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.

Soft wearable robotic devices have emerged as a promising solution for human mobility assistance and rehabilitation, yet current solutions suffer from issues such as bulkiness, high cost, nonscalability, noise, and limited portability. This study introduces a novel approach to soft robotic assistive devices using untethered, soft actuators with seamlessly integrated sensing, heating, and actuation properties through digital machine knitting and low‐boiling liquid. The proposed soft actuator operates under a voltage of less than 12.5 V, generating a tip force of up to 50 mN. This actuator achieves a bending motion when filled with 2 mL of low‐boiling liquid and supplied with 15 W. The dynamic response of the actuator is examined under consistent parameters, revealing a 60‐second inflation time and a subsequent natural cooling period of 30 s at room temperature. Notably, over 12 cycles, the tip force of the actuator exhibits minimal variation, highlighting its durability for prolonged usage. The proposed approach paves the way for overcoming the limitations of existing technologies, particularly in terms of motion assistance and rehabilitation applications, with an emphasis on at‐home usage during daily activities.

Remote manipulation devices extend human capabilities over vast distances or in inaccessible environments, removing constraints between patients and treatment. The integration of therapeutic and assistive devices with the Internet of Things (IoT) has demonstrated high potential to develop and enhance intelligent rehabilitation systems in the e‐health domain. Within such devices, soft robotic products distinguish themselves through their lightweight and adaptable characteristics, facilitating secure collaboration between humans and robots. The objective of this research is to combine a textile‐based sensorized glove with an air‐driven soft robotic glove, operated wirelessly using the developed control system architecture. The sensing glove equipped with capacitive sensors on each finger captures the movements of the medical staff's hand. Meanwhile, the pneumatic rehabilitation glove designed to aid patients affected by impaired hand function due to stroke, brain injury, or spinal cord injury replicates the movements of the medical personnel. The proposed artificial intelligence‐based system detects finger gestures and actuates the pneumatic system, responding within an average response time of 48.4 ms. The evaluation of the system further in terms of accuracy and transmission quality metrics verifies the feasibility of the proposed system integrating textile gloves into IoT infrastructure, enabling remote motion sensing and actuation.

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.

Stride length estimation (SLE) is a fundamental component of pedestrian dead reckoning (PDR) in indoor navigation and positioning (INP) applications. The knowledge of stride length is crucial for determining the distances covered by pedestrians and estimating their position in real time. In this study, we proposed a real-time SLE method using innovative textile-based capacitive strain sensors (TCSSs) attached to knee pads. The SLE performance of the capacitive sensors was compared with smartphone inertial measurement units (IMUs), and the results were reported. We applied a supervised SLE approach by creating labeled gait data from participants who wore sensors and walked along controlled paths created with predetermined stride lengths. An adaptive stride detection (ASD) algorithm was developed to handle data diversity resulting from varying participant characteristics. Furthermore, we investigated the contribution of gait phase features (GPFs) to SLE. The proposed model achieved impressive outcomes with a mean absolute error (MAE) of 8.73 cm, showcasing its significance in accurate real-time SLE.

The integration of agriculture and Internet of Things (IoT) technology has transformed traditional farming into data‐driven, automated, and highly interactive systems. Central to this evolution are sensor platforms that monitor soil conditions and environmental parameters such as moisture, temperature, pH, nutrients, and pollutants. Recent progress in flexible electronics and smart functional materials has created new opportunities for developing adaptable sensor systems suited to dynamic agricultural applications. Despite these advancements, the use of geotextiles as soil‐sensing platforms remains largely unexplored. This review traces the shift from conventional rigid soil sensors to flexible and potentially geotextile‐based systems, assessing their roles in IoT‐enabled agricultural applications. While soil sensors are generally discussed under separate headings, the review integrates them under the common themes, including material selection, fabrication methods, testing methodologies, energy management strategies, and IoT connectivity. By identifying common materials, design principles, and manufacturing techniques across various soil sensors, the review proposes a synthesis framework that supports multi‐sensor integration. Extending this integrative approach to geotextile‐based structures envisions the realization of flexible, large‐area, and adaptive soil sensing platforms that could enable next‐generation of sustainable and smart precision agriculture.