups' experiences with the magnetic wireframe. Lastly, as a part of questionnaires, a sentence completion method has been used in order to collect the participants' exact thoughts about the magnetic tool.
Observations and results of data analysis in this research show that the tool was a helpful `make tool' for users and clients. They could talk about their ideas and also designers could learn more about people. The entire series of activities caused an empathetic relationship among stakeholders of the sample project. This method of using `make tools' in user experience design for web sites can be useful for collaborative UX design activities and further research in user experience design with empathy.
ContributorsEslamifar, Ali (Author) / Heywood, William (Thesis advisor) / Walker, Erin (Committee member) / Takamura, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015