reflex-muc-2023

Study data, analysis and supplemental material for short paper "Exploring Dynamic Vibrotactile Feedback for Layer-based Interaction on Elastic Displays" published at Mensch und Computer 2023 - DOI: 10.1145/3603555.3608534

Content

Directory	Description
code	jupyter notebooks for cleaning/converting data, plots, descriptive statistics
data	raw study data
data\trial_data	generated data for the study (feedback mode, target layer, positions of data points in scatter plot)
export	data generated by jupyter notebooks (large files / images are excluded from source control)
export\data	processed study data
export\img	plots
export\spss	data processed for easier analysis in SPSS
mounting	CAD files for 3d printing mounts for vibro-motors
spss	SPSS working files (incomplete)

Study data

• Study data was pregenerated for 50 participants
• Generated data can be found in data/trial_data
• each participant is associated with a data file: data_{ParticipantId}.json

Structure

{
  "probandId": 0,                                 
  "blockData": [
    {
      "index": -4,
      "condition": "Combined Feedback",
      "trials": [
        {
          "trialIndex": 0,
          "layerIndex": 0,
          "resultNumber": 166,
          "resultIdx": 1,
          "points": {
            "points": [
              {
                "pos": { "posX": 781.69552830154, "posY": 263.7441949885747 },
                "layer": 3,
                "center": { "posX": 1548, "posY": 617 },
                "offset": {
                  "posX": 138.09552830153993,
                  "posY": 117.94419498857468
                },
                "category": 2,
                "distance": 524930.662542738
              }

              ... // more points

            ],
            "borderDistance": [
              0, 177754.79355419573, 355509.58710839145, 533264.3806625872,
              711019.1742167829, 888773.9677709786, 1066528.7613251742,
              1244283.5548793701
            ],
            "histogramDistance": [0, 0, 38, 173, 182, 126, 127],
            "histogramLayer": [158, 118, 145, 160, 135, 136, 148]
          },
          "globalTrialIndex": -1
        },

        ... // more trials
      ]
    }
  ]
}

• each block is associated with a specific feedback condition and consists of a number of trials
• blocks with a negative index are training blocks (excluded from analysis)
• each trial consists of a list of points that are used to draw the scatterplot
• each trial has an associated index (within the block), the result number and layer and a global trial index (for the participant over the whole experiment)
• each point has a screen position, a layer, in which it is displayed and a category that defines the color

Study results

For each participant two files exist:

• Pxx_2022-xx-xx.csv: contains raw tracking data for analysis of turning points, and overall tracking quality
• Pxx_2022-xx-xx.json : contains results for each trial, especially:
  • Expected and reported result layers and result numbers
  • Layer configuration used in the task
  • Start of the Trial: StartDate
  • End of Interaction: EndInteractionDate
  • Time when layer result and number were reported: CommitResultDate
• Remarks:
  • Datasets starting with P00 refer to test runs before the final study for testing/finetuning study setup and procedure

Notebooks

• study_convertMissingColumns: used to reconstruct the empty columns for layer numbers in the first trials (one-time fix)
• convert_results:
  • merges separate results for each proband in one large result file (json and data) - do this, when new data is added, as export/data/data_experiment.csv is not in version control (too large)
  • computes additional columns
  • computes cleaned up interaction phase and saves times and markers to associated columns in results / data
• study_general: first raw extraction of results on separate data files
• study_results:
  • load data either from export/data/results_experiment_cleanedDuration.csv (cleaned-up interaction times) or from export/data/results_experiment.csv
  • computes statistics for duration and
  • correctness of results
• study_turning-points:
  • computes turning points
  • compute cleaned up interaction data by removing leading and trailing idle times
• study_frametimes: compute frametimes from data_all.csv

update results

in case of new results added, execute the following steps in the given order:

1. add missing ; at the end of the header in the newly added .csv files
2. run convert_results.ipynb to update the result including the computed values
3. run the follwing nb files in arbitrary order:
   • study_results.ipynb
   • study_frametimes.ipynb
   • study_inLayerDurations.ipynb
   • study_border-durations.ipynb
   • study_turning-points.ipynb

afterwards, the exported csv -files can be re-imported to spss to regenerate the statistics results.