h i g h l i g h t s• A flexible and stretchable durable fabric-based tactile sensor capable of capturing typical human interaction forces was developed.• We present elaborate measurement results of the sensor. • A process of creating multiple sensor areas in a single fabric patch was developed.• The measures against performance degradation due to moisture are presented. • Using the developed technology, a tactile dataglove with 54 pressure sensitive regions was built.a r t i c l e i n f o Article history: Available online xxxx Keywords: Tactile sensor Flexible tactile sensor Stretchable tactile sensor Tactile dataglove a b s t r a c tWe introduce a novel, fabric-based, flexible, and stretchable tactile sensor, which is capable of seamlessly covering natural shapes. As humans and robots have curved body parts that move with respect to each other, the practical usage of traditional rigid tactile sensor arrays is limited. Rather, a flexible tactile skin is required. Our design allows for several tactile cells to be embedded in a single sensor patch. It can have an arbitrary perimeter and can cover free-form surfaces. In this article we discuss the construction of the sensor and evaluate its performance. Our flexible tactile sensor remains operational on top of soft padding such as a gel cushion, enabling the construction of a human-like soft tactile skin. The sensor allows pressure measurements to be read from a subtle less than 1 kPa up to high pressures of more than 500 kPa, which easily covers the common range for everyday human manual interactions. Due to a layered construction, the sensor is very robust and can withstand normal forces multiple magnitudes higher than what could be achieved by a human without sustaining damage.As an exciting application for the sensor, we describe the construction of a wearable tactile dataglove with 54 tactile cells and embedded data acquisition electronics. We also discuss the necessary implementation details to maintain long term sensor performance in the presence of moisture.

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The feeling of embodiment, i.e., experiencing the body as belonging to oneself and being able to integrate objects into one's bodily self-representation, is a key aspect of human self-consciousness and has been shown to importantly shape human cognition. An extension of such feelings toward robots has been argued as being crucial for assistive technologies aiming at restoring, extending, or simulating sensorimotor functions. Empirical and theoretical work illustrates the importance of sensory feedback for the feeling of embodiment and also immersion; we focus on the the perceptual level of touch and the role of tactile feedback in various assistive robotic devices. We critically review how different facets of tactile perception in humans, i.e., affective, social, and self-touch, might influence embodiment. This is particularly important as current assistive robotic devices – such as prostheses, orthoses, exoskeletons, and devices for teleoperation–often limit touch low-density and spatially constrained haptic feedback, i.e., the mere touch sensation linked to an action. Here, we analyze, discuss, and propose how and to what degree tactile feedback might increase the embodiment of certain robotic devices, e.g., prostheses, and the feeling of immersion in human-robot interaction, e.g., in teleoperation. Based on recent findings from cognitive psychology on interactive processes between touch and embodiment, we discuss technical solutions for specific applications, which might be used to enhance embodiment, and facilitate the study of how embodiment might alter human-robot interactions. We postulate that high-density and large surface sensing and stimulation are required to foster embodiment of such assistive devices.

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Surface electromyography (sEMG) of the forearm is an active research topic since the 1990s in the rehabilitation robotics / machine learning community, as it can be used to predict the hand posture and overall grip force. We hereby advance the state of the art by describing a multi-subject experiment in which sEMG is successfully used to predict simultaneous forces applied by a human subject at the fingertips, that is, when six voluntary muscle contractions (VMCs) are elicited (flexion of the little, ring, middle and index fingers, thumb rotation and thumb adduction). Using a multi-sensor setup sEMG activity of the forearm of a human subject and the forces exerted at the fingertips are measured; a Support Vector Machine is then used to associate sEMG signals and forces. Our results clearly show that sEMG can be used to predict the required forces with an error as small as 1.5% of the sensor range. Targeted positioning of the electrodes is not required. The prediction is uniformly accurate across all VMCs and all 12 subjects considered, and it is robust against subsampling. This result goes in the direction of enabling natural force / impedance control of a highly dexterous prosthetic hand over a continuous, infinite manifold of force configurations, rather than using posture classification like in the traditional approach.