Journal articles: 'Leap motion sensor'

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Fernando, Redondo, Supria Supria, and Muhamad Nasir. "AplikasiMarawis Digital Menggunakan Sensor Leap Motion." INOVTEK Polbeng - Seri Informatika 2, no. 2 (2017): 141. http://dx.doi.org/10.35314/isi.v2i2.201.

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Intisari - Marawis adalah salah satu jenis "band tepuk” dengan perkusi sebagai alat musik utamanya. Musik ini merupakan kolaborasi antara kesenian Timur Tengah dan Betawi, dan memiliki unsur keagamaan yang kental. Itu tercermin dari berbagai lirik lagu yang dibawakan yang merupakan pujian dan kecintaan kepada Sang Pencipta. Pada saat ini memainkan alat musik marawis masih dengan cara manual dengan cara memukul alat musik marawis tersebut dengan sekuat mungkin dan membutuhkan banyak tenaga, dan lama kelamaan tangan akan terasa sakit. Untuk mengatasi masalah tersebut maka di usulkan Aplikasi Marawis Digital Menggunakan Sensor Leap Motion. Aplikasi dibuat dengan Leap Motion untuk mendeteksi titik-titk koordinat tangan pengguna. Untuk menghasilkan dua suara marawis yang berbeda maka di buat mapping area untuk menentukan suara 1 dan suara 2. Aplikasi ini dapat menjadi alternative untuk memainkan alat musik marawis. Dari hasil uji coba yang dilakukan menunjukkan bahwa aplikasi ini mampu menggantikan penggunaan tenaga manusia ke teknik digital. Kata Kunci : Leap Motion, Aplikasi Desktop, Marawis

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Sousa, L., J. Monteiro, P. J. S. Cardoso, and J. M. F. Rodrigues. "Natural user interface based on gestures recognition using Leap Motion sensor." Dos Algarves: a Multidisciplinary e-journal 26, no. 1 (2015): 107–21. http://dx.doi.org/10.18089/damej.2015.26.1.6.

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Jadhav, Siddhant A., Rohan R. Yashod, Aditya P. Sabne, Yash K. Rokde, and Chandrabhan R. Ghuge. "Leap Motion Sensor Technology Based Robo-Control System." International Journal of Computer Science and Mobile Computing 9, no. 12 (2020): 50–58. http://dx.doi.org/10.47760/ijcsmc.2020.v09i12.007.

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Jadhav, Siddhant A., Rohan R. Yashod, Aditya P. Sabne, Yash K. Rokde, and Chandrabhan R. Ghuge. "Leap Motion Sensor Technology Based Robo-Control System." International Journal of Computer Science and Mobile Computing 9, no. 12 (2020): 50–58. http://dx.doi.org/10.47760/ijcsmc.2020.v09i12.007.

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Nizamis, Kostas, Noortje Rijken, Ana Mendes, Mariska Janssen, Arjen Bergsma, and Bart Koopman. "A Novel Setup and Protocol to Measure the Range of Motion of the Wrist and the Hand." Sensors 18, no. 10 (2018): 3230. http://dx.doi.org/10.3390/s18103230.

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The human hand is important for the performance of activities of daily living which are directly related to quality of life. Various conditions, such as Duchenne muscular dystrophy (DMD) can affect the function of the human hand and wrist. The ability to assess the impairment in the hand and the wrist by measuring the range of motion (ROM), is essential for the development of effective rehabilitation protocols. Currently the clinical standard is the goniometer. In this study we explore the feasibility and reliability of an optical sensor (Leap motion sensor) in measuring active hand/wrist ROM. We measured the hand/wrist ROM of 20 healthy adults with the goniometer and the Leap motion sensor, in order to check the agreement between the two methods and additionally, we performed a test-retest of the Leap motion sensor with 12 of them, to assess its reliability. The results suggest low agreement between the goniometer and the leap motion sensor, yet showing a large decrease in measurement time and high reliability when using the later. Despite the low agreement between the two methods, we believe that the Leap motion sensor shows potential to contribute to the development of hand rehabilitation protocols and be used with patients in a clinical setting.

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Supria, Supria, Depandi Enda, and Muhamad Nasir. "Pengenalan Bentuk Tangan secara Real Time Menggunakan Leap Motion dan K-Nearest Neighbors sebagai Sistem Kendali Robot Beroda." CESS (Journal of Computer Engineering, System and Science) 5, no. 2 (2020): 178. http://dx.doi.org/10.24114/cess.v5i2.18141.

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Sistem kendali robot saat ini telah banyak dibuat dengan menggunakan berbagai metode seperti sensor accelerometer, sensor suara, leap motion. Pada penelitian ini diusulkan pengenalan bentuk tangan secara real time menggunakan leap motion dan K-Nearest Neighbors (KNN) sebagai sistem kendali robot beroda. Leap motion digunakan untuk mendeteksi titik koordinat posisi tangan pada pandangan leap motion. pembentukan fitur dilakukan dengan mengukur jarak euclidean distance antara palm position dengan finger tip. KNN digunakan untuk menentukan kelas pada data testing. Uji coba dilakukan menggunakan tangan penulis dengan 5 jenis instruksi yaitu maju, mundur, stop, belok kanan, belok kiri dan setiap instruksi di ujicoba sebanyak 20 kali percobaan. Dari hasil ujicoba yang dilakukan menunjukkan bahwa sistem yang diusulkan memiliki tingkat akurasi pengenalan 94%.

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Behera, Santosh Kumar, Debi Prosad Dogra, and Partha Pratim Roy. "Analysis of 3D signatures recorded using leap motion sensor." Multimedia Tools and Applications 77, no. 11 (2017): 14029–54. http://dx.doi.org/10.1007/s11042-017-5011-4.

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Nugraha, Syaiful, Darlis Heru Murti, and Wijayanti Nurul Khotimah. "Penggunaan Dual Sensor Leap Motion Controller untuk Sistem Isyarat Bahasa Indonesia (SIBI)." INFORMAL: Informatics Journal 4, no. 2 (2019): 48. http://dx.doi.org/10.19184/isj.v4i2.12634.

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Bahasa isyarat adalah bahasa yang dibentuk dengan kombinasi gerakan jari, tangan, tubuh serta ekspresi wajah yang digunakan oleh para penyandang disabilitas seperti tunarungu dan tunawicara. Salah satu pengenalan bahasa isyarat tersebut adalah pengenalan dengan menggunakan teknologi sensor Leap Motion Controller (LMC). Akan tetapi pada penerapannya, sensor LMC tidak selalu dapat mengenali semua jari dengan baik. Hal ini dikarenakan pembentukan dari bahasa isyarat yang saling bersentuhan antar jari, jari yang dilipat, tersembunyi ataupun terhalang oleh jari lainnya. Sehingga pada penelitian ini diusulkan penggunaan dual sensor LMC yang bertujuan untuk mencegah dan meminimalisir adanya kesalahan dalam mengenal bahasa isyarat dari jari yang dibentuk. Tahapan proses yang dilakukan adalah mendesain tata letak sensor LMC dan mengkombinasikan data fitur dari masing-masing sensor LMC baik yang bersifat statis dan dinamis yang kemudian diklasifikasikan menggunakan metode L-GCNN. Dari hasil pengujian yang dilakukan, penggunaan dual sensor LMC ini dapat mengenal isyarat SIBI yang diberikan. Skema yang terbaik adalah dengan mengacu pada tata letak multi sensor yang saling berhadapan membentuk sudut 120 derajat dengan tingkat akurasi sebesar 86,42%, dengan rata-rata akurasi dari keseluruhan skenario sebesar 84,36%. Adanya desain tata letak sensor LMC dan kombinasi penggabungan data fitur masing-masing sensor dapat meningkatkan akurasi pengenalan dengan rata-rata peningkatan mencapai 5,35%.

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Kumar, Pradeep, Rajkumar Saini, Partha Pratim Roy, and Debi Prosad Dogra. "Study of Text Segmentation and Recognition Using Leap Motion Sensor." IEEE Sensors Journal 17, no. 5 (2017): 1293–301. http://dx.doi.org/10.1109/jsen.2016.2643165.

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Sun, Xinyao, Anup Basu, and Irene Cheng. "Multi-Sensor Motion Fusion Using Deep Neural Network Learning." International Journal of Multimedia Data Engineering and Management 8, no. 4 (2017): 1–18. http://dx.doi.org/10.4018/ijmdem.2017100101.

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Hand pose estimation for a continuous sequence has been an important topic not only in computer vision but also human-computer-interaction. Exploring the feasibility to use hand gestures to replace input devices, e.g., mouse, keyboard, joy-stick and touch screen, has attracted increasing attention from academic and industrial researchers. The fast advancement of hand pose estimation techniques is complemented by the rapid development of smart sensors technology such as Kinect and Leap. We introduce a hand pose estimation multi-sensor system. Two tracking models are proposed based on Deep (Recurrent) Neural Network (DRNN) architecture. Data captured from different sensors are analyzed and fused to produce an optimal hand pose sequence. Experimental results show that our models outperform previous methods with better accuracy, meeting real-time application requirement. Performance comparisons between DNN and DRNN, spatial and spatial-temporal features, and single- and dual- sensors, are also presented.

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Galván-Ruiz, Jesús, Carlos M. Travieso-González, Acaymo Tejera-Fettmilch, Alejandro Pinan-Roescher, Luis Esteban-Hernández, and Luis Domínguez-Quintana. "Perspective and Evolution of Gesture Recognition for Sign Language: A Review." Sensors 20, no. 12 (2020): 3571. http://dx.doi.org/10.3390/s20123571.

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This review analyses the different gesture recognition systems through a timeline, showing the different types of technology, and specifying which are the most important features and their achieved recognition rates. At the end of the review, Leap Motion sensor possibilities are described in detail, in order to consider its application on the field of sign language. This device has many positive characteristics that make it a good option for sign language. One of the most important conclusions is the ability of the Leap Motion sensor to provide 3D information from the hands for due identification.

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Rusanu, O. A., L. Cristea, M. C. Luculescu, P. A. Cotfas, and D. T. Cotfas. "Virtual robot arm controlled by hand gestures via Leap Motion Sensor." IOP Conference Series: Materials Science and Engineering 514 (June 26, 2019): 012021. http://dx.doi.org/10.1088/1757-899x/514/1/012021.

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Marin, Giulio, Fabio Dominio, and Pietro Zanuttigh. "Hand gesture recognition with jointly calibrated Leap Motion and depth sensor." Multimedia Tools and Applications 75, no. 22 (2015): 14991–5015. http://dx.doi.org/10.1007/s11042-015-2451-6.

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Rizal, Mochammad, Uung Ungkawa, and Marisa Premitasari. "EFISIENSI PENGGUNAAN AIR PADA SISTEM MOBIL WUDHU." Jurnal Ilmiah Teknologi Infomasi Terapan 7, no. 1 (2021): 1–8. http://dx.doi.org/10.33197/jitter.vol7.iss1.2020.444.

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Wudhu merupakan kegiatan yang dilakukan setiap umat muslim ketika hendak melakukan kegiatan shalat. Mobil wudhu adalah suatu mobil yang terdapat saluran air yang diperuntukan untuk berwudhu ketika jumlah jamaah di suatu masjid membeludak atau terlalu banyak. Mobil air wudhu bisa menampung 4000 liter air. Proses berwudhu secara manual yang dilakukan memiliki kelemahan yaitu boros nya penggunaan air, Tujuan dari penelitian ini adalah untuk menghemat penggunaan air wudhu pada mobil wudhu. Metode Penelitian ini memodelkan sistem menggunakan sensor leap motion, Arduino Uno, sensor ultrasonic, dan kran air dengan motor servo. Sistem yang di buat menghasilkan output berupa keluar nya deras air menggunakan sensor leap motion untuk membuka kran air dengan motor servo sebagai pembuka kran deras air. Leap motion membaca pola tangan pada berupa data hasil pendeteksian tangan yang kemudian diproses oleh arduino untuk diteruskan ke web service dan diteruskan kembali untuk di proses di database. Pada penelitian ini di peroleh hasil dari pengujian yang dilakukan pada beberapa pengguna dengan kapasitas tangki air 3 liter dan menghasilkan rata rata penggunaan air 2,1 liter.

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Abdul-Ameer, Hussam K., Luma Issa Abdul-Kreem, Huda Adnan, and Zahra Sami. "A Haptic feedback system based on leap motion controller for prosthetic hand application." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5772. http://dx.doi.org/10.11591/ijece.v10i6.pp5772-5778.

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Leap Motion Controller (LMC) is a gesture sensor consists of three infrared light emitters and two infrared stereo cameras as tracking sensors. LMC translates hand movements into graphical data that are used in a variety of applications such as virtual/augmented reality and object movements control. In this work, we intend to control the movements of a prosthetic hand via (LMC) in which fingers are flexed or extended in response to hand movements. This will be carried out by passing in the data from the Leap Motion to a processing unit that processes the raw data by an open-source package (Processing i3) in order to control five servo motors using a micro-controller board. In addition, haptic setup is proposed using force sensors (FSR) and vibro-motors in which the speed of these motors is proportional to the amount of the grasp force exerted by the prosthetic hand. Investigation for optimal placement of the FSRs on a prosthetic hand to obtain convenient haptic feedback has been carried out. The results show the effect of object shape and weight on the obtained response of the FSR and how they influence the locations of the sensors.

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Houston, Andrew, Vanessa Walters, Thomas Corbett, and Russell Coppack. "Evaluation of a multi-sensor Leap Motion setup for biomechanical motion capture of the hand." Journal of Biomechanics 127 (October 2021): 110713. http://dx.doi.org/10.1016/j.jbiomech.2021.110713.

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Kim, Sung Hee, Yongchan Kwon, KangGeon Kim, and Youngsu Cha. "Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network." Applied Sciences 10, no. 6 (2020): 2194. http://dx.doi.org/10.3390/app10062194.

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Soft sensors are attracting significant attention in human–machine interaction due to their high flexibility and adaptability. However, estimating motion state from these sensors is difficult due to their nonlinearity and noise. In this paper, we propose a deep learning network for a smart glove system to predict the moving state of a piezoelectric soft sensor. We implemented the network using Long-Short Term Memory (LSTM) units and demonstrated its performance in a real-time system based on two experiments. The sensor’s moving state was estimated and the joint angles were calculated. Since we use moving state in the sensor offset calculation and the offset value is used to estimate the angle value, the accurate moving state estimation results in good performance for angle value estimation. The proposed network performed better than the conventional heuristic method in estimating the moving state. It was also confirmed that the calculated values successfully mimic the joint angles measured using a leap motion controller.

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Vysocký, Aleš, Stefan Grushko, Petr Oščádal, et al. "Analysis of Precision and Stability of Hand Tracking with Leap Motion Sensor." Sensors 20, no. 15 (2020): 4088. http://dx.doi.org/10.3390/s20154088.

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In this analysis, we present results from measurements performed to determine the stability of a hand tracking system and the accuracy of the detected palm and finger’s position. Measurements were performed for the evaluation of the sensor for an application in an industrial robot-assisted assembly scenario. Human–robot interaction is a relevant topic in collaborative robotics. Intuitive and straightforward control tools for robot navigation and program flow control are essential for effective utilisation in production scenarios without unnecessary slowdowns caused by the operator. For the hand tracking and gesture-based control, it is necessary to know the sensor’s accuracy. For gesture recognition with a moving target, the sensor must provide stable tracking results. This paper evaluates the sensor’s real-world performance by measuring the localisation deviations of the hand being tracked as it moves in the workspace.

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Melfasoni, Keken, Anggy Trisnadoli, and Ibnu Surya. "Implementasi Leap Motion Controller pada Aplikasi Pengenalan Tata Surya." IT JOURNAL RESEARCH AND DEVELOPMENT 4, no. 1 (2019): 40–45. http://dx.doi.org/10.25299/itjrd.2019.vol4(1).2676.

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Metode pembelajaran diterapkan saat ini kebanyakan masih bersifat manual salah satunya materi tentang sistem tata surya. Sulitnya media pembelajaran yang sekarang masih bersifat manual salah satunya seperti gambar-gambar dibuku membuat siswa sulit untuk memahami materi dan membayangkan keberadaan sistem tata surya yang dipelajari. Untuk memudahkan dalam pemahaman materi, maka dibangunlah aplikasi pengenalan tata surya sebagai alternatif media pembelajaran bagi siswa sekolah dasar yang dimana di implementasikan menggunakan Leap Motion. Leap Motion adalah alat sensor perangkat keras komputer yang mendukung gerakan tangan dan jari manusia di udara lalu dijadikan masukkan agar dapat diproses oleh program komputer. Aplikasi pengenalan tata surya dibangun menggunakan aplikasi Unity dan Blender dengan menyajikan pembelajaran tata surya dengan objek 3D. Berdasarkan hasil pengujian didapatkan bahwa keseluruhan fungsi yang terdapat pada aplikasi pengenalan tata surya sudah sesuai 100% dengan output yang diharapkan. Kemudian didapatkan bahwa Leap Motion dapat mendeteksi tangan pengguna pada rentang 7-50 cm untuk jarak tangan pengguna secara vertikal, rentang 0-40 cm untuk jarak sisi kiri dan kanan Leap Motion, rentang 0-30 cm untuk jarak sisi depan dan belakang Leap Motion, dan terdapat cahaya. Lalu pada pengujian menggunakan Paired Sample t-Test diperoleh bahwa terdapat kenaikan nilai pengerjaan soal latihan oleh siswa sebesar 33.75% setelah menggunakan aplikasi pengenalan tata surya yang berarti bahwa aplikasi pengenalan tata surya memberikan pengaruh terhadap nilai siswa dan memahami konsep pembelajaran dengan menggunakan aplikasi.

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Guzsvinecz, Tibor, Veronika Szucs, and Cecilia Sik-Lanyi. "Suitability of the Kinect Sensor and Leap Motion Controller—A Literature Review." Sensors 19, no. 5 (2019): 1072. http://dx.doi.org/10.3390/s19051072.

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As the need for sensors increases with the inception of virtual reality, augmented reality and mixed reality, the purpose of this paper is to evaluate the suitability of the two Kinect devices and the Leap Motion Controller. When evaluating the suitability, the authors’ focus was on the state of the art, device comparison, accuracy, precision, existing gesture recognition algorithms and on the price of the devices. The aim of this study is to give an insight whether these devices could substitute more expensive sensors in the industry or on the market. While in general the answer is yes, it is not as easy as it seems: There are significant differences between the devices, even between the two Kinects, such as different measurement ranges, error distributions on each axis and changing depth precision relative to distance.

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Archana, T., Srigitha S. Nath, and S. Praveenkumar. "Development of Robotic Arm Through Hand Gesture Recognition Using Leap Motion Sensor." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1889–93. http://dx.doi.org/10.1166/jctn.2020.8461.

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The objective of this paper has been the development of a prototype of articulated Robotic arm and implementation of a control strategy for gesture recognition through (Leap motion sensor), by means the natural movement of the fore-arm and hand. The series of advances relative to the control techniques have caused that the robotics it has also introduced as an educational and complement in obligatory basic teachings. To develop and to control Robotic elements locally or remotely, it has always proven to be a clear example of additional motivation. The prototype developed has exceeded the initial expectations and at low cost.

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Joseph Vedhagiri, Godwin Ponraj, and Hongliang Ren. "Sensor Fusion of Leap Motion Controller and Flex Sensors using Kalman Filter for Human Finger Tracking." IAES International Journal of Robotics and Automation (IJRA) 6, no. 3 (2017): 178. http://dx.doi.org/10.11591/ijra.v6i3.pp178-187.

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<span>In our daily life, we, human beings use our hands in various ways for most of our day-to-day activities. Tracking the position, orientation and articulation of human hands has a variety of applications including gesture recognition, robotics, medicine and health care, design and manufacturing, art and entertainment across multiple domains. However, it is an equally complex and challenging task due to several factors like higher dimensional data from hand motion, higher speed of operation, self-occlusion, etc. This paper puts forth a novel method for tracking the finger tips of human hand using two distinct sensors and combining their data by sensor fusion technique.</span>

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Ponraj, Godwin, and Hongliang Ren. "Sensor Fusion of Leap Motion Controller and Flex Sensors Using Kalman Filter for Human Finger Tracking." IEEE Sensors Journal 18, no. 5 (2018): 2042–49. http://dx.doi.org/10.1109/jsen.2018.2790801.

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Rehman, Inam Ur, Sehat Ullah, Dawar Khan, et al. "Fingertip Gestures Recognition Using Leap Motion and Camera for Interaction with Virtual Environment." Electronics 9, no. 12 (2020): 1986. http://dx.doi.org/10.3390/electronics9121986.

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The emergence in computing and the latest hardware technologies realized the use of natural interaction with computers. Gesture-based interaction is one of the prominent fields of natural interactions. The recognition and application of hand gestures in virtual environments (VEs) need extensive calculations due to the complexities involved, which directly affect the performance and realism of interaction. In this paper, we propose a new interaction technique that uses single fingertip-based gestures for interaction with VEs. The objective of the study is to minimize the computational cost, increase performance, and improve usability. The interaction involves navigation, selection, translation, and release of objects. For this purpose, we propose a low-cost camera-based system that uses a colored fingertip for the fastest and accurate recognition of gestures. We also implemented the proposed interaction technique using the Leap Motion controller. We present a comparative analysis of the proposed system with the Leap Motion controller for gesture recognition and operation. A VE was developed for experimental purposes. Moreover, we conducted a comprehensive analysis of two different recognition setups including video camera and the Leap Motion sensor. The key parameters for analysis were task accuracy, interaction volume, update rate, and spatial distortion of accuracy. We used the Standard Usability Scale (SUS) for system usability analysis. The experiments revealed that camera implementation was found with good performance, less spatial distortion of accuracy, and large interaction volume as compared to the Leap Motion sensor. We also found the proposed interaction technique highly usable in terms of user satisfaction, user-friendliness, learning, and consistency.

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Güttler, Jörg, Dany Bassily, Christos Georgoulas, Thomas Linner, and Thomas Bock. "Unobtrusive Tremor Detection While Gesture Controlling a Robotic Arm." Journal of Robotics and Mechatronics 27, no. 1 (2015): 103–4. http://dx.doi.org/10.20965/jrm.2015.p0103.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/12.jpg"" width=""300"" />Gesture based validation</div> A light weight robotic arm (Jaco) has been interfaced with a novel gesture detection sensor (Leap Motion Controller), substituting complicated conventional input devices, i.e., joysticks and pads. Due to the enhanced precision and high throughput capabilities of the Leap Motion Controller, the unobtrusive measurement of physiological tremor can be extracted. An algorithm was developed to constantly detect and indicate potential user hand tremor patterns in real-time. Additionally a calibration algorithm was developed to allow an optimum mapping between the user hand movement, tracked by the Leap Motion Controller, and the Jaco arm, by filtering unwanted oscillations, allowing for a more natural human-computer interaction. </span>

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Qi, Wen, Xiaorui Liu, Longbin Zhang, Lunan Wu, Wenchuan Zang, and Hang Su. "Adaptive sensor fusion labeling framework for hand pose recognition in robot teleoperation." Assembly Automation 41, no. 3 (2021): 393–400. http://dx.doi.org/10.1108/aa-11-2020-0178.

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Purpose The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system’s performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation. Design/methodology/approach In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand. Findings The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods. Originality/value This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement.

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Oguntosin, Victoria, and Akindele Ayoola E. "Control of Soft Robotic Artificial Muscle with Hand Gesture Using Leap Motion Sensor." Advances in Science, Technology and Engineering Systems Journal 5, no. 5 (2020): 1007–12. http://dx.doi.org/10.25046/aj0505123.

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Gunawardane, Hiroshan, Nimali Medagedara, and Achintha Madhusanka. "Control of Robot Arm Based on Hand Gestures Using Leap Motion Sensor Technology." International Journal of Robotics and Mechatronics 3, no. 1 (2017): 7–14. http://dx.doi.org/10.21535/ijrm.v3i1.930.

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Ahmed, S., V. Popov, A. Topalov, and N. Shakev. "Hand Gesture based Concept of Human - Mobile Robot Interaction with Leap Motion Sensor." IFAC-PapersOnLine 52, no. 25 (2019): 321–26. http://dx.doi.org/10.1016/j.ifacol.2019.12.543.

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Rudd, Grant, Liam Daly, and Filip Cuckov. "Intuitive gesture-based control system with collision avoidance for robotic manipulators." Industrial Robot: the international journal of robotics research and application 47, no. 2 (2020): 243–51. http://dx.doi.org/10.1108/ir-03-2019-0045.

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Purpose This paper aims to present an intuitive control system for robotic manipulators that pairs a Leap Motion, a low-cost optical tracking and gesture recognition device, with the ability to record and replay trajectories and operation to create an intuitive method of controlling and programming a robotic manipulator. This system was designed to be extensible and includes modules and methods for obstacle detection and dynamic trajectory modification for obstacle avoidance. Design/methodology/approach The presented control architecture, while portable to any robotic platform, was designed to actuate a six degree-of-freedom robotic manipulator of our own design. From the data collected by the Leap Motion, the manipulator was controlled by mapping the position and orientation of the human hand to values in the joint space of the robot. Additional recording and playback functionality was implemented to allow for the robot to repeat the desired tasks once the task had been demonstrated and recorded. Findings Experiments were conducted on our custom-built robotic manipulator by first using a simulation model to characterize and quantify the robot’s tracking of the Leap Motion generated trajectory. Tests were conducted in the Gazebo simulation software in conjunction with Robot Operating System, where results were collected by recording both the real-time input from the Leap Motion sensor, and the corresponding pose data. The results of these experiments show that the goal of accurate and real-time control of the robot was achieved and validated our methods of transcribing, recording and repeating six degree-of-freedom trajectories from the Leap Motion camera. Originality/value As robots evolve in complexity, the methods of programming them need to evolve to become more intuitive. Humans instinctively teach by demonstrating the task to a given subject, who then observes the various poses and tries to replicate the motions. This work aims to integrate the natural human teaching methods into robotics programming through an intuitive, demonstration-based programming method.

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Hu, TianJian, XiaoJun Zhu, XueQian Wang, TianShu Wang, JunFeng Li, and WeiPing Qian. "Human stochastic closed-loop behavior for master-slave teleoperation using multi-leap-motion sensor." Science China Technological Sciences 60, no. 3 (2017): 374–84. http://dx.doi.org/10.1007/s11431-016-0434-x.

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C. C., Lim, Sim K. S., and Toa C.K. "Development of Visual-based Rehabilitation Using Sensors for Stroke Patient." International Journal on Robotics, Automation and Sciences 2 (October 30, 2020): 25–30. http://dx.doi.org/10.33093/ijoras.2020.2.4.

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This project concerns on the development of applications using sensors for the rehabilitation of stroke patients. Thus, the leap motion sensor is employed for the finger motor rehabilitation training while the Microsoft Kinect sensor is utilized for the upper limb motor rehabilitation. Two applications which are named ‘Pick and Place’ and ‘Stone Breaker’ are developed. For the first application, the patient is required to pick up the virtual blocks and stack it up. The ‘Stone Breaker’ game requires the patient to move the upper limb in controlling the paddle movement in the game. At the end of the project, it is able to achieve the dominant objective of the project when the tested patient shows significant improvement in both the application

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Soares, Nayron Medeiros, Gabriela Magalhães Pereira, Renata Italiano da Nóbrega Figueiredo, Gleydson Silva Morais, and Sandy Gonzaga De Melo. "Terapia baseada em realidade virtual usando o Leap Motion Controller para reabilitação do membro superior após acidente vascular cerebral." Scientia Medica 27, no. 2 (2017): 25935. http://dx.doi.org/10.15448/1980-6108.2017.2.25935.

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*** Virtual reality therapy using the Leap Motion Controller for post-stroke upper limb rehabilitation ***AIMS: To evaluate the applicability of a virtual reality-based motion sensor for post-stroke upper limb rehabilitation.CASES DESCRIPTION: Three post-stroke patients were subjected to virtual reality training for rehabilitation of their upper limbs using the Leap Motion Controller technology and the game Playground 3D® for 3 consecutive days. On the first and last days, the Box and Blocks test, the De Melo Eye-Hand Coordination Test, and transcranial magnetic stimulation were applied. On the last day, the patients were evaluated with the Experience Evaluation Form. After the proposed training, a lower motor threshold was observed in both cerebral hemispheres, as well as better performance in the tests that evaluated hand and eye-hand coordination skills. The proposed therapy was well received by the patients.CONCLUSIONS: No adverse effects were observed, and promising and precise results were obtained for the virtual reality-based training using the Leap Motion Controller and Playground 3D®. The training allowed patients to have an active role in the rehabilitation of stroke-induced upper limb sequelae.

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Gamboa, Edwin, Andres Serrato, Juan Castro, Diana Toro, and Maria Trujillo. "Advantages and Limitations of Leap Motion from a Developers', Physical Therapists', and Patients' Perspective." Methods of Information in Medicine 59, no. 02/03 (2020): 110–16. http://dx.doi.org/10.1055/s-0040-1715127.

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Abstract Background Physical rehabilitation exergames (PREGs) are suitable for motivating patients toward completing treatments. Leap Motion (LM) is a motion sensor that may be useful for developing PREGs targeted at hands and fingers rehabilitation. Therefore, knowing the advantages and limitations of LM is relevant to understand under which conditions this sensor may be suitable. Objective In this article, we present a qualitative study to identify the main advantages and limitations of LM for PREGs. Methods We collect data using interviews with a group of PREGs developers, physical therapy experts, and patients. We employ the thematic analysis method to analyze the collected data. Results We found that the advantages and limitations of LM are related to (1) the role as PREG development tool that enables hand movements detection, (2) the capability to be a mobile and easy-to-use capturing technology, and (3) the contribution to add value in rehabilitation therapy by motivating physical therapists and patients to use PREGs. Conclusion The analysis shows that LM is a suitable and cost-effective solution for developing usable PREGs for some hand and finger rehabilitation movements with a moderate development effort. However, the development maturity of LM poses limitations related to reliability and robustness, preventing the use of LM as a standalone physical rehabilitation tool. Our findings serve as guidelines for developers and physical therapists during the development and use of PREGs targeted at hands and fingers, guiding the decision-making process during feasibility analysis and design stages.

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Oguntosin, Victoria, and Ademola Abdulkareem. "Hand gesture control and design of a rotary pneumatic soft actuator using leap motion sensor." International Journal of Intelligent Robotics and Applications 4, no. 3 (2020): 328–41. http://dx.doi.org/10.1007/s41315-020-00140-5.

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N. Abila, Aljhon. "Recognition of American Sign Language Fingerspelling by Applying Matrix Matching Algorithm Using Leap Motion Controller Sensor." International Journal for Research in Applied Science and Engineering Technology V, no. III (2017): 243–53. http://dx.doi.org/10.22214/ijraset.2017.3044.

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Jarsaillon, Pierre J., Naohisa Sakamoto, and Akira Kageyama. "Flexible visualization framework for head-mounted display with gesture interaction interface." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 03 (2018): 1840002. http://dx.doi.org/10.1142/s1793962318400020.

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As the development of new visualization systems, within the field of simulation, offers their users more insights from their simulations, immersive systems are becoming a part of the visualization techniques. With the recent advancements of the Head-Mounted Displays (HMDs) and popularity of motion sensors, human beings and computer become more interactive. This study aims to evaluate the potential of such systems as a visualization tool through the development of a new flexible framework for visualization within virtual reality (VR) environment, using an Oculus Rift and a Leap Motion. Two approaches are then compared: high-3D object rendering within the virtual scene approach and a user experience-oriented system with an intuitive interface. To assess the quality of the interface and its relevance for the user, different types of gestures are implemented and tested. From an experiment on users to evaluate the developed system as a proper visualization tool, the HMDs, paired with a motion sensor to make a gesture-controlled interface seem to be promising mediums despite various constraints on development regarding the technology limitations.

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Felippsen, Eduardo Alberto, and Clodis Boscarioli. "Explorando a Interação com Sensor Gestual em Sala de Aula: o Leap Motion no ensino de Crianças Surdas." Revista Brasileira de Informática na Educação 27, no. 02 (2019): 23. http://dx.doi.org/10.5753/rbie.2019.27.02.23.

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Dispositivos para a interação gestual com computadores têm o potencial de ampliar as possibilidades de interação das pessoas com o computador e aplicações específicas. Essas possibilidades favorecem a acessibilidade e a inclusão, importantes no contexto escolar. Este trabalho investiga meios de potencializar a participação e o engajamento de crianças surdas em sala de aula por meio do uso de sensores gestuais para interagir via computador. A investigação foi conduzida em um cenário real envolvendo uma equipe multidisciplinar de professores com formação bilíngue que atua com crianças surdas de ensino fundamental. Planos de aulas foram elaborados visando construir, por meio da interface de interação gestual, cenários que potencializassem a interação entre os alunos e também entre os alunos e o professor em sala de aula. Os resultados sugerem que o uso do dispositivo contribuiu à participação dos professores e alunos de forma integrada, especialmente daqueles que já dominam Libras (Língua Brasileira de Sinais), promovendo o engajamento dos participantes em um ambiente de aprendizagem mais interativo.

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de Oliveira, Juliana M., Rafael Carneiro G. Fernandes, Cristtiano S. Pinto, Plácido R. Pinheiro, Sidarta Ribeiro, and Victor Hugo C. de Albuquerque. "Novel Virtual Environment for Alternative Treatment of Children with Cerebral Palsy." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8984379.

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Cerebral palsy is a severe condition usually caused by decreased brain oxygenation during pregnancy, at birth or soon after birth. Conventional treatments for cerebral palsy are often tiresome and expensive, leading patients to quit treatment. In this paper, we describe a virtual environment for patients to engage in a playful therapeutic game for neuropsychomotor rehabilitation, based on the experience of the occupational therapy program of the Nucleus for Integrated Medical Assistance (NAMI) at the University of Fortaleza, Brazil. Integration between patient and virtual environment occurs through the hand motion sensor “Leap Motion,” plus the electroencephalographic sensor “MindWave,” responsible for measuring attention levels during task execution. To evaluate the virtual environment, eight clinical experts on cerebral palsy were subjected to a questionnaire regarding the potential of the experimental virtual environment to promote cognitive and motor rehabilitation, as well as the potential of the treatment to enhance risks and/or negatively influence the patient’s development. Based on the very positive appraisal of the experts, we propose that the experimental virtual environment is a promising alternative tool for the rehabilitation of children with cerebral palsy.

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Bird, Jordan J., Anikó Ekárt, and Diego R. Faria. "British Sign Language Recognition via Late Fusion of Computer Vision and Leap Motion with Transfer Learning to American Sign Language." Sensors 20, no. 18 (2020): 5151. http://dx.doi.org/10.3390/s20185151.

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In this work, we show that a late fusion approach to multimodality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of image classification (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a Convolutional Neural Network and optimised Artificial Neural Network, and the Leap Motion model is implemented by an evolutionary search of Artificial Neural Network topology. Next, the two best networks are fused for synchronised processing, which results in a better overall result (94.44%) as complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multimodality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via British Sign Language, all three models outperform standard random weight distribution when classifying American Sign Language (ASL), and the best model overall for ASL classification was the transfer learning multimodality approach, which scored 82.55% accuracy.

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Guna, Jože, Grega Jakus, Matevž Pogačnik, Sašo Tomažič, and Jaka Sodnik. "An Analysis of the Precision and Reliability of the Leap Motion Sensor and Its Suitability for Static and Dynamic Tracking." Sensors 14, no. 2 (2014): 3702–20. http://dx.doi.org/10.3390/s140203702.

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Oña, Edwin Daniel, Carlos Balaguer, Roberto Cano-de la Cuerda, Susana Collado-Vázquez, and Alberto Jardón. "Effectiveness of Serious Games for Leap Motion on the Functionality of the Upper Limb in Parkinson’s Disease: A Feasibility Study." Computational Intelligence and Neuroscience 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/7148427.

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The design and application of Serious Games (SG) based on the Leap Motion sensor are presented as a tool to support the rehabilitation therapies for upper limbs. Initially, the design principles and their implementation are described, focusing on improving both unilateral and bilateral manual dexterity and coordination. The design of the games has been supervised by specialized therapists. To assess the therapeutic effectiveness of the proposed system, a protocol of trials with Parkinson’s patients has been defined. Evaluations of the physical condition of the participants in the study, at the beginning and at the end of the treatment, are carried out using standard tests. The specific measurements of each game give the therapist more detailed information about the patients’ evolution after finishing the planned protocol. The obtained results support the fact that the set of developed video games can be combined to define different therapy protocols and that the information obtained is richer than the one obtained through current clinical metrics, serving as method of motor function assessment.

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Paparizos, Christos, Nikolaos Tsafas, and Michael Birbas. "A Zynq-Based Robotic System for Treatment of Contagious Diseases in Hospital Isolated Environment." Technologies 8, no. 2 (2020): 28. http://dx.doi.org/10.3390/technologies8020028.

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The rapid evolution of smart assisted living operations in combination with the blooming of commercial robots calls for the use of robotic based systems. Specifically, certain circumstances such as the handling of critical, contagious virus outbreaks like the recent novel Coronavirus epidemic can be benefited by an assisting mobile robot system controlled remotely, complementing measures like the isolation of patients from medical stuff. Within this context, the robotic-based solution to be employed needs to be easy to deploy, able to manufacture with low cost, and able to operate with ease by non-trained personnel. Also, to address the needs of existing hospitals, traditional or smart ones, as well as the temporary risk management facilities in, for example, quarantined cities, ease of integration in terms of size and infrastructure requirements is a must. In this work, the design and implementation of a robotic chassis bearing an arm manipulator is presented, addressing all these needs efficiently. Special attention has been given to the ease of teleoperation with minimal need for equipment and expertise, utilizing a Leap Motion virtual reality sensor which outweighs Microsoft’s Kinect capabilities. Furthermore, a reconfigurable hardware and software integrated system has been used to control the communication, algorithm processing and motion control utilizing a Xilinx Zynq system on chip (SoC).

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Yeamkuan, Suparat, and Kosin Chamnongthai. "3D Point-of-Intention Determination Using a Multimodal Fusion of Hand Pointing and Eye Gaze for a 3D Display." Sensors 21, no. 4 (2021): 1155. http://dx.doi.org/10.3390/s21041155.

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This paper proposes a three-dimensional (3D) point-of-intention (POI) determination method using multimodal fusion between hand pointing and eye gaze for a 3D virtual display. In the method, the finger joint forms of the pointing hand sensed by a Leap Motion sensor are first detected as pointing intention candidates. Subsequently, differences with neighboring frames, which should be during hand pointing period, are checked by AND logic with the hand-pointing intention candidates. A crossing point between the eye gaze and hand pointing lines is finally decided by the closest distance concept. In order to evaluate the performance of the proposed method, experiments with ten participants, in which they looked at and pointed at nine test points for approximately five second each, were performed. The experimental results show the proposed method measures 3D POIs at 75 cm, 85 cm, and 95 cm with average distance errors of 4.67%, 5.38%, and 5.71%, respectively.

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Ramírez-Fernández, Cristina, Victoria Meza-Kubo, Eloísa García-Canseco, et al. "Massage Therapy of the Back Using a Real-Time Haptic-Enhanced Telerehabilitation System." Mobile Information Systems 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/5253613.

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We present the usability evaluation of a haptic-enhanced telerehabilitation system for massage therapy of the back using the Vybe haptic gaming pad and the gesture sensor LEAP motion controller. The evaluated system includes features that allow for (i) administering online therapy programs, (ii) providing self-adjustable and safety treatment of back massages using a virtual environment, and (iii) saving and replaying massage sessions according to a patient’s therapy program. The usability evaluation with 25 older adults and 10 specialists suggests that the haptic telerehabilitation system is perceived with high usability and pleasurable user experience, while providing personalized intensity of haptic therapy in a supervised, real-time, and secure way to treat the patient. Moreover, the specialists totally agree that the system design features, such as save and play, and delimiting therapy zones are the most important for back massage therapy, while the features of regulating feedback intensity and providing/receiving a massage remotely are also important. Finally, based on their comments, five design insights aiming at improving the current version of the system were generated.

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Zhang, Bo, Guanglong Du, Wenming Shen, and Fang Li. "Gesture-based human-robot interface for dual-robot with hybrid sensors." Industrial Robot: the international journal of robotics research and application 46, no. 6 (2019): 800–811. http://dx.doi.org/10.1108/ir-11-2018-0245.

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Purpose The purpose of this paper is the research of a novel gesture-based dual-robot collaborative interaction interface, which achieves the gesture recognition when both hands overlap. This paper designs a hybrid-sensor gesture recognition platform to detect the both-hand data for dual-robot control. Design/methodology/approach This paper uses a combination of Leap Motion and PrimeSense in the vertical direction, which detects both-hand data in real time. When there is occlusion between hands, each hand is detected by one of the sensors, and a quaternion-based algorithm is used to realize the conversion of two sensors corresponding to different coordinate systems. When there is no occlusion, the data are fused by a self-adaptive weight fusion algorithm. Then the collision detection algorithm is used to detect the collision between robots to ensure safety. Finally, the data are transmitted to the dual robots. Findings This interface is implemented on a dual-robot system consisting of two 6-DOF robots. The dual-robot cooperative experiment indicates that the proposed interface is feasible and effective, and it takes less time to operate and has higher interaction efficiency. Originality/value A novel gesture-based dual-robot collaborative interface is proposed. It overcomes the problem of gesture occlusion in two-hand interaction with low computational complexity and low equipment cost. The proposed interface can perform a long-term stable tracking of the two-hand gestures even if there is occlusion between the hands. Meanwhile, it reduces the number of hand reset to reduce the operation time. The proposed interface achieves a natural and safe interaction between the human and the dual robot.

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Karacı, Abdulkadir, Kemal Akyol, and Mehmet Ugur Turut. "Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features." Applied Computer Systems 26, no. 1 (2021): 12–21. http://dx.doi.org/10.2478/acss-2021-0002.

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Abstract In this study, a machine learning-based system, which recognises the Turkish sign language person-independent in real-time, was developed. A leap motion sensor was used to obtain raw data from individuals. Then, handcraft features were extracted by using Euclidean distance on the raw data. Handcraft features include finger-to-finger, finger -to-palm, finger -to-wrist bone, palm-to-palm and wrist-to-wrist distances. LR, k-NN, RF, DNN, ANN single classifiers were trained using the handcraft features. Cascade voting approach was applied with two-step voting. The first voting was applied for each classifier’s final prediction. Then, the second voting, which voted the prediction of all classifiers at the final decision stage, was applied to improve the performance of the proposed system. The proposed system was tested in real-time by an individual whose hand data were not involved in the training dataset. According to the results, the proposed system presents 100 % value of accuracy in the classification of one hand letters. Besides, the recognition accuracy ratio of the system is 100 % on the two hands letters, except “J” and “H” letters. The recognition accuracy rates were 80 % and 90 %, respectively for “J” and “H” letters. Overall, the cascade voting approach presented a high average classification performance with 98.97 % value of accuracy. The proposed system enables Turkish sign language recognition with high accuracy rates in real time.

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Hou, Chun Xia, and Er Hua Zhang. "Pipeline Leak Detection Based on Double Sensor Negative Pressure Wave." Applied Mechanics and Materials 313-314 (March 2013): 1225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1225.

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Pipeline leak lead to huge economic losses and environmental pollution. Leak detection system based on single sensor negative pressure wave often causes false alarm. In this paper the double sensor method is adopted to exclude false alarm by determining the propagation direction of the pressure wave. In order to remove the inverse coherent interference caused by pump running, the phase difference of primary low frequency component is used to identify the sign of the time delay between the double sensors. The experiment shows the mothod is effective.

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Jamjoom, Abdulrzaq Naji. "Motion Prediction of Underwater Sensors." European Journal of Engineering Research and Science 5, no. 10 (2020): 1249–52. http://dx.doi.org/10.24018/ejers.2020.5.10.2177.

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In this work, we will simulate the motion of a single underwater sensor knowing the current velocity to predict its location and velocity during certain time frame using a numerical approach of non-linear time-dependent partial differential equations and develop numerical computer programming code to solve the equations. The underwater sensor are used to collect data for many scientific and practical reasons all the sensor collected data without specifying the sensor location and time will be missing lowers valuable information and by simulating the sensor motion numerically will have many values and impact on the underwater sensor industries as this will lead to less power consumption sensors with smaller size and less network coverage required. This paper will study the kinetics of the underwater sensor which will resulted to a set of non-linear time-dependent partial differential equations that can be solved analytically and computer programming simulation is developed to solve the equations and predict the motion of underwater sensor. Different scenarios considered in the work such as simulating the result for different sensor’s density and the effect on its final position. Also, the result will include the sensor velocity simulation and comparison with the sea current velocity. This work is limited to the motion prediction of single underwater sensor and the result is only for mechanical aspect of the problem, the networks connectivity or coverage is out-of-scope.

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Meier, Walter N., and Mingrui Dai. "High-resolution Sea-ICE motions from AMSR-E imagery." Annals of Glaciology 44 (2006): 352–56. http://dx.doi.org/10.3189/172756406781811286.

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AbstractPassive microwave remote-sensing imagery has proven to be a useful Source for Sea-ICE motions because of its all-sky capabilities. However, the low Spatial resolution of the passive microwave Sensors has not allowed the retrieval of Small-scale motion details Such as lead and ridge formation. The NAsA Earth Observing System Advanced Microwave Scanning Radiometer (AMSR-E) has more than double the Spatial resolution of previous passive microwave Sensors, allowing it to track the formation of moderate-sized leads and yield much more detailed and more accurate ICE-motion estimates. Comparisons with buoys indicate that AMSR-E motions have errors >30% lower than ICE motions derived from the previous passive microwave Sensors. While AMSR-E Still cannot retrieve the Same level of detail as Synthetic aperture radars or visible/infrared Sensors, AMSR-E’s complete coverage can better capture the ephemeral motions of the Sea-ICE cover on daily, and potentially Sub-daily, timescales.

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Journal articles on the topic 'Leap motion sensor'

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