*Proceeding Paper* **Development of the Smart Jacket Featured with Medical, Sports, and Defense Attributes using Conductive Thread and Thermoelectric Fabric †**

**Aman Ul Azam Khan 1,\*, Aurghya Kumar Saha 1, Zarin Tasnim Bristy 2, Tasnima Tazrin 1, Abdul Baqui 1,‡ and Barshan Dev <sup>1</sup>**


**Abstract:** The exigency of humans is boosting the necessity of Smart Textiles in this modern era. A decade ago, envisioning sophisticated outerwear with several uses were considered a challenge. This study aims to a jacket with 15 features; divided into 7 groups, including defense, sports, health, medical, women, and children safety mechanisms, 4 out of these 15 functions can be controlled by an Android app, "Smart Jacket BUFT". To avoid nonrenewable energy sources, solar power and energy harvesting technology to produce electricity from body heat and foot-powered energy were used, Smart jacket has embedded circuits and sensors alone with AD8232, MAX30100, NEO6m GPS, and ESP32 microcontrollers & voice and app-control. It is hopping that; his initial stage of growth and improvement will pave the way for subsequent activities.

**Keywords:** smart jacket; 15 features; android application; medical; defense; conductive thread; energy harvesting technology

#### **1. Introduction**

The invention of textiles roughly 27,000 years ago could be regarded as the first time humans created a useful material [1]. Smart Textile can detect mechanical, thermal, magnetic, chemical, electrical, or other environmental variables and respond in a programmed manner (stimuli) [2]. Electronics are made from conductive threads and fabrics, whose limits and potential are determined by textile materials and production procedures [3]. In the late 1990s, MIT and the Georgia Institute of Technology conducted a series of studies on E-textiles in academia [4]. In the field of Textile Technology, it was hard to imagine the concept of Smart Jackets with all sorts of features, including health and medicinal functionalities, sports flexibilities, women's safety options, children's safety options, defense options, and a lot more, in just one jacket. The wearable electronic jacket is nowadays playing an important role in the medical world. New monitoring system support resources have been developed because of recent technological advances in mobile devices and wireless communications. A few years ago, the thought of a jacket that could seamlessly communicate and converse with a personal assistant and deliver practical solutions to everyday difficulties was simply a concept. This research paper brought the notion to life by designing a practical smart jacket with a total of fifteen features. To assure the jacket's flexibility and efficiency, we developed our own conductive thread and fabric flexible circuit. The Wearable MotherboardTM envisioned clothing that could monitor important signals discretely [5]. Cooseman et al. described a wireless charging garment with a patient

**Citation:** Khan, A.U.A.; Saha, A.K.; Bristy, Z.T.; Tazrin, T.; Baqui, A.; Dev, B. Development of the Smart Jacket Featured with Medical, Sports, and Defense Attributes using Conductive Thread and Thermoelectric Fabric. *Eng. Proc.* **2023**, *30*, 18. https:// doi.org/10.3390/engproc2023030018

Academic Editors: Steve Beeby, Kai Yang, Russel Torah and Theodore Hughes-Riley

Published: 7 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

monitoring system [6]. The use of carbon nanotubes to convert cotton thread for use in E-textiles was described in a 2008 study [7]. MyHeart, a 2009 EU (FP 6)-funded project, created ECG- and breathing-sensitive smart textiles [8]. For energy textile solution, in 2007, Qin et al. described energy harvesting utilizing Piezoelectric Zinc Oxide nanowires grown around textile fibers [9]. Design Research Lab at the Berlin University of the Arts and Telekom Innovation Laboratories designed the Smart Maintenance Jacket, which uses wearable technology for industrial maintenance work and predicts the future role of networked wearables in these contexts for telecommunications carriers [10]. Muhammad Arsalan et al. developed a next-generation tactical system for monitoring military health data. The suit's internal CPU receives and sends sensor data using LoRaWAN technology. The report analyzes the suit's system design and functionality [11]. Anurag Sharma et al. developed an IoT-based smart jacket in 2022 with ECG, heart rate, body temperature, air temperature, and oximeter sensors [12]. In the same year, Manibabu A and colleagues designed a Smart Thermal Jacket with Wearable Sensors using IoT [13]. In addition, Paolo Visconti et al. demonstrated a smart garment, in 2022, that monitors environmental factors and vital signs to monitor workers in hazardous industries [14]. The University of Engineering and Technology, Pakistan designed a wearable solar energy harvesting jacket for vital health monitoring systems in the current year [15]. Dilber Uzun Ozsahin et al. used multiple devices in one smart jacket [16].

#### **2. Objectives**


#### **3. Materials & Methods**

#### *3.1. Materials*

The jacket was made with 100% polyester fabric of 300 GSM.

Trims: Plastic Zipper, Metal Zipper; Sewing Thread: 20/2, 40/3 and Fabric Glue was also used. Conductive thread and fabric flexible circuits maintain conductivity and add flexibility. To make it eco-friendly and cost-effective, waste materials were utilized. Besides, developed thermoelectric fabric was used which body heat into electrical energy. Table 1 shows the waste materials used in Smart Jacket.


**Table 1.** Waste Material Used in Smart Jacket.

#### *3.2. Methodology*

Figure 1 shows Jacket's methodology according to units. The smart jacket's heart and brain work simultaneously. It collects data from NEO6M GPS module, MAX30100 Oxi-Pulse sensor, and AD8232 sensor circuit. An Android app was developed for Smart Jacket's brain and heart. Before starting the app, ESP32 was connected to a mobile hotspot. The NEO6M GPS module sent a signal to satellites. GPS started when satellites sent data to NEO6M GPS module. Firebase displays the user's location, heart rate, oxygen level, and

ECG via a mobile app after receiving ESP32 data. (Figure 2b) Despite using two 1000 mAh 3.7 V DC batteries for a total voltage of 7.2 V and an LM7805 MOSFET as a 5.0 V voltage regulator, they were all turned on by 5.0 V. This 7.2-volt battery was charged by a 5.0-volt, two-piece, 1-watt solar panel on the back of the jacket.

**Figure 1.** Flowchart of Jacket's methodology according to units.

**Figure 2.** (**a**) Final Smart Jacket (**b**) Dual Display of automatic heat-warm system; (**c**) Jacket Defense Unit (**d**) The Android application's data of the smart jacket.

The body unit was equipped with an automated heating system, automatic light on/off, a smart screen on the cuff, and a device charging system. Automatic heating used a W1219 temperature controller circuit and an NTC sensor module. It was a heating/cooling controller. First, a W1219 circuit was connected to a 14.8-volt power bank charged by a shoe's piezo element generated electricity. Three heating pads were on W1219. As the user walked, mechanical energy was transferred to piezo components and converted to electrical energy. The potential difference's energy was then converted with a DC-to-DC converter and sent to a 14.8-volt power source. Nichrome wire and cotton made a heating pad formed a solenoid. The heating pad warmed. W1219 has two screens. The two displays showed the current and set temperatures. The user could set the temperature to their personal preferences. Moreover, the automated light on/off system constructed by a flexible fabric circuit with a resistor, transistor, and LDR. The battery was 3.7 V, 800 mAh DC which was charged by thermoelectric fabric. Thermoelectric fabric was formed from electronic and textile waste. When light-dependent resistors detect more light, their resistance rises, deactivating the circuit. When the LDR sensed no light, its resistance dropped, triggering the circuit. This turned on the jacket's hoodie LEDs.

Electronic device charging system contained by a 3.7 volt, 3600 mAh Lithium-ion battery with a voltage boost module. A 5-volt, 1-watt solar panel charged this battery. A USB voltage booster was attached to the battery in the welt pocket. This study featured a smart screen on the cuff of the smart jacket which was created with a fabric flexible circuit using Arduino pro mini, HC-05 Bluetooth module, 0.96" OLED display, 3.7-volt 2800 mAh battery (which can be charged by1 watt, 5.0-volt solar panel), and polyester fabric and conductive thread. Figure 2c exhibits the methods of the smart screen the on cuff.

The jacket's voice unit consisted of an old speaker PCB circuit, 3.7 volts and 1000 mAh a lithium-ion battery (which can be charged by 1 watt, 5.0-volt solar panel), a speaker (3 w) and a microphone. A Bluetooth circuit and an mp3 circuit were combined to from this circuit whose connection was formed with the smartphone upon activation. Personal assistants such as Google, Siri, and Alexa can be linked into smartphones.

In the jacket's right-glove there is a shocking element. For that, a high-voltage transformer whose input was 3.7 to 6 volts and output were 400,000 DC volt, a metal shank button, a 3.7-volt DC lithium-ion battery, and a 5.0-volt 1-watt mini solar panel. A high voltage transformer was connected to shank button and placed on the glove's two-finger. The glove had to be waterproof and for extra safety we used synthetic rubber on the shank button area. Upon pressing the switch shank button, 400,000 DC volts were produced, allowing them to defend themselves easily. The left glove has a toxic gas spreader system with an ultrasonic MIST module, a switch, and a 1000 mAh, 3.7-volt lithium polymer battery, which was charged by a 5-volt 1-watt solar panel. For MIST, we made a liquid reserve tank from plastic waste. MIST module had a piezo atomizer disc. When activated by battery, the MIST module main circuit of IC sent data to the disc, which turned liquid into gas. As a self-defense glove, chloroform (CHCl3) was used. So, users can easily spread toxic gas (CHCl3) for defense or protection purposes.

#### **4. Results**

All this jacket's functionalities were executed excellently. Figure 2a–c show the practical visualization of the jacket. An Android mobile application was developed and given the name "SMART JACKET BUFT" for these four functions (GPS with emergency switch, Oximeter, pulse meter, ECG). Figure 2d shows the Android application of the smart jacket. These features can be accessed by multiple users who are using the same mobile applications as the wearer. the power unit uses solar system (which covers most functions), piezoelectric shoe, and thermoelectric fabric (which is still under testing). However, during this research we were unable to find any kind of flexible battery, so we used tiny-sized lithium polymer and lithium-ion batteries.

#### *Formatting of Mathematical Components*

For the solar system,

Estimate voltage and current from the mini solar panel [17].

$$\text{Charging time} = \frac{\text{battery capacity (watt hours)}}{\text{solar power (in watt)}} \times 2 \tag{1}$$

On the back part of this jacket are five small solar panels that serve as the power output's charging mechanism. Each solar panel has dimensions of 90 mm by 90 mm (l × w) and voltage of 5.00 volts. Table 2 estimated charging time according to unit.


**Table 2.** Estimated charging time according to unit.

#### **5. Features**

The jacket includes 15 features.


#### **6. Discussion & Conclusions**

*6.1. Discussion*

This study sought to improve the adaptability of textiles and electronic components. Conductive thread was utilized to connect both flexible fabric circuits and fabric-printed circuit boards. To manufacture the most popular conductive thread, however, carbon nanotubes, graphene, and silver were utilized, which was relatively expensive [18]. For this study, a conductive thread was developed with a meager resistance (0.2 Ω/cm to 0.0164 Ω/cm). Therefore, many circuits in one jacket will not be a problem. The jacket research encompassed all human requirements. This garment can be employed to construct seven jackets in the future. Medical, Military, Women's, Children's, Tech-Tex, Cyberia Survival, and Sports Jackets. E-textile research also requires Flexible Nanochips and Nano electric particles which were not available in BD. In addition, the required chemical composition for this experiment was not available in BD. Moreover, the financial crisis was the most crucial truth about this research. Despite showing that entire jackets can be developed with Fabric PCB shown in Figure 3 and Fabric Flexible Circuits. A piezo-shoe

jacket attachment was also created for charging reasons. This feature aimed to construct wireless electrical charging technology; however, it failed due to lack of resources, time, and knowledge. This investigation was concluded despite a few flaws. Any researcher working on e-textiles or intelligent textiles might benefit from this study.

**Figure 3.** Fabric PCB.

#### *6.2. Future Outlook*

Regarding the characteristics, this investigation discovered further revolutionary concepts. Some of the jacket's attributes may not have existed because of economic slump. Among other innovations, there was power generated from human hair, a small, flexible tab on apparel, a 20× magnifying camera, a poisonous gas detector, an air oxygen level meter, and a button projector. In addition, there were several medical features, such as an automatic blood pressure measuring system, a body temperature meter, and galvanic skin resistance, and others. In addition to defensive functions, such as a web shooter, laser burning system on the glove, CO2 spreading system, heads-up translating display, 100× magnification camera glasses, and several others, the smart jacket would be outfitted with an assortment of extra functionality. This investigation also offers some original ideas for the defense industry. As additions to robotics, two micro robots resembling Mr. Doctor and Mr. Commander were proposed. These intelligent micro robots will be concealed within the flap pockets. Importantly, jacket advancements include anti-bacterial fabric, ECG electrode fabric, fire-prevention fabric, and bulletproof fabric. Energy-harvesting textiles, such as solar fabric, would boost the value of clothing. The next generation of smart jackets would be powered by artificial intelligence. In the medical and military industries, this jacket improvement would be incredibly useful. Micro- and nanotechnology would enhance the jacket's performance, reduce its weight, and make it more comfortable.

**Author Contributions:** Conceptualization, A.U.A.K.; A.K.S.; A.B.; Z.T.B.; methodology, A.U.A.K.; A.B.; software, A.K.S.; validation, A.B.; Z.T.B.; T.T.; formal analysis, A.B.; A.U.A.K.; investigation, A.B.; B.D.; resources, A.K.S.; A.U.A.K.; data curation, A.U.A.K.; Z.T.B.; A.K.S.; writing—original draft preparation Z.T.B.; A.U.A.K.; writing—review and editing, A.B.; Z.T.B.; B.D.; visualization, A.U.A.K.; Z.T.B.; supervision, A.B.; project administration, A.B.; B.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors would like to acknowledge financial support from the BGMEA University of Fashion and Technology, Dhaka 1230, Bangladesh.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available within the article and there is presented in every graph. There is no more data apart from the presented.

**Acknowledgments:** Erin Jahan Meem, Department of Textile Engineering, BGMEA University of Fashion & Technology. Taslima Ahmed Tamanna, Department of Textile Engineering, BGMEA University of Fashion & Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

## *Proceeding Paper* **The Machine-Learning-Empowered Gesture Recognition Glove †**

**Jun Luo 1, Yuze Qian 1, Zhenyu Gao 2, Lei Zhang 2,\*, Qinliang Zhuang 1,\* and Kun Zhang 1,\***


**Abstract:** Recently, gesture recognition technology has attracted increasing attention because it provides another means of information exchange in some special occasions, especially for auditory impaired individuals. At present, the fusion of sensor signals and artificial intelligence algorithms is the mainstream trend of gesture recognition technology. Therefore, this article designs a machinelearning-empowered gesture recognition glove. We fabricate a flexible strain sensor with a sandwich structure, which has high sensitivity and good cycle stability. After the sensors are configured in the knitted gloves, the smart gloves can respond to different gestures. Additionally, according to the representation characteristics and recognition targets of sampled signal data, we explore a segmented processing method of dynamic gesture recognition based on Logit Adaboost algorithm. After classification training, the recognition accuracy of smart gloves can reach 97%.

**Keywords:** gesture recognition; flexible sensor; machine learning; Logit Adaboost algorithm

#### **1. Introduction**

As a method of nonverbal communication [1], standardized gestures play an important role in the transmission of message on specific occasions, such as communication between auditory-impaired individuals [2] and information interaction in AR/VR environments [3] or military fields [4]. However, without long-term systematic learning, it is difficult for most people to understand the meaning of gestures [5]. At present, gesture recognition technology offers a feasible solution to this communication barrier, which can effectively judge the specific movements of the hand.

Machine-vision-based image taking and processing has received extensive attention as a means of gesture recognition due to simple equipment requirements [6]. However, it is troubled by a series of problems in the process of application, including complex operation mechanism and variable lighting condition [7]. Accelerometers and gyroscopes are widely used in commercial gesture recognition devices, which can track the direction of the hand movement in three-dimensional space [8]. However, rigid materials and excessive volume limit the application scenarios of these devices, which bring inconvenience to users. Benefiting from the rapid advancement of flexible electronics and artificial intelligence, wearable flexible data gloves based on flexible sensors have been developed. Common flexible sensors for gesture recognition include piezoresistive sensors [9], capacitive sensors [10], piezoelectric sensors [11], triboelectric sensors [12] and EMG sensor arrays [13]. Through the electromechanical performance of flexible sensors, the deformation degree of the hand can be reflected correspondingly.

Furthermore, the classification of sensor signals and the extraction of gesture features are the key factors to determine the accuracy of gesture recognition. It generally requires intelligent algorithms to train sensor signals and generate appropriate classifiers [14]. However, due to the differences in gesture behavior patterns of individuals, the generalization

**Citation:** Luo, J.; Qian, Y.; Gao, Z.; Zhang, L.; Zhuang, Q.; Zhang, K. The Machine-Learning-Empowered Gesture Recognition Glove. *Eng. Proc.* **2023**, *30*, 19. https://doi.org/ 10.3390/engproc2023030019

Academic Editors: Steve Beeby, Kai Yang, Russel Torah and Theodore Hughes-Riley

Published: 21 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

ability of the trained classifiers is low if the sample capacity is limited [15]. A common approach is to increase the number of people participating in algorithm training, which is time- and energy-consuming [16]. Therefore, there is a new idea to remove the irrelevant factors of sensor signals as much as possible through special designs.

In this study, we report on the machine-learning-empowered gesture recognition glove. Flexible piezoresistive strain sensors with high sensitivity and cycle stability were configured in knitted gloves. Then, in order to enhance the generalization ability of gesture recognition model, a multi segment classification model of dynamic gesture recognition based on Logit AdaBoost algorithm was explored. Static signals collected at intervals of gesture changes are usually considered invalid signals because of unobvious characteristics and uncertain duration. So, the model distinguished static signals from effective dynamic signals due to finger movements. Additionally, subsequent training and classification was only related to dynamic signals, avoiding misjudgment caused by static signals. With sufficient training for the defined gestures, the recognition accuracy reached 97%, showing great classification performance.

#### **2. Materials and Methods**

#### *2.1. Materials*

Reduced graphene oxide (r-GO) was supplied by Qingdao University. Polydimethylsiloxane (PDMS) was purchased from Microflu Co., Ltd. (Changzhou, China). *N*,*N*-Dimethylformamide (DMF, 99.5%) was bought from Aladdin Co., Ltd. (Shanghai, China).

#### *2.2. Fabrication of the Flexible Strain Sensor*

The r-Go dispersion (6 wt%) was prepared by two steps. The reduced graphene oxide powders were firstly dispersed in *N*,*N*-Dimethylformamide by mechanical shearing for 1 h at 3500 r/min, and, secondly, it was necessary to remove air bubbles from the solution using ultrasonic for 5 min. Then, the solution was poured onto the PDMS substrate (the volume ratio of PDMS matrix to curing agent was 10:1 and the curing condition was 70 ◦C for 1 h) and heated at 50 ◦C for 3 h using a hot plate. After the solvent evaporates completely, there was a composite film containing the r-Go layer and PDMS substrate. Finally, two copper wires were adhered to both sides of the r-Go layer with silver paste, which used as the electrodes of the strain sensor. Additionally, PDMS was poured and cured on the top of the composite film, resulting in a sandwich structure. The size of the final sample is about 35 mm × 4 mm × 1 mm.

#### *2.3. Characterization*

A scanning electron microscope (SEM, S-4800, Hitachi, Japan) was used to characterize the surface morphology of the R-Go film. Additionally, in order to test the sensing performance of the strain sensors, the flexible strain sensors were fixed between the metallic clamps of a stretching machine (ZQ-CI701G, 1000 N, ZHIQU, Dongguan, China), and a digital source-meter (Keithley 2400, USA) was used to measure the resistance of the sensors continuously in the process of stretching. The stretching speed was maintained at 30 mm/min.

#### *2.4. The Preparation of Data Gloves and Data Acquisition*

The configuration of strain sensors in data glove is shown in Figure 1a. To monitor the bending degree of fingers, the sensors were attached to the corresponding finger joint position of knitted gloves. Figure 1b shows the gesture library of this article. When making these gestures, the resistance signal acquisition of sensors was realized by the data acquisition card (Ni-6211). In the detection circuit, sensors were connected in parallel, and divider resistors were connected in series with the sensor on each branch. Based on the differential mode of the data acquisition card, the voltage on both sides of the sensors were collected completely and displayed in a laptop program designed with LabVIEW. The resistance of the sensor can be converted by Ohm's law.

**Figure 1.** (**a**) Configuration of flexible sensor in data glove. (**b**) Gesture library.

#### *2.5. The Design of Machine-Learning Algorithm*

A flow diagram of the machine-learning process is shown in Figure 2. During the training process, the change in voltage due to predetermined gestures was firstly merged into a matrix as the input of mRMR algorithm to discard useless information and retain relevant signals, which improves the generalization ability of the model and reduces the complexity of the algorithm. Subsequently, the multi-segment classification model was applied to classify gestures by using the extracted features with Logit AdaBoost algorithm. Based on the training results of the previous weak classifier, the Logit AdaBoost algorithm evaluated and redefined the parameter weights to update the classifier. Additionally, the Logit AdaBoost algorithm was not sensitive to noise and outliers, which prevented the model from over fitting to a certain extent. The classification process included two steps. First, static signals and dynamic signals were effectively distinguished. Then, combined with the sample characteristics and distribution, the motion trajectory of the finger was further subdivided. Finally, 10-fold cross-validation method was used to evaluate the model. It can make full use of the samples in the training process, which avoids over fitting.

**Figure 2.** The process of a multi-segment classification model.

#### **3. Results and Discussions**

#### *3.1. Morphology Characterization*

As shown in Figure 3a, based on a sandwich structure composed of a r-GO layer and two PDMS layers, the overlapping state of graphene sheets can change during the deformation of the sensor and recover under the constraint of elastic material (PDMS), resulting in the electromechanical response of sensors.

**Figure 3.** Topography characterization of the sensor. (**a**) Cross-section of the sensor. (**b**) SEM images of the r-GO film.

Figure 3b shows the that reduced graphene oxide is uniformly distributed in the r-GO layer, which is the basis of ensuring the stable electrical performance of the sensor.

#### *3.2. Piezoresistive Sensing Response*

Figure 4a shows the relationship between the sensor resistance and applied strains, from which the sensitivity of the strain sensor can be obtained. The sensitivity of the strain sensor can be calculated by the following equation:

$$GF = \frac{\Delta R / R\_0}{\varepsilon} \tag{1}$$

$$
\Delta R = R - R\_{\text{0}} \tag{2}
$$

where *ε* is the strain of the strain sensor, *R* represents the resistance of sensor after stretching, *R*<sup>0</sup> is the resistance of sensors at *ε* = 0%. Figure 4a shows the strain sensor provides high sensitivity for detecting hand movements (GF~2.69 at strains below 12%, GF~5.05 at the strain ranges from 12% to 36%). Furthermore, the strain sensors also show a stability response over a large number of stretch–release cycles (above 1500 cycles), as shown in Figure 4b.

**Figure 4.** (**a**) The relationship between the sensor resistance and the applied strain. (**b**) Sensor stability under 1500 stretch−release cycles at 10% strain.

#### *3.3. The Selection of Classification Algorithm and Feature*

In order to find the data correlation, we extracted 10 characteristics as signal features, which are related to indicators of time domain, frequency domain and entropy. Then, we used the mRMR algorithm to check the importance of the features in the two stages. As shown in Table 1, waveform factors and skewness have the great correlation with the classification results of the first stage. Skewness and waviness are of significance for the classification of the second stage.


**Table 1.** Feature importance evaluation based on mRMR algorithm.

RUSBoost, Logit AdaBoost and Bagging are the common machine learning algorithms in data classification. Based on selected features through the mRMR algorithm, the classification results of the three algorithms on gesture data are shown in Figure 5. Area under curve (AUC) is a statistical concept. It represents the probability that the predicted value of positive cases is greater than the predicted value of negative cases. Whether it is the first stage or the second stage, the AUC of Logit AdaBoost algorithm is maximum. It shows that the model generated by Logit AdaBoost algorithm is most effective for gesture classification.

**Figure 5.** Comparison of model performance by AUC for RUSBoost, Logit AdaBoost, Bagging. (**a**) Performance of different algorithm models in the first stage. (**b**) Performance of different algorithm models in the second stage.

#### *3.4. The Gesture Recognition Modeling*

In the first stage, dynamic signals are separated from all samples for subsequent training. As shown in Figure 6a, the classification model can correctly classify most of the static gesture data and dynamic gesture data. Table 2 further shows the classification results of static signals and dynamic signals. Positive predictive value (PPV) is an index to evaluate the level of correct classification. The lower PPV of dynamic signals, the more sample capacity is lost in the second stage, resulting in unobvious and incomplete features as gesture output. Additionally, false discovery rate (FDR) represents the probability of misclassification, which affects the interference degree of data directly. High PPV level (96.9%) and low FDR level (3.1%) illustrate that effective gesture features are completely preserved.

The ROC curve after 10-fold cross-validation is shown in Figure 6b. Assuming that the dynamic signal is positive, the AUC value of the model in the first stage is 0.99 and the accuracy reaches 98.4%, indicating that the model can further eliminate invalid static signals.

Based on defined gestures, the movement of the hand is divided into the bending and straightening of fingers. Figure 6c and Table 3 shows the accuracy of classification remains at a high level. Additionally, Figure 6d illustrates the AUC value of the model is 0.97, which only has subtle decreases compared with the first stage. The good classification results of two stages indicates that the multi-segment classification model has the generalization ability for gesture recognition.

**Figure 6.** Test results for classification model of two phases. (**a**) Confusion matrix of dynamic gesture and static gesture. (**b**) ROC curve of classification model assuming dynamic gesture is positive. (**c**) Confusion matrix of straightening fingers and bending fingers. (**d**) ROC curve of classification model assuming straightening fingers is positive.

**Table 2.** Evaluation of classification model to distinguish static signals and dynamic signals.


**Table 3.** Evaluation of classification model to judge finger motion trajectory.


#### **4. Conclusions**

This article reported a type of machine-learning empowered gesture recognition glove. Combined with the electrical characteristics of flexible strain sensors and the signal processing ability of machine learning algorithms, data gloves judged the specific actions of hands accurately, which showed many commendable features, such as low weight and good comfortableness. At the same time, we paid attention to the interference of invalid static signals in the training and classification process. The model successfully extracted effective dynamic signals from all signals through multi-segment classification based on Logit AdaBoost algorithm. It is of significance to enhance the generalization ability of the recognition model and increase the accuracy of gesture recognition. However, not all irrelevant factors have been excluded for this model, which requires further research.

**Author Contributions:** Conceptualization, K.Z.; methodology, J.L., Y.Q. and Z.G.; writing—original draft preparation, J.L., Y.Q. and Z.G.; writing—review and editing, J.L., Y.Q., Z.G., L.Z., Q.Z. and K.Z.; supervision, L.Z., Q.Z. and K.Z.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (2232022G-01 and 19D110106), the National Natural Science Foundation of China (NO. 51973034).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

## *Proceeding Paper* **Zinc-Ion Battery on a Polyester-Cotton Textile †**

**Sheng Yong 1,\*, Nick Hillier 1,2 and Steven Beeby <sup>1</sup>**


**Abstract:** This work presents a simple, scalable and flexible zinc-ion secondary battery, fabricated on top of a textile substrate via standard fabrication processes. The proposed zinc-ion battery was fabricated on top of a polyester-cotton textile using solution-based processes and inexpensive cathode, anode and electrolyte materials. This battery achieved an area capacity of 19.1 <sup>μ</sup>Ah·cm−<sup>2</sup> between 1.9 and 0.9 V.

**Keywords:** e-textile; flexible battery; zinc-ion battery

#### **1. Introduction**

Wearable electronics also known as e-textile are the integration of flexible electrical devices in clothing and accessories. In these systems, sufficient electrical energy is required for devices such as sensor nodes [1], microprocessors and transceivers [2]. At present, such systems are typically powered using an external electrical connection, battery or supercapacitor [3] which require frequent replacement [4] and/or need to be physically removed before washing. Therefore, a flexible energy-storage device such as a zinc-ion battery (ZIB) integrated on top of or inside a textile is important for e-textile applications.

ZIBs are rechargeable secondary electrical energy-storage devices. In comparison to ordinary secondary batteries such as lithium-ion and aluminium air batteries. This type of battery demonstrates distinct advantages, such as improving device safety by not using aggressive or environmentally unfriendly electrolytes (in lithium-ion batteries) and can be fully sealed without the requirement of oxygen access such as in an aluminium air battery. In addition, the assembly of ZIBs can be performed in the air, offering reduced fabrication complexity. Previously Yong et al. [5] presented a zinc-ion secondary textile battery. The manganese oxide cathode, zinc anode and polymer separator were integrated into a single polyester cotton textile layer. After vacuum impregnating the battery with an aqueous electrolyte, the zinc-ion battery device achieved an areal capacity of 35.6 <sup>μ</sup>Ah·cm−<sup>2</sup> between 1.9 V and 0.9 V. The use of the polyester cotton textile as the separator layer holder increased the device thickness, introducing extra encapsulation challenges. Xu et al. [6] reported flexible vanadium (V) oxide carbon-fiber cloth electrodes for potential zinc-ion battery application. This electrode demonstrated an areal capacity of 154 mAh·g<sup>−</sup>1. These examples demonstrate the capability of fabricated ZIBs within textile material. However, the electrode material vanadium (V) oxide is corrosive and required extra consideration during the device-encapsulation process. It can increase the device thickness and mechanical inflexibility, introducing extra encapsulation challenges for real-world power source units in e-textile systems.

This paper builds upon the previous works [5], the proposed zinc-ion battery was fabricated on top of a polyester-cotton textile using solution-based processes and inexpensive materials. The battery's anode was a flexible zinc polymer film prepared via spray coating,

**Citation:** Yong, S.; Hillier, N.; Beeby, S. Zinc-Ion Battery on a Polyester-Cotton Textile. *Eng. Proc.* **2023**, *30*, 20. https://doi.org/ 10.3390/engproc2023030020

Academic Editors: Steve Beeby, Kai Yang, Russel Torah and Theodore Hughes-Riley

Published: 16 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and the battery's cathode was a spray-coated manganese (II, III)-oxide (Mn3O4) polymer layer on a polyester-cotton textile. The separator layers were implemented on top of the battery's cathode with doctor blading or screen printing followed by a phase inversion process. The battery was tested with an aqueous electrolyte to study its discharge performance.

#### **2. Material and Methods**

Figure 1 shows the fabrication process of the zinc-ion battery. Firstly, silver paste (TC-C4001, Smart Fabric Inks Ltd., Southampton, UK) was screen printed on the hotmelt PEVA encapsulation film that dried at room temperature for 24 h and polyester-cotton textile where the silver paste soaked through the textile, forming the current collector after cured in a box oven at 100 ◦C for 30 min. A thin film of nickel metal was sputter coated on top of silver textile forming an inert layer. A Mn3O4/polymer (85:15 by wt.%) cathode was spray coated onto the top of the nickel film. The coated textile was then dried in a box oven at 120 ◦C for 30 min. The current collector and cathode layer were screen-printed with a co-polymer solution and treated with a phase-inversion process to form a porous membrane published previously [5]. This co-polymer membrane acts as the separator of the supercapacitor that prevents electrical short circuitry but allows ions to transfer between the cathode and anode electrodes. The anode layer used to evaluate the battery performance was a spray-coated zinc/polymer (95:5 by wt.%) on top of silver that printed on the encapsulation film. The current collector, cathode, separator combined textiles and the zinc metal anode foil shown in Figure 2a were punched into circular shapes with a diameter of 1 cm, added with an aqueous electrolyte containing 1 M ZnSO4 + 0.1 M MnSO4 through the hole before being heat pressed together to form the full device.

**Figure 1.** Schematic, illustration of the fabrication process.

**Figure 2.** (**a**) Photograph of manganese oxide cathode textile, cathode textile with membrane coating and zinc anode layer with encapsulation film, (**b**) battery initial voltage reading under 90 degrees bending.

#### **3. Results**

The manganese-oxide cathode textile, cathode textile with membrane coating and zinc anode layer with encapsulation film are shown in Figure 2a. The device is assessed using a potentiostat Autolab pgsatat101(Metrohm Autolab, Utrecht, The Netherlands). Its electrochemical performance was obtained from galvanostatic cycling (GC) at 1 mA·cm−<sup>2</sup> and cyclic voltammetry (CV) at 10 mV·s−<sup>1</sup> between 0.9 and 1.9 V.

The cycling test in Figure 3a was derived from the GC test at 1; this battery achieved an area capacity of 19.1 <sup>μ</sup>Ah·cm−<sup>2</sup> between 1.9 and 0.9 V after the initial test cycle. Figure 3b shows the CV test results after the first test cycle for the zinc-ion battery on the textile. The oxidation (charging) peak occurred at 1.72 V; it also demonstrated a reduction (discharging) peak at 1.26 V. These are typical voltage peaks for the redox reactions in a zinc-ion secondary battery with manganese-oxide cathodes.

**Figure 3.** (**a**) GC-derived voltage charge and discharge result of the zinc-ion battery at 1 mA cm−<sup>2</sup> tested between 0.9 and 1.9 V, (**b**) CV tests between 0.9 V to 1.9 V at the scan rate of 10 mV·s<sup>−</sup>1.

#### **4. Conclusions**

This paper presents an encapsulated, flexible zinc-ion battery on a single piece of polyester cotton. The operating voltage for this textile battery in this work was between 0.9–1.9 V and achieves an area-specific capacity of 19.1 <sup>μ</sup>Ah·cm−<sup>2</sup> and demonstrates good bending durability. In comparison with the previous devices [6], the proposed zinc-ion battery is encapsulated and tested without tube fitting. Future work will include optimizing the formulation and fabrication method of the polymer membrane in the textiles for better electrochemical performances and durability. The final device can be applied in a wide range in the e-textile system.

**Author Contributions:** Conceptualization, S.Y.; methodology, S.Y. and N.H.; validation, S.Y. and N.H.; formal analysis, S.Y.; investigation, S.Y.; resources, S.B.; data curation, S.Y.; writing—original draft preparation, S.Y.; writing—review and editing, N.H. and S.B.; visualization, S.Y.; supervision, S.B.; project administration, S.B.; funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors thank the EPSRC for supporting this research with grant references EP/P010164/1 and EP/I005323/1.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data for this paper can be found at DOI: https//doi.org/10.5258/ SOTON/D1127.

**Acknowledgments:** This work was also supported by the Royal Academy of Engineering under the Chairs in Emerging Technologies scheme.

**Conflicts of Interest:** Steven Beeby is the director of Smart Fabric Inks.

#### **References**


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