Smart Hand Research Articles

Works for Me: Personalizing Skilled Trade Worker Training via Smart Hand Tools

ABSTRACTThis paper explores an approach for applying Artificial Intelligence (AI) to co‐design smart hand tools to personalize learning for future skilled trade workers in workforce training programs. The purpose of this research is to better understand the perspectives of workers in the skilled trades and to respond with co‐designed socio‐technical interventions that empower workers. The research benefits from a collaboration between The University of Texas at Austin, the City of Austin, and Austin Community College (ACC) and incorporates insights from welding instructors and students, as well as skilled trade workers and supervisors. Social science findings derived from semi‐structured interviews inform tool design implemented by an interdisciplinary research team. The participatory design approach has resulted in two prototypes: a welding simulator that uses Augmented Reality (AR) and an AI‐enabled (smart) rotary tool. This paper has implications for workforce development to address skilled worker shortages. Additionally, it contributes to ongoing research into AI and skilled trade work which is understudied compared to AI and knowledge work.

Correlation between handgrip strength and air trapping in patients with stable chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) often presents with dyspnea resulting from the condition of air trapping, assessed by lung volume measurement studies. This study aimed to investigate the relationship between handgrip strength (HGS) and air trapping in COPD patients. Cross-sectional research was conducted in COPD patients at Thammasat University Hospital, Thailand between May 2022 and December 2023. HGS was assessed using the Jamar® Smart Hand Dynamometer, and air trapping was measured using a body plethysmograph. Air trapping was defined as a ratio of residual volume (RV) to total lung capacity (TLC) greater than 40%. Receiver operator characteristic (ROC) curves, sensitivity, and specificity values were calculated to determine the optimal cutoff value of HGS for predicting air trapping. A total of 72 patients (90.3% male) were included, with an average age of 72.4±9.7 years. The body mass index was 23.5±4.3 kg/m2. The smoking history was 23.2±14.8 pack-years. Common comorbidities included hypertension (36.1%) and diabetes (22.2%). Post-bronchodilator forced expiratory volume in 1 second (FEV1) was 72.1%±21.2%. Air trapping was found in 55.6%. A negative correlation was found between HGS and RV/TLC (R=-0.399, P=0.001). The best cutoff value for HGS to predict air trapping was 28.3 kg, with 71.9% sensitivity and 65.0% specificity. The area under the ROC curve for identifying air trapping was 0.681 (95% CI: 0.554 to 0.808, P=0.009). Air trapping is common in COPD patients, and HGS is significantly correlated with air trapping. Thus, HGS may serve as an alternative tool for assessing air trapping.

Smart Hand Gloves for Deaf and Dumb People

Abstract: Approximately 2.78% of the population in our nation is nonverbal. They communicate with people by using their hands and facial gestures. As is well known, those who are silent are mute and those who are not are unaware of sign language, which is a way for silent individuals to communicate. Thus, we have put forth a brand-new method known as “The SPEAKING SYSTEM TO THE MUTE PEOPLE”. Thus, this system will be helpful in solving the issue. The accelerometer sensors form the foundation of the system. Mute people believe that every deed has a purpose. The accelerometer sensors accelerate with each action and provide a signal to the Arduino. The speech signal is created by the Arduino by synchronizing its motion with that of the database. The Guide Me app is being used as the system's output. Since gestures are consistent with people's communication habits, a lot of study has been done on gesture detection using a vision-based method.

Development of Smart Hand Gloves to solve Parkinson’s Disease

Development of Smart Hand Gloves to solve Parkinson’s Disease

Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs

<p>Control strategies of smart hand prosthesis-based myoelectric signals in<br />recent years don't provide the patients with the sensation of biological<br />control of prostheses hand fingers. Therefore, in current work<br />hyperparameters optimization in machine learning algorithm and hand<br />gesture recognition techniques were applied to the myoelectric signal-based<br />on residual muscles contraction of the amputees corresponding to intact<br />forearm limb movement to improve their biological control. In this paper,<br />myoelectric signals are extracted using the MYO armband to recognize ten<br />gestures from ten volunteers (healthy and transradial amputation) on the<br />forearm, thereafter the noise of myoelectric signals using a notch filter (NF)<br />is removed. The proposed classification system involved two machine<br />learning algorithms: (1) the decision tree (DT), tri-layered neural network<br />(TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and<br />ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning<br />classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography<br />(ODT) and ommatidia detecting algorithm (ODA). The experimental results<br />of classifiers comparison pointed out an algorithm that outperformed with<br />high accuracy is OEBT closely followed by OKNN achieves an accuracy of<br />97.8% and 97.1% for intact forearm limb, while for transradial amputation<br />with an accuracy of 91.9% and 91.4%, respectively.</p>

Integrated Visitor Management System with Smart Hand Sanitizer based on IoT Approach

Establishing visitor management system is necessary to manage the total number of visitors especially in pandemic when the protocol of social distancing is applied. The purpose of this research is to design a visitor management system integrated with an automatic Internet of Things (IoT)-based hand sanitizer using Arduino. This research contributes to answering the two challenges which are managing the total number of visitors and providing protection against bacteria or viruses. The improvement of this system compared to the existing similar approach is the automatic hand sanitizers could be installed at two entrances and exits which can simultaneously limit the number of visitors who will be monitored through the IoT platform. This system is designed with Arduino Uno R3 components as a microcontroller, ultrasonic sensors which will be installed at two entrances and exits, and automatic hand sanitizer using a water pump. For remote monitoring use the IoT platform with the ESP8266 ESP-01 Wi-Fi module as a link. From the results of testing this system, the results of testing the correct hand distance when using a hand sanitizer are from 3 cm to 9 cm and the right amount of hand sanitizer liquid volume is approximately 1 ml for a duration of 250 milliseconds. The system design has been successful for visitor management and the hand sanitizer works well. The Thingspeak webserver can receive visitor data and send it to the MIT app as a display for remote monitoring. Hopefully, this system could support the situation which needs to manage the restriction of total number visitors and hygiene practice in a more efficient way.

Development of a hand classification system for smart hand wearables

Research purpose: This study aimed to lay a research foundation for smart hand wearable design by classifying the right-hand data of 4545 adults aged 20 to 69. Further, to increase the practical applicability of the hand classification system, a hand type discrimination method and regression equations for the hand dimensions of each type were presented. Methods: This study statistically analyzed eighth Size Korea data with IBM SPSS Ver.26.0. Cluster analysis was performed to classify both finger length and circumference type. Discriminant analysis was conducted, yielding discriminant functions to aid potential smart hand wearable wearers in self-diagnosing their hand types. Linear regression analysis yielded regression equations for the detailed finger dimensions for the pattern-making of smart hand wearables. Results: The finger length type was categorized into four types: the Uphill type, Downhill type, Mountain type, and Horizon type for both men’s and women’s hands. The finger circumference type was categorized into two types, the Cone and Cylinder types, for both men’s and women’s hands. The discriminant function showed a mean accuracy rate of 89.9% and the regression equations a mean explanatory power of 72.9%. Conclusion: The hand classification system proposed in this study aimed to improve the fingertip fit of smart hand wearable products by analyzing the configuration of motion tracking gloves or haptic gloves. In addition, considering the practical applicability for both wearers and designers of smart hand wearables, a discrimination method of finger types for wearers’ self-diagnosis and regression functions of finger dimensions for designers’ pattern making were provided.

Feeling the beat: a smart hand exoskeleton for learning to play musical instruments.

Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by 'feeling' the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.

A theoretical model for two‐way bending behavior of bilayer shape memory polymer plate

AbstractThis study proposes a simple theoretical model based on the principles of phase transition and strain energy balance theories (during the thermo‐mechanical cycle process) to predict the two‐way bending behavior of a bilayer plate composed of two types of shape memory polymers (SMPs) with different glass transition temperatures under axial strains from 5% to 20%. The two‐way bending behavior observed from the theoretical results was verified via comparison with the finite‐element method (FEM) simulation based on the Generalized Maxwell model, wherein good agreement was demonstrated. The maximum errors for the critical bending angle and deflection were 6.82% and 5.36%, respectively. In addition, the two‐way bending behavior of a bilayer plate, including SMP and elastic polymer layers, was also predicted by the theoretical model, where good agreement was observed. The maximum errors for the critical bending angle and deflection were 15.20% and 12.82%, respectively. The theoretical model can predict the thermo‐mechanical behavior of smart actuators and help design the geometry configuration based on bilayer SMP plates in the future, such as smart hands or grippers.

Applications of Internet of Things in university libraries of Pakistan: An empirical investigation

This study aims to investigate the applications of the Internet of Things (IoTs) and highlights the challenges faced to implement IoTs in Pakistani university libraries. This study opted exploratory sequential mixed-methods approach and was completed in two phases. In the first phase, purposefully selected university librarians were interviewed. In the second phase, quantitative data were collected from 122 university librarians across Pakistan. This study found that Pakistani university libraries implemented IoTs-based appliances including smart air conditioners, automatic fire alarms, smart hand sanitizer machines, and smart security doors. Moreover, the university libraries were using limited IoT-based services that are auto notification of check-out, check-in the reading materials, self-check-out and self-check-in system, recognition of user's card, and the use of Radio Frequency Identification (RFID) tags for security perspective. The major challenges to implementing IoTs applications were found lack of a highly networked and integrated environment, budget problems, unavailability of policy and strategic plan, and the lack of technical personnel. This novel study explains the extent of IoTs applications and the challenges to adopt and implement IoTs in Pakistani university libraries. The research findings would be useful for library and university administrators who are interested to implement IoTs applications in their libraries.

Smart Hand Sanitisers in the Workplace: A Survey of Attitudes towards an Internet of Things Technology.

An online survey was circulated to employees from a wide range of organisations to gauge attitudes towards the idea of using smart hand sanitisers in the workplace. The sanitisers are capable of real-time monitoring and providing feedback that varies according to the hand hygiene behaviour of users. In certain circumstances, the sanitisers can monitor individuals, making it possible to identify workers whose hand hygiene falls below a certain standard. The survey was circulated between July and August 2021 during the COVID-19 pandemic. Data gathered from 314 respondents indicated support for some features of the technology, but also indicated concern about invasions of privacy and the possibility of coercion. Attitudes towards the possible implementation of the technology varied significantly according to certain characteristics of the sample, but particularly with age. Respondents above the median age were more likely to support the use of data in ways that could facilitate the promotion and enforcement of hand hygiene practices.

Smart Hand Sanitizer Dispenser

In this corona period hand sanitizer is an essential thing. Because it can kill the Covid -19 virus. but use the of normal sanitizer bottle become very danger. When an infected person presses the bottle trigger, The virus may spread from this hand sanitizer bottle. We can solve this by using Automatic hand sanitizer bottle. Automatic means, no need to trigger with our hand. Just place your hand near the bottle. the bottle will automatically trigger. Interfacing of Servo motor and interfacing of Ultrasonic sensor is explained in my previous articles. Links are given in the end of this article. We use an Ultrasonic distance sensor, Servo motor and Arduino board. here I am using Arduino Uno. You can also use any other microcontroller. When we place our hand in front of the distance sensor, it will help to the Arduino to measure the distance from the sensor to object (here the hand). if the object in the desired range, Arduino will write the servo to 180. Servo motor is mounded on the hand sanitizer bottle. And the trigger of bottle is connected to servo by a thread. When servo motor rotates, the trigger will press.

“Sign in to Speech” Smart Hand Glove for Especially Abled

More than 9 Crore people on the earth are facing the issue of dumbness. The communication between a dumb and normal person i.e. man or woman poses to be a crucial downside in comparison to verbal exchange among blind and historically visible human beings. The human who is facing the blindness can talk smoothly through normal human or regional language while the person facing the issue of dumbness have their own manual-visible language known as Sign or gesture using by the figures and hand language. Language is likewise a non-verbal Gesture that`s located amongst deaf groups at durations the planet. The languages have not been given a regular beginning and thence difficult to interpret. This challenge enables deaf and dumb people to which they speak with the not unusual place human beings easily. Sign languages are utilized by mute human beings as a medium of verbal exchange. Sign languages are used to deliver the mind with symbols, items, etc. They additionally deliver an aggregate of phrases and symbols (i.e. gestures). The gesture is a non-verbal shape of verbal exchange. Sign language reputation structures are used to transform signal language into textual content or speech to permit verbal exchange with those who do now no longer recognize those gestures. Usually, the focal point of those structures is to apprehend hand configurations along with position, orientation, and actions A the person facing the issue of dumbness can communicate with the normal people with the help of the device or gloves which can translate the hand or fingers gestures to sensible speech with the help of the converse used in the device. A gesture in accomplice diploma extraordinarily language is likewise a sure motion of the arms with a selected type created out of them. A posture alternatively is likewise a static form of the hand to cause an emblem. The main aim of these device is to translate the hand or figure movements of the deaf and dumb people to the language which can be understand by the normal people such as textual content and sensible or understandable voice. The interpreter uses a Gloves for the capturing the hand movements which consist of flex sensors, tool sensors which can be compared with the predefined hand movement’s data saved as the input signed language.

Effect of transcranial direct current stimulation combined with asmart hand joint training device on hand dysfunction in patients with early stroke.

The aim of this study was to investigate the effect of transcranial direct current stimulation (tDCS) combined with a smart hand joint training device on hand dysfunction in patients with early stroke. This study was a randomized controlled trial, which was conducted in the neurology department in our hospital. From March 2019 to January 2021, 60 patients diagnosed with early stroke hand dysfunction were selected. A random number table method was used to divide patients equally into control group (smart hand joint training device group) and intervention group (tDCS and smart hand joint training device group). Before and after treatment, Brunnstrom six-level staging, Fugl-Meyer motor function score (wrist hand part), hemiplegic finger function examination, and hemiplegic hand function classification evaluation were applied in both groups. Main outcome measures were Brunnstrom motor function staging (hand part), functional evaluation of hemiplegic fingers, and Fugl-Meyer motor function score (wrist hand part). After treatment, compared with control group, the results in intervention group of Brunnstrom six-level staging and hemiplegic hand function classification evaluation showed obvious improvement (p = 0.000), and the result of hemiplegic fingers' functional evaluation also improved (p = 0.026). After treatment, Fugl-Meyer motor function scores were 6.73 ±6.65 (control group) and 9.8 ±6.66 (intervention group). Slight tDCS-related adverse events occurred in one patient (3.33%) in intervention group. None in either group discontinued treatment. Both the smart hand joint training device alone and tDCS combined with the smart hand joint training device can improve hand function of patients with early stroke to varying degrees, but the treatment effect of tDCS combined with the smart hand joint training device is more significant.

Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications

Tactile sensing is a crucial perception mode for robots and human amputees in need of controlling a prosthetic device. Today, robotic and prosthetic systems are still missing the important feature of accurate tactile sensing. This lack is mainly due to the fact that the existing tactile technologies have limited spatial and temporal resolution and are either expensive or not scalable. In this article, we present the design and implementation of a hardware–software embedded system called SmartHand. It is specifically designed to enable the acquisition and real-time processing of high-resolution tactile information from a hand-shaped multisensor array for prosthetic and robotic applications. During data collection, our system can deliver a high throughput of 100 frames per second, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.7\times $ </tex-math></inline-formula> higher than previous related work. This has allowed the collection of a new tactile dataset consisting of 340 000 frames while interacting with 16 objects from everyday life during five different sessions. Together with the empty hand, the dataset presents a total of 17 classes. We propose a compact yet accurate convolutional neural network that requires one order of magnitude less memory and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15.6\times $ </tex-math></inline-formula> fewer computations compared with related work without degrading classification accuracy. The top-1 and top-3 cross-validation accuracies on the collected dataset are, respectively, 98.86% and 99.83%. We further analyze the intersession variability and obtain the best top-3 leave-one-out-validation accuracy of 77.84%. We deploy the trained model on a high-performance ARM Cortex-M7 microcontroller achieving an inference time of only 100 ms minimizing the response latency. The overall measured power consumption is 505 mW. Finally, we fabricate a new control sensor and perform additional experiments to provide analyses on sensor degradation and slip detection. This work is a step forward in giving robotic and prosthetic devices a sense of touch by demonstrating the practicality of a smart embedded system that uses a scalable tactile sensor with embedded tiny machine learning.

Psychological and Psychophysiological Responses to Challenge Variations for Virtual Hand Training in Game-based Smart Rehabilitation System

Background: Maintaining motivation is one of the most important characteristics of rehabilitation strategies for successful treatment. Understanding the underlying mechanism of mental state is helpful for developing new therapeutic methods based on virtual reality and serious game technologies. Objective: The present study aims to develop a cost-benefit game-based hand rehabilitation system and assess the influence on the psychological state of subjects when they interact with a virtual reality environment in different task difficulty levels. Methods: First, we introduced a low-cost smart hand rehabilitation system based on the Leap Motion tracker; then, the experimental study was performed with 20 healthy participants. Their mental states were evoked using interaction with two separate games in four different difficulty levels. Three measures from the SAM self-reported test described as a psychological response to this condition, and also four features were extracted from Photoplethysmogram signal in order to quantify psychophysiological responses of Autonomic Nervous System. Results: Comparison of the different difficulty levels revealed significant changes in arousal and dominance correspond to under challenging and over the challenging condition, respectively. The results of psychophysiological feature analysis showed significant differences only for the standard deviation of intervals between consecutive heartbeats. Conclusion: The developed system is a low-cost smart solution that can be useful for upper limb neurological rehabilitation. Regulating difficulty parameters of the implemented game can be used to influence the motivation of users through rehabilitation procedures. It seems Photoplethysmogram is an appropriate psychophysiological indicator of mental states, but further studies are required.

Increasing Public Interest on Reading through Physical and Digital Library in Karangmojo Village

The current Covid-19 pandemic obliges children to study online. It requires children to use gadgets either smart hand phone or laptop. It makes a big increasing gadgets utilization. A lot of children spend their time just playing 'online games'. Adults who do activities or work from home on average spend their time using gadgets. With this phenomenon, people's interest in reading has decreased. One way to increase people's interest in reading is by making the Karangmojo Village Community Reading Corner. In the current era of globalization, this Community Reading Corner not only provides a physical library but also provides a digital library that can be accessed via the web by the people of Karangmojo Village.&#x0D;

Smart Hand Glove for Hearing and Speech Impaired

Abstract: With the advancement of technology, we can implement a variety of ideas to serve mankind in numerous ways. Inspired by this, we have developed a smart hand glove system which will be able to help the people having hearing and speech disabilities. In the world of sound, for those without it, sign language is a powerful tool to make their voices heard. The American Sign Language (ASL) is the most frequently used sign language in the world, with some differences depending on the nation. We created a wearable wireless gesture decoder module in this project that can transform the basic set of ASL motions into alphabets and sentences. Our project utilizes a glove that houses a series of flex sensors on the metacarpal and interphalange joints of the fingers to detect the bending of fingers, through piezoresistive (change in electrical resistance when the semiconductor or metal is subjected to mechanical strain) effect. The glove is attached with an accelerometer as well, that helps to detect the hand movements. Simple classification algorithms from machine learning are then applied to translate the gestures into alphabets or words. Keywords: Arduino; MPU6050; Flex sensor; Machine learning; SVM classifier

Smart hand gestures recognition using K-NN based algorithm for video annotation purposes

&lt;span&gt;Sign languages are the most basic and natural form of languages which were used even before the evolution of spoken languages. These sign languages were developed using various sign "gestures" that are made using hand palm. Such gestures are called "hand gestures". Hand gestures are being widely used as an international assistive communication method for deaf people and many life aspects such as sports, traffic control and religious acts. However, the meanings of hand gestures vary among different civilization cultures. Therefore, because of the importance of understanding the meanings of hand gestures, this study presents a procedure whichcan translate such gestures into an annotated explanation. The proposed system implements image and video processing which are recently conceived as one of the most important technologies. The system initially, analyzes a classroom video as an input, and then extracts the vocabulary of twenty gestures. Various methods have been applied sequentially, namely: motion detection, RGB to HSV conversion, and noise removing using labeling algorithms. The extraction of hand parameters is determined by a K-NN algorithm to eventually determine the hand gesture and, hence showing their meanings. To estimate the performance of the proposed method, an experiment using a hand gesture database is performed. The results showed that the suggested method has an average recognition rate of 97%. &lt;/span&gt;

Association of Physical Activity, Muscle Mass, Strength, and Function to Quality of Life in Older Adults

Physical inactivity and loss of muscle mass, strength, and function are associated with negative outcomes including disability and a decline in health-related quality of life (HRQoL) among older adults. Older adults living in continuing care retirement communities (CCRCs) are at greater risk for declining physical activity and muscle outcomes compared to community-dwelling older adults. Few researchers studying the association of muscle and physical activity have examined the distinction between physical and mental HRQoL. Understanding the differential association of physical and mental HRQoL to physical activity and muscle outcomes can inform the development of useful interventions. The aim of this study was to examine the relationships between physical activity, muscle mass, strength, function and physical and mental HRQoL. Using a descriptive, correlational design, 105 older adults living in CCRCs were recruited. Light physical activity (LPA), moderate physical activity (MPA), sedentary behavior, and steps per day were assessed using ActiGraph GT3X. Appendicular skeletal muscle mass (ASMM) was assessed with bioelectrical impedance spectroscopy, handgrip strength with JAMAR Smart Hand Dynamometer, muscle function with the Short Physical Performance Battery (SPPB) test, and physical and mental HRQoL with the SF-36 questionnaire. The mean age of participants was 83 (SD=7.4). Using multiple regression models adjusted for sex and age, steps per day and SPPB score explained 38.4 % of the variance in physical HRQoL. Handgrip strength explained 8 % of the variance in mental HRQoL. These findings suggest that QoL improvement programs should include components to improve physical activity, muscle strength and function.