This paper presents TouchpadAnyWear, a novel family of textile-integrated force sensors capable of multi-modal touch input, encompassing micro-gesture detection, two-dimensional (2D) continuous input, and force-sensitive strokes. This thin (<1.5 mm) and conformal device features high spatial resolution sensing and motion artifact tolerance through its unique capacitive sensor architecture. The sensor consists of a knitted textile compressive core, sandwiched by stretchable silver electrodes, and conductive textile shielding layers on both sides. With a high-density sensor pixel array (25/cm2), TouchpadAnyWear can detect touch input locations and sizes with millimeter-scale spatial resolution and a wide range of force inputs (0.05 N to 20 N). The incorporation of miniature polymer domes, referred to as “poly-islands”, onto the knitted textile locally stiffens the sensing areas, thereby reducing motion artifacts during deformation. These poly-islands also provide passive tactile feedback to users, allowing for eyes-free localization of the active sensing pixels. Design choices and sensor performance are evaluated using in-depth mechanical characterization. Demonstrations include an 8-by-8 grid sensor as a miniature high-resolution touchpad and a T-shaped sensor for thumb-to-finger micro-gesture input. User evaluations validate the effectiveness and usability of TouchpadAnyWear in daily interaction contexts, such as tapping, forceful pressing, swiping, 2D cursor control, and 2D stroke-based gestures. This paper further discusses potential applications and explorations for TouchpadAnyWear in wearable smart devices, gaming, and augmented reality devices.

Incorporating compliance into shape-changing displays can improve their wearability and actuation modalities. While recent advances in soft actuators highlight promising paths for soft shape-changing displays, these displays currently face some practical challenges of device failure and limited actuator displacement. A monolithic fabrication processes means the device is challenging to repair, for a single point of failure often renders the whole device ineffective. We have leveraged a modular hyperelastic bubble array layer to create a soft shape-changing skin. The modularity of this device allows for rapid repair of individual bubbles and fast prototyping, and the spherical, hyperelastic actuators enable an increase in degrees of freedom due to bubble-to-bubble interactions. Furthermore, we present a forward kinematic description of our device, incorporating these bubble-to-bubble interactions and the nonlinear instabilities unique to hyperelastic actuator inflation. We demonstrate the utility of this soft shape-changing skin as a haptic display that can be worn comfortably or applied to passive interactive objects such as a computer mouse.

SleeveIO is a modular and reconfigurable hardware platform for rapid prototyping of multimodal wearable haptic feedback interactions. SleeveIO features engineered machine-knitted sleeve and band substrates, and five categories of haptic feedback actuator modules including vibrotactors, bellows, muscles, suction/puffing cups, and quad-chamber actuators. A universal magnetic attachment mechanism unifies the different types of actuators, enabling countless multimodal haptic experiences involving combinations of different actuator types in different configurations. SleeveIO is compatible with a variety of hardware/software control platforms, such as FlowIO [42], which enables individual control of each haptic actuator and makes the system battery-powered and untethered. This paper presents the SleeveIO platform in detail along with replication resources, a novel generalized approach to making different types of haptic actuators modular and interoperable, new application possibilities enabled by SleeveIO, and a pilot assessment of the viability of the platform as a whole and each module individually.

Integration of soft haptic devices into garments can improve their usability and wearability for daily computing interactions. In this paper, we introduce PneuSleeve, a fabric-based, compact, and highly expressive forearm sleeve which can render a broad range of haptic stimuli including compression, skin stretch, and vibration. The haptic stimuli are generated by controlling pneumatic pressure inside embroidered stretchable tubes. The actuation configuration includes two compression actuators on the proximal and distal forearm, and four uniformly distributed linear actuators around and tangent to the forearm. Further, to ensure a suitable grip force, two soft mutual capacitance sensors are fabricated and integrated into the compression actuators, and a closed-loop force controller is implemented. We physically characterize the static and dynamic behavior of the actuators, as well as the performance of closed-loop control. We quantitatively evaluate the psychophysical characteristics of the six actuators in a set of user studies. Finally, we show the expressiveness of PneuSleeve by evaluating combined haptic stimuli using subjective assessments.

The notions of safe and compliant interaction are not sufficient to ensure effective physical human-robot cooperation. To obtain an optimal compliant behavior (e.g., variable impedance/admittance control), assessment techniques are required to measure the effectiveness of the interaction in terms of perceived workload by users. This study investigates electroencephalography (EEG) monitoring as an objective measure to classify workload in cooperative manipulation with compliance. An experimental study is conducted including two types of manipulation (gross and fine) with two admittance levels (low- and high-damping). Performance and self-reported measures indicate that a proper admittance level that enhances perceived workload is task-dependent. This information is used to form a binary classification problem (low- and high-workload) with spectral power density and coherence as the features extracted from EEG data. Using a subject-independent feature selection approach, a subject-dependent Linear Discriminant Analysis (LDA) is used for classification. An average classification rate of 81% is achieved that indicates the reliability of the proposed approach for assessing human workload in interaction with varying compliance across the gross and fine manipulation. Furthermore, to validate our proposed objective measure of workload, we have conducted a second experiment composed of both fine and gross motor tasks. Compared to interaction with a constant admittance, a lower EEG-based workload is observed with an open-loop variable admittance controller. This observation is in agreement with the subjective workload score (NASA-TLX).

This paper presents the design of a novel variable stiffness gripper with two parallel fingers (jaws). Compliance of the system is generated by using permanent magnets as the nonlinear springs. Based on the presented design, the position and stiffness level of the fingers can be adjusted simultaneously by changing the air gap between the magnets. The modeling of magnetic repulsion force and stiffness are presented and verified experimentally. An experiment is also conducted to demonstrate the functionality of the gripper to improve safety when a fragile object was grasped and the gripper collided with an obstacle.