Hand postures for evaluating trigger finger using leap motion controller

Chophuk, Ponlawat; Chumpen, S.; Tungjitkusolmun, S.; Phasukkit, Pattarapong

doi:10.1109/bmeicon.2015.7399560

Cited by 20 publications

(18 citation statements)

References 3 publications

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“…Our findings regarding LM-supported movements are in line with other works. 18,19 Similarly, the problems of LM with light conditions are confirmed by others. 9,10 Therefore, the data captured by LM may not be useful for providing objective measures such as patients' range of motion for finger joints.…”

Section: Discussionsupporting

confidence: 65%

“…10 Also, an average difference of 5.73 degrees was identified for finger proximal interphalangeal (PIP) in the neutral position, thumb PIP flexion and radial abduction, and MCP flexion. 19 However, negative results were found for pinkie finger movements 18,19 and MCP flexion of most fingers. 18 Additionally, LM was found to have an accuracy of less than 0.5 mm when tested in a static setting.…”

Section: Lm For Physical Rehabilitationmentioning

confidence: 99%

“…Although this selection is subjective to a therapist's criteria, it is confirmed by other works. 18,19 Although several movements were discarded, the selected ones allow supporting the detection of functional movements such as hand grabs, fingertip touch, and finger oppositions. Due to the limitations of LM, Playtherapy does not provide any measure regarding the range of motion, but it tracks the number of times a patient executed the expected movements.…”

Section: Playtherapy Pregmentioning

confidence: 99%

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Advantages and Limitations of Leap Motion from a Developers', Physical Therapists', and Patients' Perspective

et al. 2020

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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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Section: Discussionsupporting

confidence: 65%

Section: Lm For Physical Rehabilitationmentioning

confidence: 99%

Section: Playtherapy Pregmentioning

confidence: 99%

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Advantages and Limitations of Leap Motion from a Developers', Physical Therapists', and Patients' Perspective

et al. 2020

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“…Different from other vision sensors, the leap motion controller facilitates the direct acquisition of the finger joint angle without additional processing. Although the measured angle from the leap motion controller exhibits differences in the real joint angle, as compared to Chophuk et al [23], we chose the leap motion controller as a ground-truth sensor due to its characteristic and because it can facilitate large dataset generation.…”

Section: Data Acquisitionmentioning

confidence: 99%

Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network

et al. 2020

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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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“…For special purpose systems such as the OsiriX (OsiriX, OsiriX foundation, Switzerland, (accessed on 13 August 2017)) medical image viewer, several plugins with gesture control exist [ 55 , 56 , 57 ]. In particular, the LMC is suitable to evaluate abnormal finger motions of the trigger finger [ 58 , 59 ] or as a diagnostic tool for impaired hand function caused by cervical myelopathy based on a hand grip-and-release test [ 60 ]. The field of applications extends to all aspects of rehabilitation.…”

Section: Applications and Contextsmentioning

confidence: 99%

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Bachmann

Weichert

Rinkenauer

2018

Sensors

118

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Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

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Hand postures for evaluating trigger finger using leap motion controller

Cited by 20 publications

References 3 publications

Advantages and Limitations of Leap Motion from a Developers', Physical Therapists', and Patients' Perspective

Advantages and Limitations of Leap Motion from a Developers', Physical Therapists', and Patients' Perspective

Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

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