Sitting comfort is an important factor for passengers in selecting cars, airlines, etc. This paper proposes a soft robotic module that can be integrated into the seat cushion to provide better comfort experiences to passengers. Building on rapid manufacturing technologies and a data-driven approach, the module can be controlled to sense the applied force and the displacement of the top surface and actuate according to four designed modes. A total of 2 modules were prototyped and integrated into a seat cushion, and 16 subjects were invited to test the module’s effectiveness. Experiments proved the principle by showing significant differences regarding (dis)comfort. It was concluded that the proposed soft robotics module could provide passengers with better comfort experiences by adjusting the pressure distribution of the seat as well as introducing a variation of postures relevant for prolonged sitting.

Shape-changing textile interfaces have the potential to create unique functions, expressions, and interactions in everyday artifacts. However, the technical expertise required to fabricate and interact with these interfaces limits designers from rapidly iterating through diverse textile expressions. This pictorial presents TEX(alive), a low-cost and open-source physical-digital toolkit to facilitate the creation of temporal expressions in textile interfaces. TEX(alive) comprises pneumatic actuators that can be interactively configured across a 3d printed grid structure on the textile. Creative sessions with seven designers show that TEX(alive) supports the exploration of temporality in textile interfaces, opening up a design space for unforeseen future application scenarios and alive-like expressions in material-driven design. Finally, we suggest coupling TEX(alive) with a computational simulation tool to allow designers to predict spatial shape change when the textile interface increases in size or complexity.

This work presents a soft robotic module that can sense and control contact forces. The module is composed of a foam spring encapsulated by a pneumatic bellow that can be inflated to increase its stiffness. Optical sensors and a light source are integrated inside the soft pneumatic module. Changes in shape of the module lead to a variation in light reflectivity, which is captured by the optical sensors. These shape measurements are combined with air pressure measurements to predict the contact force through a machine learning model. Using these predictions, a closed-loop control of the contact force was implemented. The modules can be applied to realize pressure distribution control in support devices such as seats and mattresses. The presented method is robust and low-cost, can measure both shape and contact force, and does not require (rigid) sensors to be present at the movable contact interface between the support device and the user.

The purpose of this study is to examine the differences in pressure sensitivity for areas of the foot in a toe-off position and with the feet on the ground. This data could provide a base for adapting the softness of different areas while designing footwear. 21 healthy subjects are asked to participate in a test where a researcher applies pressure with an advanced force gauge in 20 locations on the foot until the subject starts experiencing discomfort. Rigid shells of three sizes have been designed and 3D printed based on 3D foot scans. The test is performed in two positions: standing with load on the plantar surface and toe-off loading only the forefoot. The outcome is a pressure discomfort threshold map of the foot. Interestingly, in 16 locations the sensitivity was similar in both conditions (toe-off and complete foot on the ground). Especially, stretched areas showed increased sensitivity.