Continuous measurement of cardio-respiratory signals offers various kinds of information valuable for the diagnosis of disease and management of the disease process. The article reports the development of the Piezofilm yarn sensor for healthcare applications, and investigates its performance by monitoring cardio-respiratory signals of human body over an extended period of time. Piezofilm yarn sensor was developed by embedding the thin PVDF strips within the textile yarn. The working mechanism of the Piezofilm yarn sensor is based on voltage generation due to the applied stress. In order to deploy the Piezofilm yarn sensor in the application environment, it was integrated into the knitted textile fabric and then sewn to form belt to be placed at the chest wall and wrist area. The raw signals were acquired through the Piezofilm lab amplifier, National Instrument data acquisition device and SignalExpress software. Fast Fourier Transform analysis was performed to calculate the average cardio-respiratory signal frequencies. Based on Fast Fourier Transform analysis, an additional signal-processing step was added to eliminate the unwanted mechanical interference and body signals by using an Infinite Impulse Response band pass filter. The Piezofilm yarn sensor embedded sensing fabric was able to measure both respiratory rate and heart beat rate under static and dynamic conditions. The wrist area measurements for heart beat signals were found to be more uniform in comparison to the chest area measurements. Apart from the general healthcare, this sensing fabric could also be used in studies related to biorhythms, sports, detection of sleep apnea and heart problems.

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.

Soft wearable robotic devices offer significant potential for human mobility assistance and rehabilitation; however, existing solutions are often hindered by bulkiness, limited scalability, and restricted portability. This study introduces a textile‐based exoskeleton glove equipped with thermally driven actuators, achieving dexterous motion in under 12 s using only 10.8 W of power while maintaining a low operating temperature of 48 °C. This performance surpasses the fastest previously reported system in terms of power input and operating temperature, which achieved actuation in 10 s but required 15 W and operated at 100 °C. In comparison, recent studies report response times of 120 s, with 14 W consumption and temperatures near 95 °C. The actuators utilize low‐boiling‐point liquids that undergo phase transitions upon heating, enabling fast, untethered actuation without external systems. The seamless knitted structure integrates sensing and actuation functionalities, including self‐return to initial position capability. This is achieved through digital machine knitting of specific patterns using functional yarns. The actuators demonstrate 270° bending, generating 2 N gripping force while maintaining energy consumption efficiency. The glove is mountable on an industrial robotic arm, demonstrating its ability to grasp and relocate objects. This study presents a quick‐response, scalable, energy‐efficient solution for wearable robotics.