Exploration of foot-based interaction for menu control and virtual reality applications
Bibliographic record
Abstract
Although hand-based interaction is dominant in the field of human-computer interaction, the hands can be occupied during various daily activities, such as driving, and may be unavailable for those with motor impairments.To support or replace some functions of a hand-based interface, this thesis explores the alternative of foot-based interaction.As a preliminary matter relevant to the design of foot-based interfaces, we investigated elementary characteristics including the comfortable range of motion, increments to control, and interaction metaphors.Next, we carried out a systematic comparison of performance and user experience of two interaction techniques, isometric and isotonic control, in the context of foot-based menu selection and parameter control for a musical performance user interface.We then investigated the effect of auditory or vibrotactile feedback as an alternative to visual feedback in particular for tasks where the user's visual attention is heavily occupied, such as in medical scenarios.Our experiments demonstrated that parameter manipulation by foot interaction is viable even in a non-visual feedback situation.Furthermore, to explore foot-based multimodal immersive experiences, we presented approaches to simulating the surface of a frozen pond, including ice cracking under increased foot pressure.We believe that the lessons learned through this research can provide helpful insights to future designers of interfaces employing interaction by foot. PrefaceChapter 3 and 4 are based predominantly on the publication [1] (Kim, Taeyong, Hao Ju, and Jeremy R. Cooperstock."Pressure or Movement?Usability of Multi-Functional Foot-Based Interfaces".Proceedings of the 2018 Designing Interactive Systems Conference.ACM).Taeyong Kim designed the full system and implemented algorithms to detect position and pressure of foot.He also ran the experiments, analyzed the experimental data and wrote the manuscript.Ju Hao contributed to the implementation of a foot-based interface, such as embedding sensors, and managed the experiments and wrote the manuscript.Jeremy R.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".