Computerized machine knitting offers an attractive fabrication technology for incorporating wearable assistive devices into garments. In this work, we utilized, for the first time, whole-garment knitting techniques to manufacture a seamless fully knitted pneumatic bending actuator, which represents an advancement to existing cut-and-sew manufacturing techniques. Various machine knitting parameters were investigated to create anisotropic actuator structures, which exhibited a range of bending and extension motions when pressurized with air. The functionality of the actuator was demonstrated through integration into an assistive glove for hand grip action. The achieved curvature range when pressurizing the actuators up to 150 kPa was sufficient to grasp objects down to 3 cm in diameter and up to 125 g in weight. This manufacturing technique is rapid and scalable, paving the way for mass-production of customizable soft robotics wearables.

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Human gait phase detection has become an emerging field of study due to its impact in various clinical studies. In this study, a system is developed to detect the toe-off, midswing, heel-strike, and heel-off phases of a gait cycle in real-time by using a textile-based capacitive strain sensor mounted on the kneepad. Five healthy subjects performed walks including those four phases of the gait at a constant speed and gait distance in a laboratory environment while wearing the kneepad. The phases are labeled according to the gyroscope data of the Inertial Measurement Unit (IMU) located on the kneepad. An Long Short-Term Memory (LSTM) based network is utilized to detect the phases using the capacitance data obtained from the strain sensor. Recognition of four phases with 87% accuracy is accomplished.

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The use of textiles in soft robotics is gaining popularity because of the advantages textiles offer over other materials in terms of weight, conformability, and ease of manufacture. The purpose of this research is to examine the stitching process used to construct fabric-based pneumatic bending actuators as well as the effect of segment types on the actuators’ properties when used in soft robotic glove applications. To impart bending motion to actuators, two techniques have been used: asymmetry between weave and weft knit fabric layers and mechanical anisotropy between these two textiles. The impacts of various segment types on the actuators’ grip force and bending angle were investigated further. According to experiments, segmenting the actuator with a sewing technique increases the bending angle. It was discovered that actuators with high anisotropy differences in their fabric combinations have high gripping forces. Textile-based capacitive strain sensors are also added to selected segmented actuator types, which possess desirable properties such as increased grip force, increased bending angle, and reduced radial expansion. The sensors were used to demonstrate the controllability of a soft robotic glove using a closed-loop system. Finally, we demonstrated that actuators integrated into a soft wearable glove are capable of grasping a variety of items and performing various grasp types.