Game-Based Myoelectric Training

Tabor, Aaron; Bateman, Scott; Scheme, Erik

doi:10.1145/2968120.2987731

Cited by 14 publications

(15 citation statements)

References 13 publications

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“…These EMG armbands have unlocked new possibilities for myoelectric control applications, which are not limited to the more traditional prosthetics market. Applications have included accessible game-based training for myoelectric prostheses [ 5 , 6 ], sport training systems [ 7 ], portable music player interfaces [ 3 ], sign language interpretation [ 8 ], and user authentication [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

Phinyomark

Khushaba

Scheme

2018

Sensors

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239

164

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Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly (p< 0.05) from 2% to 56% depending on the evaluated features when using the lower sampling rate, and especially for transradial amputee subjects. Importantly, for these subjects, no number of existing features can be combined to compensate for this loss in higher-frequency content. From these results, we identify two new sets of recommended EMG features (along with a novel feature, L-scale) that provide better performance for these emerging low-sampling rate systems.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

Phinyomark

Khushaba

Scheme

2018

Sensors

Self Cite

239

164

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“…In particular, this work considers the situation where several days have elapsed between each recording session. Such a setting naturally arises when sEMG-based gesture recognition is used for video games, artistic performances or, simply, to control devices of sporadic use [5], [13], [14]. In contrast to within-day or even day-to-day adaptation, this work's setting is especially challenging as the change in the signal between two sessions is expected to be substantially greater and no intermediary data is available to bridge this gap.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

et al. 2020

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Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art selfcalibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

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“…Throughout the design of our game, we have kept these concepts in mind, and we highlight below how our game incorporates these elements. Further information about our game can be found in [35,36].…”

Section: Game Design Principlesmentioning

confidence: 99%

“…To facilitate the exploration of myo training, we designed and implemented a training game [35,36]. This game was shown to participants during playtest-interview sessions (introduced below) and served as a concrete example to stimulate feedback and discussion.…”

Section: Designing a Myoelectric Training Gamementioning

confidence: 99%

“…Our initial design, The Falling of Momo ( Figure 3) [35,36], is a casual, survival-style game, where players earn points by descending through a series of rising platforms to avoid being squished by spikes across the top of the screen. Players collect coins that can later be used to purchase new characters, themes and unlockables.…”

Section: Initial Game Design: the Falling Of Momomentioning

confidence: 99%

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Designing Game-Based Myoelectric Prosthesis Training

Tabor

Bateman

Scheme

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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A myoelectric prosthesis (myo) is a dexterous artificial limb controlled by muscle contractions. Learning to use a myo can be challenging, so extensive training is often required to use a myo prosthesis effectively. Signal visualizations and simple muscle-controlled games are currently used to help patients train their muscles, but are boring and frustrating. Furthermore, current training systems require expensive medical equipment and clinician oversight, restricting training to infrequent clinical visits. To address these limitations, we developed a new game that promotes fun and success, and shows the viability of a low-cost myoelectric input device. We adapted a user-centered design (UCD) process to receive feedback from patients, clinicians, and family members as we iteratively addressed challenges to improve our game. Through this work, we introduce a free and open myo training game, provide new information about the design of myo training games, and reflect on an adapted UCD process for the practical iterative development of therapeutic games.

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Game-Based Myoelectric Training

Cited by 14 publications

References 13 publications

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

Designing Game-Based Myoelectric Prosthesis Training

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