A virtual upper limb prosthesis as a training system

Barraza-Madrigal, José A.; Ramirez-Garcia, A.; Roberto, Munoz-Guerrero

doi:10.1109/iceee.2010.5608586

Cited by 11 publications

(7 citation statements)

References 4 publications

(4 reference statements)

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“…To be able to control virtual prosthesis and to become familiar with a real-time prosthesis, voluntary muscle contraction control is very important; this can be done by using a visual feedback system. The improvement of learning depends on the user and visual feedback system, thus, the feedback system must allow the user to learn new tasks using their muscles [87]. Most of the virtual prosthesis followed the same stages to operate.…”

Section: Real-time Application Of Myoelectric Prosthesismentioning

confidence: 99%

“…Then, the signal is amplified and filtered to acquire the myoelectric signal to be used. The interface between the virtual system and acquisition of myoelectric signal is created, which consists of isolation, pre-processing of the signal in the hardware, personal computer (PC) communication, communication between PC and MATLAB, processing in software and communication between MATLAB and virtual world [87] (part of MATLAB). The general idea followed by most of the virtual prosthesis is shown in Figure 4.…”

Section: Real-time Application Of Myoelectric Prosthesismentioning

confidence: 99%

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Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Parajuli

Sreenivasan

Bifulco³

et al. 2019

Sensors

228

128

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Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

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Section: Real-time Application Of Myoelectric Prosthesismentioning

confidence: 99%

Section: Real-time Application Of Myoelectric Prosthesismentioning

confidence: 99%

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Parajuli

Sreenivasan

Bifulco³

et al. 2019

Sensors

228

128

View full text Add to dashboard Cite

show abstract

“…The use of interactive technology can foster intrinsic motivation and thus the effort invested in the training of neuromuscular rehabilitation [ 5 ]. Various computer-based systems, including video games, have been suggested [ 6 - 9 ] to support motor training. Physiological data suggest that gaming can cause neuroplastic reorganizing, leading to the long-term retention and transfer of skills; however, further clinical research is needed in this field [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Video Game–Based Rehabilitation Approach for Individuals Who Have Undergone Upper Limb Amputation: Case-Control Study

Hashim¹,

Razak²,

Gholizadeh³

et al. 2021

JMIR Serious Games

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Background Brain plasticity is an important factor in prosthesis usage. This plasticity helps with brain adaptation to learn new movement and coordination patterns needed to control a prosthetic hand. It can be achieved through repetitive muscle training that is usually very exhausting and often results in considerable reduction in patient motivation. Previous studies have shown that a playful concept in rehabilitation can increase patient engagement and perseverance. Objective This study investigated whether the inclusion of video games in the upper limb amputee rehabilitation protocol could have a beneficial impact for muscle preparation, coordination, and patient motivation among individuals who have undergone transradial upper limb amputation. Methods Ten participants, including five amputee participants and five able-bodied participants, were enrolled in 10 1-hour sessions within a 4-week rehabilitation program. In order to investigate the effects of the rehabilitation protocol used in this study, virtual reality box and block tests and electromyography (EMG) assessments were performed. Maximum voluntary contraction was measured before, immediately after, and 2 days after interacting with four different EMG-controlled video games. Participant motivation was assessed with the Intrinsic Motivation Inventory (IMI) questionnaire and user evaluation survey. Results Survey analysis showed that muscle strength and coordination increased at the end of training for all the participants. The results of Pearson correlation analysis indicated that there was a significant positive association between the training period and the box and block test score (r8=0.95, P<.001). The maximum voluntary contraction increment was high before training (6.8%) and in the follow-up session (7.1%), but was very small (2.1%) shortly after the training was conducted. The IMI assessment showed high scores for the subscales of interest, perceived competence, choice, and usefulness, but low scores for pressure and tension. Conclusions This study demonstrated that video games enhance motivation and adherence in an upper limb amputee rehabilitation program. The use of video games could be seen as a complementary approach for physical training in upper limb amputee rehabilitation.

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“…VR has been used in the prosthetic field as a prosthetic training and assessment tool to monitor amputee performance [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], alternative treatment for phantom limb pain [36][37][38][39][40][41][42][43][44], and virtual prototyping and testing of new prosthetics hand control system [45][46][47][48]. This medium has demonstrated its efficiency in improving the traditional method of training and makes it easy for people to learn [13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Conventional and Virtual Reality Box and Blocks Tests in Upper Limb Amputees: A Case-Control Study

2021

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Previous studies have demonstrated the potential of virtual reality as an effective teaching tool for motor training. Despite the growing interest in this field, only a few studies have determined the validity of the tasks made in virtual environments to real physical environments. This case-control study compares the score of pick and place activity in the real and virtual environment by using the box and blocks test setup on 4 able-bodied and 4 transradial amputees (2 myoelectric prosthetic users and 2 nonprosthetic users). This study integrates the traditional Box and Blocks Test mechanics into gameplay by using the Leap Motion controller and Oculus Rift headset. The participants were instructed to complete the test in both environments randomly for 10 sessions, with 30 minutes of training in each session. Pearson's correlation interpretation was conducted to investigate the relation between the test's score with the training duration, also the score obtained in the real and virtual environment. Independent samples t-test was also carried out to compare the score from the different test environments. All participants showed a greater percentage change of test score in the virtual version and better performance was achieved with increasing training duration. Both environments were positively correlated. However, there was a significant difference in the test score obtained in the real and virtual environment for able-bodied t(9)=18.19, p<0.05 and myoelectric prosthetic user t(9)=4.51, p<0.05, but not for the non-prosthetic user. This study has demonstrated that two different environments showed significantly comparable results in pick and place tasks by individuals with different abilities.

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A virtual upper limb prosthesis as a training system

Cited by 11 publications

References 4 publications

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Video Game–Based Rehabilitation Approach for Individuals Who Have Undergone Upper Limb Amputation: Case-Control Study

Comparison of Conventional and Virtual Reality Box and Blocks Tests in Upper Limb Amputees: A Case-Control Study

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