PressureVision: Estimating Hand Pressure from a Single RGB Image (2203.10385v3)

Abstract: People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the environment, doing so can alter contact mechanics, interfere with human tactile perception, require costly sensors, and scale poorly to large environments. We explore the possibility of using a conventional RGB camera to infer hand pressure, enabling machine perception of hand pressure from uninstrumented hands and surfaces. The central insight is that the application of pressure by a hand results in informative appearance changes. Hands share biomechanical properties that result in similar observable phenomena, such as soft-tissue deformation, blood distribution, hand pose, and cast shadows. We collected videos of 36 participants with diverse skin tone applying pressure to an instrumented planar surface. We then trained a deep model (PressureVisionNet) to infer a pressure image from a single RGB image. Our model infers pressure for participants outside of the training data and outperforms baselines. We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions. Overall, our results suggest the appearance of a previously unobserved human hand can be used to accurately infer applied pressure. Data, code, and models are available online.

• The paper presents PressureVisionNet, a deep learning model that infers hand pressure from RGB images by analyzing soft-tissue deformation and blood distribution.
• It employs an encoder-decoder architecture with a pre-trained SE-ResNeXt50 and a custom dataset, PressureVisionDB, to achieve accurate pressure mapping.
• Empirical results demonstrate superior performance over traditional sensor-based methods, highlighting applications in human-computer interaction, robotics, and augmented reality.

Overview of "PressureVision: Estimating Hand Pressure from a Single RGB Image"

The paper "PressureVision: Estimating Hand Pressure from a Single RGB Image" explores an innovative approach to inferring hand pressure using a standard RGB camera rather than conventional pressure sensors. Traditional methods rely on physical instrumentation, such as pressure-sensitive gloves or arrays of pressure sensors, which can alter natural contact mechanics and impede tactile perception. These methods also present limitations in terms of cost and scalability across varied environments. In contrast, the method proposed in this paper seeks to leverage the appearance changes in hands, such as soft-tissue deformation and blood distribution, as captured by an RGB camera to estimate pressure, thus eliminating the need for direct physical instrumentation.

Key Contributions and Methodology

1. PressureVisionNet: The authors developed a deep learning model named PressureVisionNet to infer pressure from a single RGB image. This model utilizes an encoder-decoder architecture where the input consists of an RGB image and the output is a pressure map estimating the pressure applied by the hand. The architecture employs SE-ResNeXt50 as the encoder, pre-trained on ImageNet, indicating its robustness in feature extraction.
2. Dataset Collection: The paper involved collecting a unique dataset, PressureVisionDB, where 36 participants with diverse skin tones were recorded applying pressure to a planar surface using their bare hands. The dataset features RGB video data synchronized with high-resolution pressure images captured using a sensorized surface, the Sensel Morph, providing ground truth for model training and validation. This dataset supports the investigation into the capability of inferring contact pressure from visual data alone.
3. Generalization to Unseen Participants: The results indicate that PressureVisionNet achieves significant performance even with participants not included in the training data. This suggests the model's capacity to generalize across diverse human subjects, which is essential for application in real-world scenarios.

Empirical Results

PressureVisionNet outperformed traditional physical sensor-based baselines in estimating hand pressure, achieving high temporal accuracy and volumetric Intersection over Union (IoU). The model demonstrated an ability to detect and quantify pressure even when the hand was visually depicted without auxiliary markers or sensors. Additionally, the performance metrics indicated that the model could differentiate between varying force levels and scenarios such as high pressure, low pressure, and no contact.

Theoretical and Practical Implications

The research presents both theoretical and practical implications in the fields of human-computer interaction, robotics, and augmented reality. On a theoretical level, it challenges the traditional reliance on invasive physical sensors for pressure estimation by proposing a vision-based alternative that capitalizes on observable biomechanical cues. Practically, the approach could facilitate the development of cost-effective, wide-area pressure-sensing applications using existing imaging hardware. Potential applications include augmenting virtual interaction interfaces, enhancing robotic manipulation tasks, and enabling expansive touch-sensitive environments like interactive surfaces or virtual reality spaces.

Future Directions

While PressureVisionNet demonstrates promising results, future work could address several areas. Expanding the method to analyze pressure interactions in more complex, three-dimensional environments and various surface textures would test the model's adaptability. Additionally, optimizing the model for real-time applications and varied lighting conditions would enhance its practical deployment in real-world settings. Further research could also investigate integrating this approach with other sensory modalities to create a comprehensive, multisensory perception system.

In summary, "PressureVision: Estimating Hand Pressure from a Single RGB Image" introduces a novel, non-invasive method for hand pressure estimation, leveraging deep learning techniques to interpret biomechanical cues from standard RGB images. This work paves the way for broader applications in interactive technologies and presents a shift from traditional sensor-based methods towards flexible and scalable machine perception solutions.