STGauntlet: Recognizing Hand Gestures over Multiple Hand-Worn Motion Sensors

Author(s):  
Mengyuan Ran ◽  
Shanshan Wang ◽  
Jun Liao ◽  
Yuhao Zhang ◽  
Li Liu
Keyword(s):  
2013 ◽  
Author(s):  
Margaux Larre-Perez ◽  
Pierre Jacob ◽  
Therese Collins
Keyword(s):  

Author(s):  
Avtandil kyzy Ya

Abstract: This paper highlights similarities and different features of the category of kinesics “hand gestures”, its frequency usage and acceptance by different individuals in two different cultures. This study shows its similarities, differences and importance of the gestures, for people in both cultures. Consequently, kinesics study was mentioned as a main part of body language. As indicated in the article, the study kinesics was not presented in the Kyrgyz culture well enough, though Kyrgyz people use hand gestures a lot in their everyday life. The research paper begins with the common definition of hand gestures as a part of body language, several handshake categories like: the finger squeeze, the limp fish, the two-handed handshake were explained by several statements in the English and Kyrgyz languages. Furthermore, this article includes definitions and some idioms containing hand, shake, squeeze according to the Oxford and Academic Dictionary to show readers the figurative meanings of these common words. The current study was based on the books of writers Allan and Barbara Pease “The definite book of body language” 2004, Romana Lefevre “Rude hand gestures of the world”2011 etc. Key words: kinesics, body language, gestures, acoustics, applause, paralanguage, non-verbal communication, finger squeeze, perceptions, facial expressions. Аннотация. Бул макалада вербалдык эмес сүйлѳшүүнүн бѳлүгү болуп эсептелген “колдордун жандоо кыймылы”, алардын эки башка маданиятта колдонулушу, айырмачылыгы жана окшош жактары каралган. Макаланын максаты болуп “колдордун жандоо кыймылынын” мааниси, айырмасы жана эки маданиятта колдонулушу эсептелет. Ошону менен бирге, вербалдык эмес сүйлѳшүүнүн бѳлүгү болуп эсептелген “кинесика” илими каралган. Берилген макалада кѳрсѳтүлгѳндѳй, “кинесика” илими кыргыз маданиятында толугу менен изилденген эмес, ошого карабастан “кинесика” илиминин бѳлүгү болуп эсептелген “колдордун жандоо кыймылы” кыргыз элинин маданиятында кѳп колдонулат. Андан тышкары, “колдордун жандоо кыймылынын” бир нече түрү, англис жана кыргыз тилдеринде ма- селен аркылуу берилген.Тѳмѳнкү изилдѳѳ ишин жазууда чет элдик жазуучулардын эмгектери колдонулду. Түйүндүү сѳздѳр: кинесика, жандоо кыймылы, акустика,кол чабуулар, паралингвистика, вербалдык эмес баарлашуу,кол кысуу,кабыл алуу сезими. Аннотация. В данной статье рассматриваются сходства и различия “жестикуляции” и частота ее использования, в американской и кыргызской культурах. Следовательно, здесь было упомянуто понятие “кинесика” как основная часть языка тела. Как указано в статье, “кинесика” не была представлена в кыргызской культуре достаточно хорошо, хотя кыргызский народ часто использует жестикуляцию в повседневной жизни. Исследовательская работа начинается с общего определения “жестикуляции” как части языка тела и несколько категорий жестикуляции, таких как: сжатие пальца, слабое рукопожатие, рукопожатие двумя руками, были объяснены несколькими примерами на английском и кыргызском языках. Кроме того, эта статья включает определения слов “рука”, “рукопожатие”, “сжатие” и некоторые идиомы, содержащие данных слов согласно Оксфордскому и Академическому словарю, чтобы показать читателям их образное значение. Данное исследование было основано на книгах писателей Аллана и Барбары Пиз «Определенная книга языка тела» 2004 года, Романа Лефевра «Грубые жестикуляции мира» 2011 года и т.д. Ключевые слова: кинесика, язык жестов, жесты, акустика, аплодисменты, паралингвистика, невербальная коммуникация, сжатие пальца, чувство восприятия, выражение лиц.


2020 ◽  
Author(s):  
Vijayaraghavan D ◽  
Harini K R ◽  
Vithya Ganeshan ◽  
Sushmidha S
Keyword(s):  

2020 ◽  
Author(s):  
Nirmala J S ◽  
Ajeet Kumar ◽  
Adith Jose E A ◽  
Kapil Kumar ◽  
Abhishek R Malvadkar

Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.


Author(s):  
Giuseppe Placidi ◽  
Danilo Avola ◽  
Luigi Cinque ◽  
Matteo Polsinelli ◽  
Eleni Theodoridou ◽  
...  

AbstractVirtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.


Author(s):  
Kirti Sundar Sahu ◽  
Arlene Oetomo ◽  
Niloofar Jalali ◽  
Plinio P. Morita

The World Health Organization declared the coronavirus outbreak as a pandemic on March 11, 2020. To inhibit the spread of COVID-19, governments around the globe, including Canada, have implemented physical distancing and lockdown measures, including a work-from-home policy. Canada in 2020 has developed a 24-Hour Movement Guideline for all ages laying guidance on the ideal amount of physical activity, sedentary behaviour, and sleep (PASS) for an individual in a day. The purpose of this study was to investigate changes on the household and population-level in lifestyle behaviours (PASS) and time spent indoors at the household level, following the implementation of physical distancing protocols and stay-at-home guidelines. For this study, we used 2019 and 2020 data from ecobee, a Canadian smart Wi-Fi thermostat company, through the Donate Your Data (DYD) program. Using motion sensors data, we quantified the amount of sleep by using the absence of movement, and similarly, increased sensor activation to show a longer duration of household occupancy. The key findings of this study were; during the COVID-19 pandemic, overall household-level activity increased significantly compared to pre-pandemic times, there was no significant difference between household-level behaviours between weekdays and weekends during the pandemic, average sleep duration has not changed, but the pattern of sleep behaviour significantly changed, specifically, bedtime and wake up time delayed, indoor time spent has been increased and outdoor time significantly reduced. Our data analysis shows the feasibility of using big data to monitor the impact of the COVID-19 pandemic on the household and population-level behaviours and patterns of change.


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