A comprehensive leap motion database for hand gesture recognition

Ameur, Safa; Khalifa, Anouar Ben; Bouhlel, Med Salim

doi:10.1109/setit.2016.7939924

Cited by 46 publications

(10 citation statements)

References 7 publications

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“…On the more practical applications, Ameur, et. al [35] developed a comprehensive LEAP Motion database for hand gesture recognition. This was used for medical visualization while focusing on user satisfaction with movements such as click, left and right rotate, increase and decrease contrast, zoom in and out, move left and right, previous and next.…”

Section: Fig 5 the Leap Motion Controllermentioning

confidence: 99%

Using gestures to interact with home automation systems

Villanueva

2021

Advanced Materials Proceedings

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Homes and working spaces are considered significant contributors of energy consumption and carbon emissions worldwide. Previous studies in the field of home-and building automation have demonstrated the sustainability gain brought by smart home solutions, in terms of energy-efficiency, economic savings, and enhanced living and working conditions.A major barrier, however, to the adoption of these solutions is the complexity and limited usability of user interfaces. As a response to these challenges, this study investigates the use of gestures as a natural way of controlling and interacting with home automation systems.Using the available motion capture technology, a gesture dictionary will be defined as a set of meaning actions in free-form and in-air movements. A usability test will be conducted to measure the resulting socio-technical aspects. Lastly, the study will present the analysis and effects of gestures control for a higher up-take of smart home solutions towards designing and maintaining buildings of the future that are both user-centric and resource efficient to reduce our overall carbon footprint.iii ACKNOWLEDGEMENTSThe author extends his sincerest gratitude to the following people who made this graduate thesis possible and a worthwhile endeavor.To Prof. Olaf Droegehorn, my thesis supervisor, for the utmost patience and sincerity for keeping up with my quirks. After all the long consultation hours spent during the formulation, implementation and documentation of the thesis, and personal mentoring that made this study possible, thank you very much.To Prof. Jean-Philippe Georges, Prof. Jari Porras, and Prof. Karl Andersson, the country coordinators, for the knowledge shared during the early stages of the thesis, and the appraisals given with our accomplishments during final presentation of this thesis.To Prof. Eric Rondeau, the PERCCOM programme coordinator, who inspired me to pursue this undertaking with hopes of realizing the real-world applications of technology and innovation to help address issues on sustainability. Thank you for choosing to grant me this Erasmus Mundus Master scholarship, and for the trust as I fulfill my duties as the former Erasmus Mundus Association programme representative for PERCCOM.To the PERCCOM Consortium, the faculty of professors from our different universities, and the industrial partners who guided me in shaping the knowledge that helped me accomplish the tasks needed in this research.To all the university and administrative staffs from France, Finland, Russia, United Kingdoms, Sweden, and Germany, especially to Caroline Schrepff, our very hardworking PERCCOM secretary, for the time they dedicated in answering my questions, preparing necessary documents for us, and for making sure of our smooth mobility from different countries.To the European Commission responsible for the Erasmus+ programmes, for this one-of-a-kind learning experience of sharing myself with the world. Thank you!

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Section: Fig 5 the Leap Motion Controllermentioning

confidence: 99%

Using gestures to interact with home automation systems

Villanueva

2021

Advanced Materials Proceedings

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“…Terapi dan rehabilitasi otot tangan sangat bergantung pada gerakan olahraga rutin pada pasien dengan rekomendasi dari dokter. Latihan gerak yang rutin dan tepat dapat membantu terapi dan rehabilitasi pasien [2].…”

Section: Kata Kunci-terapi Otot Tangan Rehabilitasi Game Leap Motion Controllerunclassified

Leap Motion Controller Game Sebagai Alat Bantu Terapi Rehabilitasi Otot Tangan

Rakhmawati

Jannah

2020

JEPIN

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Salah satu bagian tubuh yang rawan mengalami cedera adalah tangan. Cedera tangan umumnya disebabkan oleh kecelakaan yang tidak disengaja ataupun dampak dari beberapa penyakit seperti parkinson dan stroke. Cedera otot tangan memerlukan proses penyembuhan secara terapi dan pemulihan dengan rehabilitasi. Terapi dan rehabilitasi otot tangan sangat bergantung pada gerakan olahraga rutin pada pasien dengan rekomendasi dari dokter. Namun terdapat beberapa kendala yang dihadapi oleh pasien yang memerlukan terapi ini. Pertama, proses terapi rehabilitasi otot tangan memerlukan waktu yang panjang sehingga membuat pasien merasa bosan. Kedua, pasien mendapatkan instruksi dari dokter untuk melakukan gerakan yang melatih otot tangan seperti mengepal-kepalkan tangan, mengambil benda-benda di rumah dengan menggunakan tangan. Pasien memerlukan alat bantu yang dapat mempermudah mereka untuk melakukan gerakan terapi secara rutin sesuai dengan perkembangan kondisi tangan mereka. Leap motion controller akan digunakan sebagai perangkat masukan (input) pada alat bantu terapi yang akan dikembangkan. Permainan tersebut dapat dimainkan oleh pemain dengan menggunakan leap motion controller. Permainan didesain berdasarkan rekomendasi gerakan rehabilitasi oleh dokter. Penelitian ini bertujuan untuk mengembangkan teknologi yang dapat membantu proses terapi rehabilitasi pasien cedera otot tangan. Alat bantu terapi yang dikembangan akan menerapkan gamifikasi untuk membuat proses terapi lebih mudah dan menyenangkan bagi pasien.

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“…For instance, Chen et al [ 17 ] extract directional codes of 3D motion trajectory as the feature and exploit a classifier based on SVM to classify letter and number gestures. Ameur et al [ 18 ] extract the positions of fingertips and palm center as features that are then trained with an SVM classifier. Their method reaches an average recognition rate of about 81% with 11 kinds of dynamic gestures.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

Chen

Kaikai

2020

Computational Intelligence and Neuroscience

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In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.

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A comprehensive leap motion database for hand gesture recognition

Cited by 46 publications

References 7 publications

Using gestures to interact with home automation systems

Using gestures to interact with home automation systems

Leap Motion Controller Game Sebagai Alat Bantu Terapi Rehabilitasi Otot Tangan

A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

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