Arévalo-Arboleda, Stephanie; Miller, Stanislaw; Janka, Martha; Gerken, Jens What's behind a choice? Understanding Modality Choices under Changing Environmental Conditions InproceedingsMIA ICMI '19 2019 International Conference on Multimodal Interaction, S. 291-301, 2019, ISBN: 978-1-4503-6860-5. Abstract | BibTeX | Links:   @inproceedings{Arévalo-Arboleda2019,
title = {What's behind a choice? Understanding Modality Choices under Changing Environmental Conditions},
author = {Stephanie Arévalo-Arboleda and Stanislaw Miller and Martha Janka and Jens Gerken},
url = {https://hci.w-hs.de/pub_whatsbehindachoiceunderstandingmodalitychoicesunderchangingenvironmentalconditions/, PDF Download},
doi = {10.1145/3340555.3353717},
isbn = { 978-1-4503-6860-5},
year = {2019},
date = {2019-10-14},
booktitle = {ICMI '19 2019 International Conference on Multimodal Interaction},
pages = {291-301},
abstract = {Interacting with the physical and digital environment multimodally enhances user flexibility and adaptability to different scenarios. A body of research has focused on comparing the efficiency and effectiveness of different interaction modalities in digital environments. However, little is known about user behavior in an environment that provides freedom to choose from a range of modalities. That is why, we take a closer look at the factors that influence input modality choices. Building on the work by Jameson & Kristensson, our goal is to understand how different factors influence user choices. In this paper, we present a study that aims to explore modality choices in a hands-free interaction environment, wherein participants can choose and combine freely three hands-free modalities (Gaze, Head movements, Speech) to execute point and select actions in a 2D interface. On the one hand, our results show that users avoid switching modalities more often than we expected, particularly, under conditions that should prompt modality switching. On the other hand, when users make a modality switch, user characteristics and consequences of the experienced interaction have a higher impact on the choice, than the changes in environmental conditions. Further, when users switch between modalities, we identified different types of switching behaviors. Users who deliberately try to find and choose an optimal modality (single switcher), users who try to find optimal combinations of modalities (multiple switcher), and a switching behavior triggered by error occurrence (error biased switcher). We believe that these results help to further understand when and how to design for multimodal interaction in real-world systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Interacting with the physical and digital environment multimodally enhances user flexibility and adaptability to different scenarios. A body of research has focused on comparing the efficiency and effectiveness of different interaction modalities in digital environments. However, little is known about user behavior in an environment that provides freedom to choose from a range of modalities. That is why, we take a closer look at the factors that influence input modality choices. Building on the work by Jameson & Kristensson, our goal is to understand how different factors influence user choices. In this paper, we present a study that aims to explore modality choices in a hands-free interaction environment, wherein participants can choose and combine freely three hands-free modalities (Gaze, Head movements, Speech) to execute point and select actions in a 2D interface. On the one hand, our results show that users avoid switching modalities more often than we expected, particularly, under conditions that should prompt modality switching. On the other hand, when users make a modality switch, user characteristics and consequences of the experienced interaction have a higher impact on the choice, than the changes in environmental conditions. Further, when users switch between modalities, we identified different types of switching behaviors. Users who deliberately try to find and choose an optimal modality (single switcher), users who try to find optimal combinations of modalities (multiple switcher), and a switching behavior triggered by error occurrence (error biased switcher). We believe that these results help to further understand when and how to design for multimodal interaction in real-world systems. |
Wöhle, Lukas; Miller, Stanislaw; Gerken, Jens; Gebhard, Marion A Robust Interface for Head Motion based Control of a Robot Arm using MARG and Visual Sensors InproceedingsMIA 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy, 2018. Abstract | BibTeX | Links: @inproceedings{Wöhle2018,
title = {A Robust Interface for Head Motion based Control of a Robot Arm using MARG and Visual Sensors},
author = {Lukas Wöhle and Stanislaw Miller and Jens Gerken and Marion Gebhard},
url = {https://hci.w-hs.de/pub_a_robust_interface_for_head_motion_based_control/, PDF Download},
doi = {10.1109/MeMeA.2018.8438699},
year = {2018},
date = {2018-06-13},
booktitle = {2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)},
address = {Rome, Italy},
abstract = {Head-controlled human machine interfaces have gained popularity over the past years, especially in the restoration of the autonomy of severely disabled people, like tetraplegics. These interfaces need to be reliable and robust regarding the environmental conditions to guarantee safety of the user and enable a direct interaction between a human and a machine. This paper presents a hybrid MARG and visual sensor system for head orientation estimation which is in this case used to teleoperate a robotic arm. The system contains a Magnetic Angular Rate Gravity (MARG)-sensor and a Tobii eye tracker 4C. A MARG sensor consists of tri-axis accelerometer, gyroscope as well as a magnetometer which enable a complete measurement of orientation relative to the direction of gravity and magnetic field of the earth. The tri-axis magnetometer is sensitive to external magnetic fields which result in incorrect orientation estimation from the sensor fusion process. In this work the Tobii eye tracker 4C is used to increase head orientation estimation because it also features head tracking even though it is commonly used for eye tracking. This type of visual sensor does not suffer magnetic drift. However, it computes orientation data only, if a user is detectable. Within this work a state machine is presented which enables data fusion of the MARG and visual sensor to improve orientation estimation. The fusion of the orientation data of MARG and visual sensors enables a robust interface, which is immune against external magnetic fields. Therefore, it increases the safety of the human machine interaction.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Head-controlled human machine interfaces have gained popularity over the past years, especially in the restoration of the autonomy of severely disabled people, like tetraplegics. These interfaces need to be reliable and robust regarding the environmental conditions to guarantee safety of the user and enable a direct interaction between a human and a machine. This paper presents a hybrid MARG and visual sensor system for head orientation estimation which is in this case used to teleoperate a robotic arm. The system contains a Magnetic Angular Rate Gravity (MARG)-sensor and a Tobii eye tracker 4C. A MARG sensor consists of tri-axis accelerometer, gyroscope as well as a magnetometer which enable a complete measurement of orientation relative to the direction of gravity and magnetic field of the earth. The tri-axis magnetometer is sensitive to external magnetic fields which result in incorrect orientation estimation from the sensor fusion process. In this work the Tobii eye tracker 4C is used to increase head orientation estimation because it also features head tracking even though it is commonly used for eye tracking. This type of visual sensor does not suffer magnetic drift. However, it computes orientation data only, if a user is detectable. Within this work a state machine is presented which enables data fusion of the MARG and visual sensor to improve orientation estimation. The fusion of the orientation data of MARG and visual sensors enables a robust interface, which is immune against external magnetic fields. Therefore, it increases the safety of the human machine interaction. |
