Data from: DigiPalp: Quantifying palpation of tissue hardness and surface geometry with a smart sensor-equipped glove
Data files
Jul 30, 2026 version files 404.80 KB
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Fig2C_Shore10OO.csv
8.58 KB
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Fig2C_Shore20A.csv
5.89 KB
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Fig2C_Shore20OO.csv
7.47 KB
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Fig2C_Shore30A.csv
7.10 KB
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Fig2C_Shore30OO.csv
7.16 KB
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Fig2C_Shore50OO.csv
6.25 KB
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Fig2E_Shore30OO_10mm.csv
6.68 KB
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Fig2E_Shore30OO_15mm.csv
5.69 KB
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Fig2E_Shore30OO_2p5mm.csv
7 KB
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Fig2E_Shore30OO_5mm.csv
8.30 KB
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Fig2E_Shore50OO_10mm.csv
5.83 KB
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Fig2E_Shore50OO_15mm.csv
7.54 KB
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Fig2E_Shore50OO_2p5mm.csv
5.32 KB
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Fig2E_Shore50OO_5mm.csv
5.55 KB
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Fig2H.csv
92.19 KB
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Fig2J.csv
107.24 KB
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Fig3C_150points.csv
12.73 KB
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Fig3C_750points.csv
63.68 KB
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Fig4D_10mmTumorModel.csv
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Fig4D_15mmTumorModel.csv
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Fig4D_5mmTumorModel.csv
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Fig4D_NoTumorModel.csv
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README.md
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Abstract
Manual palpation is a cornerstone of medical assessment, yet its subjective nature limits its ability to provide quantitative data on characteristics like tissue hardness. Here, we introduce DigiPalp, a wearable smart glove designed to enable real-time 4D tactile scanning, combining 3D surface mapping with a tissue hardness measurement at each point. This quick (typically <0.5 s) and non-invasive measurement is achieved through a fusion of custom piezoresistive pressure sensors and magnetic position sensors embedded into the glove’s fingertips. By featuring silicone-encapsulated stretchable wiring, the hand’s natural range of motion is maintained to support a workflow like conventional palpation. We show that the system can reliably differentiate six hardness levels across the soft tissue range, identify small, harder nodules (down to 5 mm radius) embedded in silicone phantoms, mimicking tumor detection, and demonstrate the system’s capability on complex tissue through a full 4D scan of the torso of a living person.
Description of the data and file structure
The individual data files are named according to the following naming convention: 'Fig[ASSOCIATED SUBFIGURE]_[DETAILS].csv'.
'[ASSOCIATED SUBFIGURE]` references the subfigure of the manuscript where the data is plotted. For example, data from Fig. 2, panel C is referenced by 'Fig2C'.
'[DETAILS]` refers to a designator describing the measurement object on which the hardness measurements were conducted or details about the measurement. For example, Fig. 2, panel C, contains data from six objects with different Shore hardnesses, so there are six data files related to this subfigure and [DETAILS] describes the Shore hardness of the object.
All files contain data about hardness measurements conducted with the smart glove. Each row contains the data from one individual hardness measurement.
In the experiments where a full surface scan was conducted (Fig. 2HJ and Fig. 3CD), the 3D surface point and the associated Δs/Δp slope is listed. In all other experiments, all pressure-distance data pairs of the individual hardness measurement and the resulting Δs/Δp slope are listed.
Units are not listed in the file headers. In all files, the following units are used:
- kPa for pressure values
- mm for position and distance values.
- mm/kPa for the listed Δs/Δp slopes. Please note that in all figures in the associated paper, the reciprocal of these values is used: Δp/Δs slopes with the unit kPa/mm.
List of all data files:
- Fig2C_Shore10OO.csv
- Fig2C_Shore20OO.csv
- Fig2C_Shore30OO.csv
- Fig2C_Shore50OO.csv
- Fig2C_Shore20A.csv
- Fig2C_Shore30A.csv
- Fig2E_Shore30OO_2p5mm.csv
- Fig2E_Shore30OO_5mm.csv
- Fig2E_Shore30OO_10mm.csv
- Fig2E_Shore30OO_15mm.csv
- Fig2E_Shore50OO_2p5mm.csv
- Fig2E_Shore50OO_5mm.csv
- Fig2E_Shore50OO_10mm.csv
- Fig2E_Shore50OO_15mm.csv
- Fig2H.csv
- Fig2J.csv
- Fig3C_150points.csv
- Fig3C_750points.csv
- Fig4D_5mmTumorModel.csv
- Fig4D_10mmTumorModel.csv
- Fig4D_15mmTumorModel.csv
- Fig4D_NoTumorModel.csv
Code/software
Data processing and figure creation was done using Python.
Human subjects data
Informed consent to conduct the torso hardness scan and publish the data was obtained from the participant
