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A smart glove with integrated triboelectric nanogenerator

Wearable devices have benefited from flexible electronics with fantastic functions. Among all human-machine interfaces, gesture-sensing has been extensively studied.

Our paper describes a self-powered gesture system that detects hand gestures by analyzing the triboelectric nanogenerator output signal, a smart glove which is attached to the back of the hand. Our sensor detects gestures by measuring the distance tendons are moved.

The device was also tested for humidity resistance and durability. Additionally, we have developed a set of rules defining how gestures correspond to English letters. This sensor could be used to convert gestures into words by converting signs.

To enhance the use and applicability of this system, a smart glove can be integrated into it. We developed a self-powered real-time hand gesture recognition system that recognizes a variety of hand gestures.


Due to their broad spectrum of biomedical diagnostics, personal healthcare, human-machine interfaces, etc., wearable electronics for sensing human motions and gestures have attracted considerable attention over the past few years.

With vast potential for transforming hand gestures into digital controls, sensors based on gestures, among many other classifications, are growing rapidly. As of now, most gestures are recognized by cameras.

In these systems, fingers are monitored by embedded cameras so that low latencies and high spatial resolution can be achieved. Various commercially available methods are also used for capturing hand movements,

including electromyography (EMG) and inertial measurement units (IMUs). Using these strategies, however, in the form of wearable devices can be very expensive and may cause the sensor to become overweight.

For the development of stretchable strain sensors that can be worn without using cameras, a variety of nanomaterials with high conductivity were used. Wearable gesture sensors currently rely on external power sources, which may be inconvenient, affecting their charging time and battery life.

A self-powered sensing system was introduced to deal with this problem. Recently, energy harvesting devices like nanogenerators have been integrated into self-powered systems in order to generate energy.

Many different types of piezoelectric nanogenerators have been studied, including piezoelectric nanogenerators (PENG), which convert vibrational mechanical energy into usable electrical energy.

The limited output performance of these devices remains a challenge. As a result, nanogenerators (TENG) are more promising candidates for flexible wearable electronics than PENG, for example, because they are lighter, smaller, more manageable, and better to manufacture.’

Based on the charge transfer between the electrode and the epidermis, they have successfully created a flexible/wearable gesture sensor. Even with progress, self-powered gesture sensors need still to be improved.

As of today, TENG-based gesture sensors are generally attached to finger joints to monitor random finger movements, reducing dexterity of the fingers and increasing discomfort for users. The large bending angle may also affect the sensors’ durability.

In this paper, a TENG-based gesture sensor has been placed on the back of the hands to correct the drawbacks of the aforementioned examples. In addition, the backs of hands can be seen moving because the flexor tendons of the fingers are attached to the forearm muscles.

At the backside of the hand, we positioned three sensors on the tendons of the thumb, index finger, and little finger to validate our system. We defined eight gestures by moving and sticking out the three fingers in anyone, one, two, or all (three) directions.

A total of 64 combinations of two-handed gestures could be demonstrated to demonstrate 26 alphabets in English for sign language recognition.

A variety of environmental factors, such as humidity, would impact sensor performance since the sensors are attached to the hands. The chitosan-based TENG we have developed has the ability to maintain electric output even when the relative humidity is high, as outlined in our previous study.

As a result, we selected chitosan/glycerol film as a triboelectric layer during fabrication to ensure humidity independence and biocompatibility. The purpose of this work is to demonstrate a self-powered gesture sensor that can recognize finger movements in real-time, in a wearable form factor.

In this research, the authors demonstrate how their technology can be applied to self-powered electronic skin, sustainable wearable devices, and personalized medical devices.

Material and method

For making the chitosan/glycerol film

Chitosan/glycerol film was prepared by adding different concentrations of glycerol to chitosan in an acetic acid solution over a period of time. In addition to adding the solutions, they were vigorously stirred until homogeneous.

By spin coating the chitosan/glycerol solution on a silicon v-groove substrate containing nanostructure patterns, the solutions were then coated onto the silicon substrate.

In a vacuum chamber, the chitosan/glycerol coated silicon substrate was evacuated using a vacuum pump to remove any remaining bubbles. Lastly, the nanostructure silicon substrate containing the chitosan/glycerol thin film was incubated at 60°C with the as-prepared sample for 4 hours to form a 100 nm thin film on the top.

To characterize the surface morphology of the formed chitosan/glycerol films, FESEM (JEOL JSM-7600F) was used.

Chitosan/glycerol film stability test

To fabricate the TENG, small pieces (2 * 3 cm2) of chitosan/glycerol and fluorinated ethylene propylene (FEP) films were cut. To make the TENG operate between 2 Hz and 10 Hz, a linear motor (LinMot H01) system was employed.

An electrometer with programmable functions (Keithley Model 6514) may be used to monitor the voltage output to determine the durability of the device. Furthermore, a programmable electrometer was used in order to measure the voltage output of TENG when it was exposed to the relative humidity of 20% to 80%.

Alphabet recognition and gesture measurement

TENGs of 0.5 cm in radius were fabricated with the purpose of measuring gestures and recognizing letters. Each finger’s thumb, forefinger, and little finger’s tendons were disposed of three sensors.

TENG was created by layering ITO-FEP on top of a chitosan/glycerol coating on the other layer and separating them with double-sided adhesive.

A polyethylene terephthalate (PET) substrate (20 *m thick) coated with indium tin oxide (ITO) with high transparency and flexibility characteristics was attached to the polydimethylsiloxane (PDMS) substrates. Over the ITO sheet was placed a 50 micron-thick FEP film.

We then stuck two triboelectric layers together with donut-shaped double-sided adhesives of thickness 50*m. The ITO and Chitosan/Glycerol films were connected to two copper wires in order to obtain the electrical output.

On the device substrate, a PDMS/curing agent mixture with a weight ratio of 10 (base/curing agent) was dispensed onto ITO films and cured for over 12 hours at 60°C.

Performance in electrical outputs

TENG works on the principle that the surface of the contact material should be rough to maximize its electrical output. An increase in the contact area is achieved by adding nanostructures to the chitosan/glycerol film, encouraging a triboelectric effect.

Using SEM, it is clear that Nano-pyramid-like arrays are present on the surface of the chitosan/glycerol. Additionally, we compared TENG voltage output with and without nanostructure.

Compared with the unmodified TENG, the nanostructure-modified one produces a slightly higher voltage output. The nanostructure-modified TENG was then used for all of the experiments. TENG’s output is also decreased by humidity.

Particularly on wearable sensors, humidity lowers the performance. The design of our solution supports high output properties in various humidity conditions with chitosan-based TENGs.

Previous studies have shown that chitosan/glycerol as-prepared films increase conductivity and relative permittivity with increasing relative humidity, which can counteract the loss of electrical output caused by moisture.

An oven that controls the humidity is used to measure TENG. This diagram illustrates how relative humidity has a varying impact on the output voltage of the device. As can be seen in the illustration, the voltage remains constant no matter how high the relative humidity (20% to 80%).

TENG containing chitosan proved applicable to a wide variety of environmental conditions, according to an experimental result. The wearable sensor application of the as-developed TENGs requires that the electrode surface be flexible. Testing was conducted on an ITO electrode to determine how resistance affects bending angle performance.

With different resistance values, S3, the bending angle of the electrode remains unaffected, demonstrating its flexible and conductive properties. Furthermore, we tested the TENG’s mechanical stability by performing a durability test.

5000 times were measured for each operating frequency of 2, 5, and 10 Hz for the TENG. In theory, there shouldn’t be any significant change in output voltage with frequency change, which proves that the device is robust and mechanically durable.

Establishment of a sign language system

Three gesture sensors measuring 0.5 cm across were attached to the back of the hand in order to demonstrate the ability of the device to detect finger motions.

The position of each of the three sensors means that only the movement of the corresponding finger can be sensed because they are placed right above the thumb, index finger, and little finger tendons, respectively. As well, middle and ring fingers move when measuring other fingers.

Due to this, sensors were not placed over these two fingers’ tendons. A clenched fist state and a fully stretched state were compared in the sensing experiment. It is more likely that the movement of a single finger will interfere with the tendon associated with another finger in

the former case if the hand gesture is fully extended originally. The movement of the finger may affect the movement of other fingers if the initial gesture is in the state of clenched fists.

Our first step was to determine the initial output of a clenched fist so we could elaborate on this difference. It is impossible for any of the three sensors to detect any signal when the first gesture is maintained. We can then detect the induced signal from various fingers independently

when we stretch out our thumb, index finger, and little finger. The fact that a sensor will not detect a signal if one finger is still, despite other fingers moving simultaneously, demonstrates how accurate and sensitive the sensor is.

A further explanation for the differences between the output voltages can be attributed to how the skin has deformed under each finger as well. A further reason for the smaller output signal can be found in the fact that the little finger induces a smaller deformation than the other fingers.

Apart from this, we also measured the output currents of the sensors. When I stretched out my index finger or thumb, the peak generated current was 4 Na but, since the little finger tendon was less displaced, the current generated by it was only around 2 Na

The electrical output of the initially stretched finger state was then evaluated. As already mentioned, there were up to 64 combinations of gestures possible with both hands together. Therefore, we were able to create a new sign language system by using 26 gestures as different English alphabets.

The pattern on the back of the hand was used to create a system of hand-powered sign language. In addition, to demonstrate the practical application of the technology, our smart glove includes gesture sensors, thereby converting a regular glove into a smart glove.

It also indicates that the flexibility of the sensor allows for good conformity on the glove, thereby causing a minimal sensitivity drop. A sample smart glove example is displayed as well, where different gestures are detected to determine NTHU.

When the aforementioned defined gestures are made, the smart glove induces an electrical signal.  For the left index finger and right thumb, only the extended state was maintained, while the bent state remained for other fingers. Thus, only the extended fingers can generate signals. It is also possible to represent other letters in the same way.

There is still a subjective element to this experiment. Hence, the developed self-powered gesture sensor implies a great deal of potential with respect to applications such as self-powered wearable medical devices, military communication systems, and other machines that interact with humans.

Using multi-channel multi-language systems with a smart keyboard, this study demonstrates how to increase their practicability in the future by combining interpretation programs and multi-channel systems.



Our paper presents a demonstration of the utility of a self-powered gesture-sensing system mounted behind the hands. TENG’s chitosan-based structure is designed to achieve uniform triboelectric performance while enhancing biocompatibility, which can in turn result in safe operating conditions.

Our low-cost sign language conversion system was developed by integrating a TENG into a smart glove that could define gestures. A biomechanical energy harvesting system that doesn’t compromise the comfort of fingers can effectively harvest biomechanical energy from finger motions.

Furthermore, the device has demonstrated excellent stability. Additionally, we demonstrate self-powered back-of-hand sensing techniques, thereby countering the existing disadvantages of devices that rely on external power or cameras.

This finger-actuated wearable device provides new methods of recognizing hand gestures and could be an interesting candidate to use as a human-machine interface due to its numerous advantageous features, including being lightweight, self-powered, flexible, comfortable, and scalable.

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