NUST Institutions Library Catalogue

The goal of this research project is to show how to design, build, and test a soft robotic
glove that can be used in real life to help people with hand injuries. The glove uses both
hybrid control mechanisms and pneumatic actuation. The device is meant to help patients
who can't move their hands very well, such those who are recovering from a stroke, by
giving them both passive and active movement support. The main part of the gadget is
five custom-made pneumatic bladder actuators. These actuators are made of cow leather,
elastic sleeves, and big latex balloons. When you fill them up, they move like fingers do
naturally.
The electronic glove has two modes: Auto Mode, which lets the user run programmable
treatment cycles, and EMG Mode, which lets the user control the glove based on realtime surface electromyography (sEMG) data. One of the most important new aspects of
the system is the addition of an industrial-grade pressure sensor that can measure between
4 and 20 milliamperes. The HW-685 current-to-voltage converter connects to this
pressure sensor. This makes sure that the pressure feedback is clear and free of noise.
This method solves a typical challenge when it comes to academic prototypes.
You can utilize a touchscreen interface with visuals made just for the glove to control it.
You can see live EMG charts, get real-time status updates, and make several changes to
the settings with this interface. There are other safety measures in place, such as the
software-based emergency stop logic and controlled depressurization operations.
The full experimental validation was done with people who were in good health. A
calibrated WIKA master gauge was used for the pressure accuracy test, and the findings
showed that the pressure stayed the same up to 600 kPa. The error was less than three
percent, and the overshoot was not very big. We used a Jamar digital dynamometer to do
the testing, and the results showed that the peak grip forces could reach a maximum of
9.3 kilograms. Compared to the established benchmark values for functional grab
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support, our results were either like or better than those values. We used a goniometer to
measure the range of motion, and the results showed that the angles of finger flexion can
reach up to fifty degrees, which is very close to how the hand typically moves.
The results of this study show that it is possible to create a soft robotic system that is both
flexible and cheap, and that may be used in hospitals. Because it has reliable sensing,
adaptive control, and an easy-to-use interface, it would be great for use in clinical or
home rehabilitation settings. A translation like this would be very helpful for both
patients and therapists in many ways.