Evaluation of Myoelectric Control Learning Using Multi-Session Game-Based Training

Tabor, Aaron; Bateman, Scott; Scheme, Erik

doi:10.1109/tnsre.2018.2855561

Cited by 29 publications

(34 citation statements)

References 30 publications

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“…Most notably, the armband is limited to a sampling rate of 200 sps with 8-bit precision and comprised of only 8 channels. Regardless of these limitations, the Myo Armband has been widely utilized in a wide array of research topics (e.g., robotic arm control [2], video game control [13], motor imagery [14], and sign language recognition [15]).…”

Section: Overview Of Surface Emg Acquisition Systemsmentioning

confidence: 99%

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

Côté-Allard

Gagnon-Turcotte

Laviolette

et al. 2019

Sensors

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Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost (∼150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly ( p < 0.05 ) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository.

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Section: Overview Of Surface Emg Acquisition Systemsmentioning

confidence: 99%

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

Côté-Allard

Gagnon-Turcotte

Laviolette

et al. 2019

Sensors

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“…There is recent research that analyses myoelectric games to facilitate the transfer to a robotic prosthesis [16,18]. However, we were not able to find the literature where myoelectric games are tested with a homogeneous group of patients with SCI with no limb amputation.…”

Section: Resultsmentioning

confidence: 83%

“…The myoelectric gaming approach looks very suitable for these cases and research papers can be found describing real-time myoelectric systems with feedback [17]. However, the effectiveness of myoelectric training for adaptation to robotic prostheses can take time and is still under study [18].…”

Section: Discussionmentioning

confidence: 99%

Myoelectric Gaming in the Rehabilitation of Patients with C7 Spinal Cord Injury

et al. 2019

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This paper analyses the role of myoelectric games in the rehabilitation of paraplegic patients. The University of Valladolid neuromuscular training system, UVa-NTS platform, which allows the myoelectric command of computer applications, has been introduced in rehabilitation sessions of a group of paraplegic patients. The experiments took place both at the University of Valladolid and at the National Hospital for Paraplegics of Toledo in Spain. A homogeneous population of five patients with a C7 spinal cord injury was compared with a group of control subjects. The myoelectric control was performed with the flexor carpi radialis and the extensor carpi radialis muscles. The myoelectric routines were timed and the game scores measured. Notwithstanding the reduced mobility of the patients, they achieved fast adaptation and better timings than the control subjects in the first experiment (p < 0.001), although this difference was reduced in further experiments. Both patients and control subjects played satisfactorily with the Myo-Pong game. However, the improvement in the scores was better for the control subjects between sessions (p = 0.009) when compared with the patients (p = 0.978). The results show that patients and control subjects were able to perform and reached similar scores. However, patients’ improvement in further rehabilitation sessions was lesser than when compared with the control subjects.

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“…The topic of training with regards to goals was fairly unanimous between the limb different participants and the researchers. In current research, it appears to have mostly been assumed that an improvement in game control would translate readily to an improvement in prosthetic control as only in-game improvement or abstract control has been measured by most studies [4,6,8,12–14,20,30,31]. The question whether this transfer can happen and with which type of game this might happen has yet to be answered, as current research challenges the idea that a general myocontrol skill exists [10,32].…”

Section: Trainingmentioning

confidence: 99%

Perception of game-based rehabilitation in upper-limb prosthetic training: a survey of users and researchers

Garske

Dyson

Dupan

et al. 2020

Preprint

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Background: Serious games have been investigated for their use in multiple forms of rehabilitation for decades. The rising trend to use games for physical fitness in more recent years has also provided more options and garnered more interest for their use in physical rehabilitation and motor learning. In this paper, we report the results of an opinion survey of serious games in upper limb prosthetic training. Objective: This study investigates and contrasts the expectations and preferences for game based prosthetic rehabilitation of people with limb difference and researchers. Methods: Both participant groups answered open and closed questions as well as a questionnaire to assess their user types. The distribution of the user types was compared with a Pearson′s χ2 test against a sample population. The data was analysed with the thematic framework method; answers fell within the themes of usability, training, and game design. Researchers shared their views on current challenges and what could be done to tackle these. Results: A total of 14 people with limb difference and 12 researchers took part in this survey. The open questions resulted in an overview of the different views on prosthetic training games between the groups. The user types of people with limb difference and researchers were both significantly different from the sample population with χ2=12.31 and χ2=26.50, respectively. Conclusions: We found that the respondents show a general willingness and tentative optimism towards the topic, but also acknowledge hurdles limiting the adoption of these games by both clinics and users. Results indicate a noteworthy difference between researchers and limb different people in their game preferences, which could lead to design choices that do not represent the target audience. Furthermore, focus on long-term in-home experiments is expected to shed more light onto the validity of games in upper limb prosthetic rehabilitation.

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Evaluation of Myoelectric Control Learning Using Multi-Session Game-Based Training

Cited by 29 publications

References 30 publications

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

Myoelectric Gaming in the Rehabilitation of Patients with C7 Spinal Cord Injury

Perception of game-based rehabilitation in upper-limb prosthetic training: a survey of users and researchers

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