scholarly journals Interaksi 3D Sensor Leap Motion untuk Menggenggam Benda Virtual

CYCLOTRON ◽  
2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Lukman Hakim ◽  
Surya Sumpeno ◽  
Supeno Mardi Susiki Nugroho
Keyword(s):  
Virtual Reality ◽  
Hand Movement ◽  
Fine Motor ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Leap Motion ◽  
3D Interaction ◽  
Virtual Objects ◽  
Accuracy And Precision ◽  
Fine Motor Performance

Abstrak - Penelitian ini membahas tentang interaksi 3D sensor Leap Motion untuk simulasi menggenggam Benda virtual Plastis. Sebuah interaksi 3D sensor Leap Motion yang digunakan sebagai simulasi untuk menggenggam benda virtual Plastis dengan menggunakan media objek telur virtual secara presisi dan akurasi yang tepat. Pada dasarnya menggenggam merupakan suatu kegiatan yang menerapkan kinerja motorik halus pada tangan untuk melakukan gerakan. Penggunaan sensor Leap Motion sebagai interaksi 3D dipakai untuk menggenggam objek maya dalam hal ini bentuk 3D telur virtual sebagai media praktiknya. Telur sendiri merupakan benda yang gampang distimulasi dan memiliki sifat texture yang halus untuk merespon segala bentuk gerakan pada genggaman tangan. Dalam penelitian Interaksi 3D Sensor Leap Motion untuk simulasi untuk menggenggam benda Virtual Plastis dengan menggunakan media objek telur virtual, ini di peruntukkan untuk mengetahui akurasi dan presisi dari pola gerakan tangan secara imersif. Pengembangan dari metode ini bertujuan untuk simulasi menggenggam benda atau objek maya dengan adanya interaksi pola gerakan tangan.Kata kunci: leapmotion, 3d, virtual reality, benda, telurAbstract - This study discusses about the 3D interaction of the Leap Motion sensor for the simulation of holding virtual plastic objects. A 3D interaction of the Leap Motion sensor that is used as a simulation to hold Plastis virtual objects by using virtual egg object media with precise and right accuracy. Basically, holding is an activity that applies fine motor performance on the hands to make movements. The use of the Leap Motion sensor as a 3D interaction is used to hold virtual objects in this case a 3D form of virtual eggs as practice media. Eggs are objects that are easily stimulated and have subtle texture to respond to all forms of movement in the hands. In the 3D interaction Leap Motion Sensors for virtual plastic objects holding simulation by using virtual egg object media, it is intended to find out the accuracy and precision of immersive hand movement patterns. The development of this method aims to simulate holding virtual objects or objects with the interaction of hand movement patterns.Keywords: leap motion, 3d, virtual reality, object, egg

scholarly journals Advantages of using 3D virtual reality based training in persons with Parkinson’s disease: a parallel study

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Imre Cikajlo ◽  
Karmen Peterlin Potisk
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Virtual Reality ◽  
Rating Scale ◽  
Functional Performance ◽  
Hand Movement ◽  
Clinical Effectiveness ◽  
Small Scale ◽  
Leap Motion ◽  
Parallel Study

Abstract Background Parkinson’s disease (PD) is a slowly progressive neurodegenerative disease. There are mixed reports on success of physiotherapy in patients with PD. Our objective was to investigate the functional improvements, motivation aspects and clinical effectiveness when using immersive 3D virtual reality versus non-immersive 2D exergaming. Methods We designed a randomized parallel study with 97 patients, but only 20 eligible participants were randomized in 2 groups; the one using 3D Oculus Rift CV1 and the other using a laptop. Both groups participated in the 10-session 3 weeks training with a pick and place task in the virtual world requiring precise hand movement to manipulate the virtual cubes. The kinematics of the hand was traced with Leap motion controller, motivation effect was assessed with modified Intrinsic Motivation Inventory and clinical effectiveness was evaluated with Box & Blocks Test (BBT) and shortened Unified Parkinson’s disease rating scale (UPDRS) before and after the training. Mack-Skilling non-parametrical statistical test was used to identify statistically significant differences (p < 0.05) and Cohen’s U3 test to find the effect sizes. Results Participants in the 3D group demonstrated statistically significant and substantially better performance in average time of manipulation (group x time, p = 0.009), number of successfully placed cubes (group x time, p = 0.028), average tremor (group x time, p = 0.002) and UPDRS for upper limb (U3 = 0.35). The LCD and 3D groups substantially improved their BBT score with training (U3 = 0.7, U3 = 0.6, respectively). However, there were no statistically significant differences in clinical tests between the groups (group x time, p = 0.2189, p = 0.2850, respectively). In addition the LCD group significantly decreased the pressure/tension (U3 = 0.3), the 3D did not show changes (U3 = 0.5) and the differences between the groups were statistically different (p = 0.037). The 3D group demonstrated important increase in effort (U3 = 0.75) and perceived competences (U3 = 0.9). Conclusions The outcomes of the study demonstrated that the immersive 3D technology may bring increased interests/enjoyment score resulting in faster and more efficient functional performance. But the 2D technology demonstrated lower pressure/tension score providing similar clinical progress. A study with much larger sample size may also confirm the clinical effectiveness of the approaches. Trial registration The small scale randomized pilot study has been registered at ClinicalTrials.gov Identifier: NCT03515746, 4 May 2018

scholarly journals Data integration by two-sensors in a LEAP-based Virtual Glove for human-system interaction

2021 ◽  
Author(s):  
Giuseppe Placidi ◽  
Danilo Avola ◽  
Luigi Cinque ◽  
Matteo Polsinelli ◽  
Eleni Theodoridou ◽  
...  
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Vision System ◽  
Hand Tracking ◽  
Selection Strategy ◽  
Motion Sensors ◽  
Leap Motion ◽  
Time Data ◽  
Lower Velocity ◽  
Human System

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.

3D word spotting using leap motion sensor

2021 ◽  
Author(s):  
Partha Pratim Roy ◽  
Pradeep Kumar ◽  
Shweta Patidar ◽  
Rajkumar Saini
Keyword(s):  
Motion Sensor ◽  
Leap Motion ◽  
Word Spotting

scholarly journals Quantifying Habitual Physical Activity and Sedentariness in Older Adults—Different Outcomes of Two Simultaneously Body-Worn Motion Sensor Approaches and a Self-Estimation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1877
Author(s):  
Rieke Trumpf ◽  
Wiebren Zijlstra ◽  
Peter Haussermann ◽  
Tim Fleiner
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Community Dwelling ◽  
Habitual Physical Activity ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Healthy Older Adults ◽  
Lower Back ◽  
Hybrid Motion ◽  
Accurate Quantification

Applicable and accurate assessment methods are required for a clinically relevant quantification of habitual physical activity (PA) levels and sedentariness in older adults. The aim of this study is to compare habitual PA and sedentariness, as assessed with (1) a wrist-worn actigraph, (2) a hybrid motion sensor attached to the lower back, and (3) a self-estimation based on a questionnaire. Over the course of one week, PA of 58 community-dwelling subjectively healthy older adults was recorded. The results indicate that actigraphy overestimates the PA levels in older adults, whereas sedentariness is underestimated when compared to the hybrid motion sensor approach. Significantly longer durations (hh:mm/day) for all PA intensities were assessed with the actigraph (light: 04:19; moderate to vigorous: 05:08) when compared to the durations (hh:mm/day) that were assessed with the hybrid motion sensor (light: 01:24; moderate to vigorous: 02:21) and the self-estimated durations (hh:mm/day) (light: 02:33; moderate to vigorous: 03:04). Actigraphy-assessed durations of sedentariness (14:32 hh:mm/day) were significantly shorter when compared to the durations assessed with the hybrid motion sensor (20:15 hh:mm/day). Self-estimated duration of light intensity was significantly shorter when compared to the results of the hybrid motion sensor. The results of the present study highlight the importance of an accurate quantification of habitual PA levels and sedentariness in older adults. The use of hybrid motion sensors can offer important insights into the PA levels and PA types (e.g., sitting, lying) and it can increase the knowledge about mobility-related PA and patterns of sedentariness, while actigraphy appears to be not recommendable for this purpose.

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