Exploring Hand-Based Haptic Interfaces for Mobile Interaction Design

Event type: 
Doctoral dissertation
Doctoral dissertation
Respondent: 
Yi-Ta Hsieh
Opponent: 
Professor Kaisa Väänänen (Tampere University of Technology, Finland)
Custos: 
Professor Giulio Jaccuci (University of Helsinki)
Event time: 
2017-06-08 12:00 to 15:00
Place: 
University of Helsinki Exactum Building, Auditorium CK112 (Gustaf Hällströmin katu 2b)
Description: 

M.Sc. Yi-Ta Hsieh will defend his doctoral thesis Exploring Hand-Based Haptic Interfaces for Mobile Interaction Design on Thursday the 8th of June 2017 at 12 o'clock noon in the University of Helsinki Exactum Building, Auditorium CK112 (Gustaf Hällströmin katu 2b). His opponent is Professor Kaisa Väänänen (Tampere University of Technology, Finland), and custos Professor Giulio Jaccuci (University of Helsinki). The defence will be held in English.

Exploring Hand-Based Haptic Interfaces for Mobile Interaction Design

Visual attention is crucial in mobile environments, not only for staying aware of dynamic situations, but also for safety reasons. However, current mobile interaction design forces the user to focus on the visual interface of the handheld device, thus limiting the user's ability to process visual information from their environment. In response to these issues, a common solution is to encode information with on-device vibrotactile feedback. However, the vibration is transitory and is often difficult to perceive when mobile. Another approach is to make visual interfaces even more dominant with smart glasses, which enable head-up interaction on their see-through interface. Yet, their input methods raise many concerns regarding social acceptability, preventing them from being widely adopted. There is a need to derive feasible interaction techniques for mobile use while maintaining the user's situational awareness, and this thesis argues that solutions could be derived through the exploration of hand-based haptic interfaces.

The objective of this research is to provide multimodal interaction for users to better interact with information while maintaining proper attention to the environment in mobile scenarios. Three research areas were identified. The first is developing expressive haptic stimuli, in which the research investigates how static haptic stimuli could be derived. The second is designing mobile spatial interaction with the user's surroundings as content, which manifests situations in which visual attention to the environment is most needed. The last is interacting with the always-on visual interface on smart glasses, the seemingly ideal solution for mobile applications. The three areas extend along the axis of the demand of visual attention on the interface, from non-visual to always-on visual interfaces.

Interactive prototypes were constructed and deployed in studies for each research area, including two shape-changing mechanisms feasible for augmenting mobile devices and a spatial-sensing haptic glove featuring mid-air hand-gestural interaction with haptic support. The findings across the three research areas highlight the immediate benefits of incorporating hand-based haptic interfaces into applications. First, shape-changing interfaces can provide static and continuous haptic stimuli for mobile communication. Secondly, enabling direct interaction with real-world landmarks through a haptic glove and leaving visual attention on the surroundings could result in a higher level of immersed experience. Lastly, the users of smart glasses can benefit from the unobtrusive hand-gestural interaction enabled by the isolated tracking technique of a haptic glove. 

Overall, this work calls for mobile interaction design to consider haptic stimuli beyond on-device vibration, and mobile hardware solutions beyond the handheld form factor. It also invites designers to consider how to confront the competition of cognitive resources among multiple tasks from an interaction design perspective.

Machine Learning Coffee Seminar: "Learning Data Representation by Large-Scale Neighbor Embedding"

Lecturer : 
Zhirong Yang
Event type: 
HIIT seminar
Event time: 
2017-06-12 09:15 to 10:00
Place: 
Lecture room T2, CS building, Konemiehentie 2, Otaniemi
Description: 

Zhirong Yang, Professor of Computer Science, Aalto University

Learning Data Representation by Large-Scale Neighbor Embedding

Abstract: Machine learning, the state-of-the-art data science, has been increasingly influencing our life. Encoding data in a suitable vector space is the fundamental starting point for machine learning. A good vector coding should respect the relations among the data items. However, conventional methods that preserve pairwise or higher order relationship are very slow and consequently they can handle only small-scale data sets. We have been developing a family of unsupervised methods called large-scale Neighbor Embedding (NE) which substantially accelerate the vector coding. Our method can thus learn low-dimensional vector representation for mega-scale data according to their neighborhoods in the original space. With our efficient algorithms and a wealth of neighborhood information, Neighbor Embedding significantly outperforms small-scale NE and many other existing approaches for learning data representation. Besides generic feature extraction, our work also delivers two important tools as special cases of Neighbor Embedding for data visualization and cluster analysis, which scales up these applications by an order of magnitude and enables the current-sized visualization and clustering for interactive use. Because neighborhood information is naturally and massively available in many areas, our method has wide applications as a critical component in scientific research, next-generation DNA sequence analysis, natural language processing, educational cloud, financial data analysis, market studies, etc.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

* we'll have a summer break and continue on September 4, 2017 *

Welcome!

Machine Learning Coffee Seminar "Graphics Meets Vision Meets Machine Learning"

Lecturer : 
Jaakko Lehtinen
Event type: 
HIIT seminar
Event time: 
2017-05-29 09:15 to 10:00
Place: 
Seminar room T5, CS building, Konemiehentie 2, Otaniemi
Description: 

Jaakko Lehtinen, Professor of Computer Science, Aalto University

Graphics Meets Vision Meets Machine Learning

Abstract: TBA

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

June 5, Kumpula: Jarno Vanhatalo
June 12, Otaniemi: Zhirong Yang: Learning Data Representation by Large-Scale Neighbor Embedding

* after June 12, we'll have a summer break and continue on September 4, 2017 *

Welcome!

Machine Learning Coffee seminar "On Priors and Bayesian Variable Selection in Large p, Small n Regression"

Lecturer : 
Aki Vehtari
Event type: 
HIIT seminar
Event time: 
2017-05-22 09:15 to 10:00
Place: 
Exactum D123, Kumpula
Description: 

Aki Vehtari, Associate Professor in Computational Science, Aalto University

On Priors and Bayesian Variable Selection in Large p, Small n Regression

Abstract: The Bayesian approach is well known for using priors to improve inference, but equally important part is the integration over the uncertainties. I first present recent development in hierarchical shrinkage priors for presenting sparsity assumptions in covariate effects. I then present a projection predictive variable selection approach, which is a Bayesian decision theoretical approach for variable selection which can preserve the essential information and uncertainties related to all variables in the study. I also present recent excellent experimental results and easy to use software.

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

May 29, Otaniemi: Jaakko Lehtinen, Graphics Meets Vision Meets Machine Learning
June 5, Kumpula: Jarno Vanhatalo
June 12, Otaniemi: Zhirong Yang: Learning Data Representation by Large-Scale Neighbor Embedding
--after June 12, we'll have a summer break and continue on September 4, 2017--

Welcome!

Allan Tucker: "Three Algorithms Inspired by Data from the Life Sciences"

Lecturer : 
Allan Tucker, PhD, Senior Lecturer
Event type: 
Guest lecture
Event time: 
2017-05-22 10:15 to 11:00
Place: 
Exactum D123, Kumpula
Description: 

Dr Allan Tucker (Brunel University, UK) will visit us on Monday, 22nd May, and present the following talk. Dr Tucker will also be available for discussions on the day of the talk.

A simulated time series from a three-state HMM for modelling disease progression. Source: Allan Tucker. 
Three Algorithms Inspired by Data from the Life Sciences
Abstract:  In this talk I will discuss how the analysis of real-world data from health and the environment can shape novel algorithms. Firstly, I will discuss some of our work on modelling clinical data using probabilistic graphical models (dynamic Bayesian networks, and hidden Markov models). In particular I will discuss the collection of longitudinal data and how this creates challenges for diagnosis and the modelling of disease progression that can be overcome with latent variables. I will then discuss how cross-sectional studies offer additional useful information that can be used to model disease diversity within a population but lack valuable temporal information. Finally, I will discuss the importance of inferring models that generalise well to new independent data and how this can sometimes lead to new challenges, where the same variables can represent subtly different phenomena. Some examples in ecology and genomics will be described.
Short bio: Dr Tucker's first degree was in Cognitive Science at Sheffield University, UK, where he became interested in models of brain function and human and animal behaviour. His other interests include learning models of time-series data in order to try and understand the underlying processes, with a focus on biological, clinical and ecological data. He received his Ph.D at Birkbeck College, University of London sponsored by the Engineering and Physical Sciences Research Council; Honeywell Hi-Spec Solutions, UK; and Honeywell HTC, USA. As a Senior Lecturer at Brunel University London he leads the Intelligent Data Analytics Research group. He has worked in conjunction with Leiden University Medical School and University College London on gene regulatory networks. His current projects include modelling high dimensional gene expression data with Rothamsted Research; modelling visual field test data from Moorfield's Eye Hospital, London; text mining flora with the Royal Botanical Gardens,  Kew London; an epidemiological big data analytics project in Kazakhstan funded by the British Council; and exploring the dynamics of fish populations in the Northern Atlantic in conjunction with the Canadian Department of Fisheries and Oceans and DEFRA.
 
 
 
Welcome!
 
Host: Teemu Roos, teemu.roos@cs.helsinki.fi.

 

Pages