Finger Gestures Research Articles

Ethnomathematics study of Minangkabau tribe: formulation of mathematical representation in the Marosok traditional trading

ABSTRACT This research aims to explore the use of ethnomathematics in revealing mathematical representations and in the formulation of a calculation system used in the Marosok trading tradition by the Minangkabau tribe in West Sumatra. Ethnomathematics studies ideas in various cultural activities practiced by ethnic, social, or professional groups. The tradition involves nonverbal communication, such as shaking hands with ‘Marosok’ or touching covered fingers to obtain price agreements in trading livestock. Therefore, this study was conducted using qualitative approaches and the ethnographic method via field notes, unstructured interviews, and documentation studies. The findings showed that mathematical representations of finger symbols and gestures in the Marosok Tradition contain basic numbers. These include half, one, two, two and a half, three, four, five, etc. with functions, such as addition and subtraction, which are used to obtain other numbers needed in livestock trading transactions using specific formulas.

Waterproof Mechanically Robust Multifunctional Conformal Sensors for Underwater Interactive Human–Machine Interfaces

Wearable sensors with water resistance and mechanical durability are of great value in dealing with long‐term movement and remote control in harsh environments. However, achieving high sensitivity with long‐term stability and real‐time remote control in a watery environment is still a challenge. Herein, the waterproof wearable sensors with good mechanical robustness composed of laser‐induced graphene and in situ‐coated protective silicone layers are reported. By being integrated with high‐capacitance ion‐gel dielectrics, the conformal sensors can detect multiple stimuli, including strain, temperature, and pressure. The long‐term water resistance of strain sensors is evaluated by continuously monitoring the resistance in underwater, sweat, and saline environment for up to 5.5 h. Underwater wireless remote control of a robotic hand is further demonstrated by mounting five sensor arrays. Moreover, different finger gestures are well recognized, making these sensor devices promising candidates for versatile waterproof wearable electronics and robotics technology.

Motor sequence learning in patients with ideomotor apraxia: Effects of long-term training

Recent studies show that limb apraxia is a quite frequent, yet often underdiagnosed, higher motor impairment following stroke. Because it adversely affects every-day life and personal independence, successful rehabilitation of apraxia is essential for personal well-being. Nevertheless, evidence of long-term efficacy of training schemes and generalization to untrained actions is still scarce. One possible reason for the tendency of this neurological disorder to persist may be a deficit in planning, conceptualisation and storage of complex motor acts.This pilot study aims at investigating explicit motor learning in apractic stroke patients. In particular, we addressed the ability of apractic patients to learn and to retain new explicit sequential finger movements across 10 training sessions over a 3-week interval.Nine stroke patients with ideomotor apraxia in its chronic stage participated in a multi-session training regimen and were included in data analyses. Patients performed an explicit finger sequence learning task (MSLT – motor sequence learning task), which is a well-established paradigm to investigate motor learning and memory processes.Patients improved task performance in terms of speed and accuracy across sessions. Specifically, they showed a noticeable reduction in the mean time needed to perform a correct sequence and the number of erroneous sequences. We found also a trend for improved performance at the Goldenberg apraxia test protocol: “imitation of meaningless hand and finger gestures” relative to when assessed before the MSLT training.Patients with ideomotor apraxia demonstrated the ability to acquire and maintain a novel sequence of movements; and, this training was associated with hints towards improvement of apraxia symptoms.

Duplex Sign Language Communicator

In this paper we are introducing a sign language converter which works as a duplex system as it can convert text to sign language as well as it can do a real time video to text conversion. It is basically a system that can be used by all people who know sign language as well as who are not familiar with it. The main aim of this system is to involve the specially abled people as much as possible to interact with others. Our system uses the basic NLP i.e. the Natural language Processing and algorithms like CNN classifier to make the implementation of this translator. Along with that this system focuses on the Indian Sign Language so that it can be used by our country people. The finger gestures are captured by the camera and using various machine learning algorithms the system will automatically translate the signs to the readable text, similarly in sign to text conversion, based on the data sets and various Machine learning algorithms the text will be converted to sign language.

Sensitivity of Surface Acoustic Wave Sensors Based LiNbO3 128° Y-X and at-Quartz to PM10 and PM2.5

IntroductionParticulate matter (PM) was estimated to cause around 7 million premature deaths worldwide per year according to World Health organization [1]. These particles can cause diseases leading to premature death [2]. Over the last decade, the monitoring of PM became an important issue on a worldwide scale. The miniaturization of the existing bulky systems has also become necessary to reduce their cost. To address this need, a new miniaturized system for PM monitoring was previously developed in our team [3]. It takes advantage of both a 3 Lpm cascade impactor and surface acoustic waves (SAW) sensors to respectively separate and measure particles. The present study aims to compare the sensitivity of two SAW devices. The first exploits Love waves on an AT cut Quartz substrate and the second is based on Rayleigh waves from a LiNbO3 Y-X 128° substrates. The latter presents a high electromechanical coupling factor that could be exploited for the self-cleaning of the device after the measurement. This could allow overcoming the fouling issue faced with impactors that wouldn’t be solved with quartz based sensors which suffer from an insufficient electromechanical coupling. Method In this study we used a dedicated 3 Lpm cascade impactor equipped with SAW delay lines as collecting plate. The Rayleigh wave sensor and the Love wave sensor were designed and fabricated respectively on LiNbO3 Y-X 128° cut and AT-Quartz cut substrates using a conventional photolithography process. The interdigital transducers (IDT) consist of double finger pairs made of 200 nm thick aluminum with a periodicity p=10 µm and a wavelength λ=40 µm. Hence, the working frequency is 125 MHz for the Love mode and 98 MHz for the Rayleigh mode. The propagation of Love wave needs a guiding layer. For that purpose, a silica layer was deposited using sputtering technique with shear velocity lower than the piezoelectric substrates. The region between the two IDTs constitutes the sensing area.Preliminary tests have been performed using candle smoke. As the obtained PM concentrations were unstable and difficult to reproduce, we have developed an experimental set-up bench consisting of a 1m3 chamber, a particle’s generator AGK 2000 and an optical system FIDAS® 100. Thus, sodium chloride (NaCl) was used to produce PM2.5 while PM10 were generated from silicon carbide (SiC) in the 0 to 200 μg/m3 concentration range. When the particles impact on the sensor surface, the velocity of the acoustic waves decreases due to the sensors’ high gravimetric sensitivity. Accordingly, the wave phase velocity shift is measured by monitoring the phase at constant frequency with an open loop interrogation [4]. It’s then correlated to the particles concentration. Results and Conclusions Figure 1 and 2 shows plots of phase derivative dϕ/dt of sensors based AT-Quartz (black) and LiNbO3 (red) with the concentration of PM10 and PM2.5 respectively measured with the optical system FIDAS®. As expected, the sensitivity of the Rayleigh wave sensor based on LiNbO3 is lower than the Love wave sensor based on quartz for both PM10 and PM2.5. The sensor’s sensitivity is estimated by applying a linear fit of the plotted data. The sensitivity of sensors based on quartz is 3.10-4 °/s.μg/m3 for PM 2.5 and 5.10-5 °/s.μg/m3 for PM10. For sensors based on LiNbO3, the sensitivity is 1.10-4 °/s.μg/m3 for PM2.5 and 1.10-5 °/s.μg/m3 for PM10. The feature that the sensors’ response is greater with PM2.5 than with PM10 can be explained by the fact that most adhesion forces are linearly dependent on particle diameter [5] which is often described as the rebound effect. As a result, the smaller particles settle more on the surface of the sensor resulting in a more significant slowdown of the wave. To ensure that each stage is measuring the targeted size range, we took images of the sensors surface at the end of the experiment using a microscope. Figures 3, 4 show the surface of the PM2.5 stage and figures 5, 6 the one of the PM10 stage. One can observe that, as expected, the PM2.5 contain more particles than the PM10 and that the particle sizes correspond well to the ranges [0.3 μm, 2.5 μm] and [2.5 μm, 10 μm]. It has therefore been shown that LiNbO3 sensors, although less sensitive, can also be used for fine particles detection. They still need work to improve their performance and limit the rebound effect, but they remain very promising for the development of self-cleaning systems.

Production environment of tomorrow (ProMo): Partially automated repair process of small tool moulds, forming tools, injection moulding tools and sand casting tools

Small defects in the grain or major damage to a moulded part or tool can bring production to a standstill. SMEs in particular have neither the personnel nor the equipment to repair such damage on their own, so they send it to specialised contractors. The repair process is carried out manually, depending on the accuracy requirements, and is usually completed by a finishing process. This work requires qualified personnel and, at the same time, requires a lot of time in case of larger damages. In this paper we present a way to map the Maintenance, Repair and Operations (MRO) process chain in a partially automated manner.The symbiosis of individual technologies results in a significantly increased efficiency of the MRO process chain, which continues to focus on people and their process knowledge.While Directed Energy Deposition (DED) for the MRO of moulded parts is used widely, usually a high manual effort in measuring the component geometries and teaching of the machine tool paths is necessary. However, there are clear advantages compared to the manufacture of new parts or manual laser welding repair. At the same time, the resource and energy requirements can often be significantly reduced compared to new part production. Promo focuses on automating the time-consuming machine programming by reducing the number of necessary work steps in CAD/CAM-based program creation.Based on a subsequent robot-guided scan, a digital actual 3D model is generated. Due to intelligent path planning algorithms, no manual programming of the robot is necessary and at the same time it is possible to detect components of different sizes, shapes and covers in this system with a minimum of effort. In addition, the operator passes on elementary information, such as the approach path of the milling head, to the subsequent processes by means of finger gestures and can thus significantly reduce tedious CAM programming steps. Now, the scanned component is transferred to a 3D-CAD model and a target/actual comparison is created for the damaged areas. Those are milled out in a defined manner and then restored using DED.

Perbandingan Antara Domain Waktu dan Frekuensi untuk Pengenalan Sinyal EMG

One way to recognize hand gestures is to use signal electromyography (EMG). The processed signal can use the time domain, frequency domain, or a mixture of the two domains. Meanwhile, the classification method that is widely used recently is the classification of Artificial Neural Networks (ANN). This paper presents a comparison study between time domains with frequency domain for EMG signals using ANN classification. This comparison aims to find out a better method for controlling the hand robot. The time domain features are root mean square (RMS) of the signal, while the signal’s octave band becomes a feature of the frequency domain. The EMG signals were obtained from the subject with eight fingers gestures. The results of this classification are used to control the robot’s hand. The success of each method in recognizing hand movements was counted. In addition, the response speed of the robot in changing positions is measured. The results showed that features using the frequency domain had a higher percentage of success than another domain. But the speed and memory used then the system using signals in the time domain is better.

Finger movements classification based on fractional Fourier transform coefficients extracted from surface EMG signals

EMG signals have played a pivotal role as a fundamental component of myriad modern prostheses to control prostheses’ movements as well as identifying individual and combined hand or finger gestures. Despite a great deal of interest in these signals, the non-stationary nature of biological EMG signals has led to complications in EMG applications. Stationary signals have been analyzed plainly by time domain approaches like Fourier Transform, while non-stationary signals analysis is not satisfactory to be carried out with such method as it is not capable to illustrate the incidence time of various frequency components, besides, extracting both time and frequency information is essential. The Fractional Fourier Transform (FrFT), which is a generalization of classical Fourier Transform, is able to demonstrate the variable frequency of non-stationary signals. In this paper, FrFT technique with different fractional orders is employed as a novel and sophisticated feature extraction method for EMG signals of 8 subjects including 6 men and 2 women recorded in 10 different finger movements which are 5 individual and 5 combined postures. Windowing method and the t-test approach are utilized to select the best FrFT extracted coefficients as features. Employing KNN method to classify 10 different classes and the average classification accuracy of 98.12% of proposed method is a significant indicator of its sufficient performance.

Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals

Objective. Estimation of finger kinematics is an important function of an intuitive human–machine interface, such as gesture recognition. Here, we propose a novel deep learning method, named long exposure convolutional memory network (LE-ConvMN), and use it to proportionally estimate finger joint angles through surface electromyographic (sEMG) signals. Approach. We use a convolution structure to replace the neuron structure of traditional long short-term memory (LSTM) networks, and use the long exposure data structure which retains the spatial and temporal information of the electrodes as input. The Ninapro database, which contains continuous finger gestures and corresponding sEMG signals was used to verify the efficiency of the proposed deep learning method. The proposed method was compared with LSTM and Sparse Pseudo-input Gaussian Process (SPGP) on this database to predict the ten main joint angles on the hand based on sEMG. The correlation coefficient (CC) was evaluated using the three methods on eight healthy subjects, and all the methods adopted the root mean square (RMS) features. Main results. The experimental results showed that the average CC, root mean square error, normalized root mean square error of the proposed LE-ConvMN method (0.82 ± 0.03,11.54 ± 1.89,0.12 ± 0.013) was significantly higher than SPGP (0.65 ± 0.05, p < 0.001; 15.51 ± 2.82, p < 0.001; 0.16 ± 0.01, p < 0.001) and LSTM (0.64 ± 0.06, p < 0.001; 14.77 ± 3.21, p < 0.001; 0.15 ± 0.02, p = < 0.001). Furthermore, the proposed real-time-estimation method has a computation cost of only approximately 82 ms to output one state of ten joints (average value of 10 tests on TitanV GPU). Significance. The proposed LE-ConvMN method could efficiently estimate the continuous movement of fingers with sEMG, and its performance is significantly superior to two established deep learning methods.

Application of WALANT technique for repairing finger skin defect with a random skin flap

BackgroundWide-awake local anesthesia no tourniquet (WALANT) technique has emerged among hand surgeons with other indications. Surgeries involving pedicled flap and revascularization are no longer used as contraindications. The present study aimed to evaluate the feasibility and merits of the WALANT technique in random skin flap surgery.MethodsFrom May 2018 to March 2019, 12 patients with finger skin defects repaired with random skin flaps were reviewed. Abdominal skin flaps or thoracic skin flaps were used to cover the wound. Both the fingers and the donor sites were anesthetized by the WALANT technique. A 40-mL conventional volume consisted of a mixture of epinephrine and lidocaine. A volume of 5 mL was injected at the distal palmar for nerve block, the other 5 mL was injected around the wound for hemostasis, and the remaining was injected at the donor site of flaps for both analgesia and hemostasis. Baseline data with respect to sex, age, side, type of finger, donor sites, flap size, dosage of anesthetics, usage of finger tourniquet, intraoperative and postoperative pain, hemostasis effect, operation time, Disabilities of the Arm, Shoulder, and Hand Questionnaire (QuickDASH) score, and hospitalization expense, were collected.ResultsAll patients tolerated the procedure, and none of them needed sedation. Single finger skin defect in 8 patients and double finger skin defect occurred in 4 patients; 5 patients were repaired by abdominal skin flaps, and 7 patients were repaired by thoracic skin flaps. The good surgical field visibility was 91.7%. All flaps survived adequately, without necrosis, pulling fingers out, and other complications. The average visual analog scale (VAS) score of the maximal pain was 1.1 in fingers vs. 2.1 in donor sites during the operation. On postoperative day one, the average VAS score of the maximal pain in fingers and donor sites was 1.3 and 1.1, respectively. The average hospitalization expense before reimbursement of the whole treatment was 11% less expensive compared to the traditional method. The average QuickDASH score was 9.1.ConclusionsUnder wide-awake anesthesia, patients have the ability to control their injured upper extremities consciously, avoiding the complications due to pulling flap pedicles. With the merits of safety, painlessness, less bleeding, and effectivity, the WALANT technique in random skin flaps is feasible and a reliable alternative to deal with finger skin defect.

A Flexible Dual-Modal Sensing System for Synchronous Pressure and Inertial Monitoring of Finger Movement

In this paper, a flexible dual-modal sensing system is proposed for monitoring the pressure and inertial information of the finger movement. The sensing system is composed of a pressure sensor array and two inertial measurement units (IMUs) for sensing the fingertip pressure and the finger gesture, respectively. The graphene aerogel (GA) with excellent sensitivity is utilized as the sensing element of the pressure sensor array, and five GA sensing elements are designed to detect the pressure distribution of different fingertip areas. With serpentine circuitry structure sealed by polydimethylsiloxane (PDMS), the pressure sensor array can demonstrate flexibility, stretchability, and biocompatibility when mounted on the finger. The two IMUs are attached to the back of the finger. The signal and power supply electrodes of the pressure sensor array are connected with the IMUs, which can obtain the pressure and inertial signals simultaneously. A prototype was fabricated and experiments were carried out to test the basic performance of the sensing system. It has been verified that the pressure sensor array shows high resolution of 108.8 kPa and well stability after a 10-hour repeatability experiment. The IMUs are also reliable for sensing inertial information after calibration. Measurement of four daily hand movements and recognition of ten typical grasping gestures were conducted to verify the feasibility of the proposed system. Experimental results showed that the sensing systems could classify the ten gestures with a high accuracy of 97%, which verified the approach can provide a promising path for hand movement measurement and gesture recognition.

Exploring 3D miniatures with action simulations by finger gestures: Study of a new embodied design for blind and sighted children.

Tactile books for blind children generally contain tactile illustrations referring to a visual world that can be difficult to understand. This study investigates an innovative way to present content to be explored by touch. Following embodied approaches and evidence about the advantages of manipulations in tactile processing, we examined 3D miniatures that children explored using their middle and index fingers to simulate leg movements. This "Action simulations by finger gestures-ASFG" procedure has a symbolic relevance in the context of blindness. The aim of the present study was to show how the ASFG procedure facilitates the identification of objects by blind and sighted children. Experiment 1 examined the identification of 3D miniatures of action objects (e.g. the toboggan, trampoline) by 8 early blind and 15 sighted children, aged 7 to 12, who explored with the ASFG procedure. Results revealed that objects were very well identified by the two groups of children. Results confirmed hypotheses that ASFG procedures are relevant in the identification process regardless of the visual status of subjects. Experiment (control) 2 studied identification of tactile pictures of same action objects by 8 different early blind and 15 sighted children, aged 7 to 12. Results confirmed that almost all objects obtained lower recognition scores in tactile pictures than in 3D miniatures by both groups and showed surprisingly higher scores in blind children than in sighted children. Taken together, our study provides evidence of the contribution of sensorimotor simulation in the identification of objects by touch and brings innovative solutions in book design for blind people. Moreover, it means that only the ASFG procedure has a very inclusive potential to be relevant for a larger number of subjects, regardless of their visual skills.

Enabling Fine-Grained Finger Gesture Recognition on Commodity WiFi Devices

Gesture recognition has become increasingly important in human-computer interaction and can support different applications such as smart home, VR, and gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present a fine-grained finger gesture recognition system by using commodity WiFi without requiring user to wear any sensors. Our system takes advantages of the fine-grained Channel State Information available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. We devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Lastly, we utilize multiple WiFi links and larger bandwidth at 5GHz to achieve finger gesture recognition under multi-user scenario. Our experimental evaluation in different environments demonstrates that our system can achieve over 90 percent recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can provide accurate gesture recognition under different scenarios.

Validation of a modular and wearable system for tracking fingers movements

&lt;p class="Abstract"&gt;Supervising manual operations performed by workers in industrial environments is crucial in a smart factory. Indeed, the production of products with superior quality, at higher throughput rates and reduced costs with the support of the Industry 4.0 enabling technologies is based on the strict control of all the resources inside the factory, including workers. This paper shows a protocol for validating a new wearable system for tracking finger movements. The wearable system consists of two measuring modules worn on the thumb and on the index measuring flexion and extension of the proximal interphalangeal (PIP) joint by a stretch sensor and rotation of the proximal phalanx (PP) by an inertial measurement unit. An optical system is used to validate the system by capturing specific finger movements. Four movements that simulate typical tasks and gestures, such as grasp and pinch, were performed to validate the proposed system. The maximum root-mean-square error is 3.7 deg for the roll angle of PP. The resistance changes of the stretch sensors with respect to flexion and extension of PIP joint is 0.47 Ohm/deg. The results are useful for the data interpretation when the system is adopted for monitoring finger movements and gestures.&lt;/p&gt;

Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals

Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals to recognize hand and finger gestures in a non-invasive manner. However, Wi-Fi signals usually contain signal interference, background noise, and mixed multipath noise. In this study, Wi-Fi Channel State Information (CSI) is preprocessed by singular value decomposition (SVD) to obtain the essential signals. Sign language includes the positional relationship of gestures in space and the changes of actions over time. We propose a novel dual-output two-stream convolutional neural network. It not only combines the spatial-stream network and the motion-stream network, but also effectively alleviates the backpropagation problem of the two-stream convolutional neural network (CNN) and improves its recognition accuracy. After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. Experimental results show that SVD preprocessing can improve the performance of our dual-output two-stream network. For home, lab, and lab + home environment, the average recognition accuracy rates are 99.13%, 96.79%, and 97.08%, respectively. Compared with other methods, our method has good performance and better generalization capability.

Flexible Strain Sensor with Tunable Sensitivity via Microscale Electrical Breakdown in Graphene/Polyimide Thin Films.

Carbon-based piezoresistive nanomaterials are widely used for the fabrication of flexible sensors. Although our previous work demonstrated that an electrical breakdown (EBD) process can endow a graphene/polyimide (G/PI) composite with piezoresistivity, the formation of EBD-induced electrical traces with high consistency in bulk nanocomposites remains a technical challenge. With the aim of developing highly sensitive flexible strain sensors using a batch fabrication process, we introduce herein a microscale EBD (μEBD) method to form localized piezoresistors with diverse shapes in a G/PI thin film. The results of scanning electron microscopy, Raman spectroscopy, and electromechanical tests indicate that high piezoresistivity is derived from the high porosity of the carbonized conductive traces generated by the μEBD process. The gauge factor of the μEBD-treated G/PI strain sensor is over 20 times greater than that of the as-prepared G/PI film, and the sensitivities of the strain sensors can be tuned by varying the applied current in the μEBD process. We also demonstrate the potential applications of μEBD-treated G/PI strain sensors in the fields of finger gesture detection, sound pressure measurement, and airflow sensing.

Recognition of additional myo armband gestures for myoelectric prosthetic applications

Myoelectric prostheses are a viable solution for people with amputations. The challenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo arm band device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.

İşitme ve Konuşma Güçlüğü Çekenlere Yardımcı Olan Eldiven Tasarımı

Communication is the proper transfer of feelings and thoughts to others. The most used communication among people is verbal communication, which is based on hearing and speech. However, it is observed that individuals with disabilities due to hearing or speech loss either do not use this communication method or can use it limitedly. These individuals communicate with each other through methods such as sign language, alphabet created using hand or finger movements. In this study, a system that can perceive sign language has been developed for individuals with speech and hearing impairments in order to communicate more easily with each other and with individuals who do not know sign language. Flex sensors are placed on the glove to be used in the developed system. Flex sensors were connected to Arduino with voltage divider circuits and varying voltage values were taken. In this system, a new sign language was developed based on the existing sign language. In the developed system, each combination of finger and hand gestures represents a separate word. These combinations are logically numerical sets consisting of logic 0 and logic 1 in computer language. Finger movements corresponding to the words to be used in the study were introduced to the developed system. The words corresponding to the combinations have been transferred to the system library and stored. In the study, the most used words in daily life were determined and test studies were conducted. A word introduced to the system in return for each combination made by the hand and finger movements performed by the user was exported via the screen.

Self-Sealing Carbon Patterns by One-Step Direct Laser Writing and Their Use in Multifunctional Wearable Sensors.

A combined photothermal simulation and experimental study leads to a novel internal reflection-assisted direct laser writing carbonization method (IR-DLWc), which enables in situ fabrication of carbon features/patterns that are self-sealed in the interior of a thin polyimide (PI) film in one step without additional packaging procedures. With this new method, carbon line patterns that are fully contained in a 50 μm PI film are fabricated, characterized, and evaluated for their electrical and piezoresistive performance. The self-sealing character of the carbon features created by IR-DLWc imparts them unprecedented mechanical stability/robustness as compared to those fabricated by the conventional DLWc method. Upon applying a double-writing scheme and strain-engineering treatment, the IR-DLWc-created carbon lines show significantly improved piezoresistive sensitivity with a gauge factor evaluated to be 428 in tension and 107 in compression. The high piezoresistive sensitivity, excellent dynamic response, reasonably good durability, self-sealing character, and compliant nature of the IR-DLWc generated carbon patterns make them suitable for a variety of wearable sensing applications. In this work, we demonstrated their use as a tactile sensor for sensing contact force; a functional bandage for monitoring physiological activities like swallowing, pulsing, and breathing; and a glove sensing system for finger gesture recognition.

A New Measure to Characterize the Degree of Self-Similarity of a Shape and Its Applicability.

We propose a new measure (Γ) to quantify the degree of self-similarity of a shape using branch length similarity (BLS) entropy which is defined on a simple network consisting of a single node and its branches. To investigate the properties of this measure, we computed the Γ values for 70 object groups (20 shapes in each group) in the MPEG-7 shape database and performed grouping on the values. With relatively high Γ values, identical groups had visually similar shapes. On the other hand, the identical groups with low Γ values had visually different shapes. However, the aspect of topological similarity of the shapes also warrants consideration. The shapes of statistically different groups exhibited significant visual difference from each other. Also, in order to show that the Γ can have a wide variety of applicability when properly used with other variables, we showed that the finger gestures in the (Γ, Z) space are successfully classified. Here, the Z means a correlation coefficient value between entropy profiles for gesture shapes. As shown in the applications, Γ has a strong advantage over conventional geometric measures in that it captures the geometrical and topological properties of a shape together. If we could define the BLS entropy for color, Γ could be used to characterize images expressed in RGB. We briefly discussed the problems to be solved before the applicability of Γ can be expanded to various fields.