A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models

Gopal, Pranesh; Gesta, Amandine; Mohebbi, Abolfazl

doi:10.3390/s22103650

Cited by 23 publications

(15 citation statements)

References 29 publications

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“…In line with related work, we noticed their assistive and companionship purposes. The former concerns upper-limb gesture recognition to help users with ADL [ 69 ]. The latter is demonstrated by interacting with older adults to prevent social isolation and mediating between the older adult, the environment, and the AAL system [ 144 ].…”

Section: Resultsmentioning

confidence: 99%

“…Mobile technologies are convenient to use (ie, market availability, affordability, and wide adoption) at the application level as lifestyle applications for health and well-being [ 65 ] and at the device level as a platform with integrated sensors [ 53 ]. Robots appeared as either assistive devices helping users carry out their activities [ 69 ] or companions for pleasurable activities [ 144 ].…”

Section: Discussionmentioning

confidence: 99%

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Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Jovanovic¹,

Mitrov²,

Zdravevski³

et al. 2022

J Med Internet Res

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Background Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Jovanovic¹,

Mitrov²,

Zdravevski³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

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“…A disabled [109]. An EMG gesture recognition system [110]. Humanmachine interaction system based on EOG and temporalis EMG [111].…”

Section: Applicationmentioning

confidence: 99%

“…Application of FMG, EMG and EIT.From the top center picture, in a clockwise order: bionic control based on EIT, EMG and FMG[109]. An EMG gesture recognition system[110]. Humanmachine interaction system based on EOG and temporalis EMG[111].…”

mentioning

confidence: 99%

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Zheng

Zhao

et al. 2022

Biosensors

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Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

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“…F OR decades, electromyography (EMG) signals recorded from the upper forearm have been used to control prosthetic devices for transradial amputees [1]- [3]. With increasingly rapid advancements in wearable technologies, there is a growing demand for reliable yet unobtrusive control interfaces for general consumers as well.…”

Section: Introductionmentioning

confidence: 99%

Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition

2022

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Wrist electromyography (EMG) signals have been explored for incorporation into subtle wrist-worn wearable devices for decoding hand gestures. Previous studies have now shown that wrist EMG can even outperform the more commonly used forearm EMG, depending on the application. However, the performance and robustness of wrist EMG-based pattern recognition systems in the presence of confounding factors remain relatively unexplored. In this paper, we investigate the day-to-day stability of wrist EMG signals to ascertain their reliability across days. The test-retest reliability of concurrently collected wrist EMG and forearm EMG signals elicited during a variety of finger and wrist gestures was evaluated over a series of days. Several classification approaches, including a novel Maximum independence domain adaptation (MIDA), were investigated to explore and mitigate the effects of natural EMG variations across days. Results showed that wrist EMG signals were reliable and relatively resilient to the negative effects of EMG variations across days. Specifically, wrist EMG-based classifiers consistently outperformed forearm EMG-based classifiers with statistically significant differences (p < 0.05) and had average classification accuracies between 93.8% − 95.7% compared to 91.3% − 92.6% for the forearm EMG-based classifiers using a novel Inter-Day Feature Set (IDFS) and a novel adaptive-MIDA linear discriminant analysis (LDA) classification technique requiring minimal training. This study builds further evidence for the viability of commercial wrist-worn EMG wearables with minimal training burden for general consumers.

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A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models

Cited by 23 publications

References 29 publications

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition

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