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Abstract Objective. Advanced robotic lower limb prostheses are mainly controlled autonomously. Although the existing control can assist cyclic movements during locomotion of amputee users, the function of these modern devices is still limited due to the lack of neuromuscular control (i.e. control based on human efferent neural signals from the central nervous system to peripheral muscles for movement production). Neuromuscular control signals can be recorded from muscles, called electromyographic (EMG) or myoelectric signals. In fact, using EMG signals for robotic lower limb prostheses control has been an emerging research topic in the field for the past decade to address novel prosthesis functionality and adaptability to different environments and task contexts. The objective of this paper is to review robotic lower limb Prosthesis control via EMG signals recorded from residual muscles in individuals with lower limb amputations. Approach. We performed a literature review on surgical techniques for enhanced EMG interfaces, EMG sensors, decoding algorithms, and control paradigms for robotic lower limb prostheses. Main results. This review highlights the promise of EMG control for enabling new functionalities in robotic lower limb prostheses, as well as the existing challenges, knowledge gaps, and opportunities on this research topic from human motor control and clinical practice perspectives. Significance. This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.
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A database of upper limb surface electromyogram signals from demographically diverse individuals
Abstract Upper limb based neuromuscular interfaces aim to provide a seamless way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (EMG) signals hold significant promise. However, their sensitivity to physiological and anatomical factors remains poorly understood, raising questions about how these factors influence gesture decoding across individuals or groups. To facilitate the study of signal distribution shifts across individuals or groups of individuals, we present a dataset of upper limb EMG signals and physiological measures from 91 demographically diverse adults. Participants were selected to represent a range of ages (18 to 92 years) and body mass indices (healthy, overweight, and obese). The dataset also includes measures such as skin hydration and elasticity, which may affect EMG signals. This dataset provides a basis to study demographic confounds in EMG signals and serves as a benchmark to test the development of fair and unbiased algorithms that enable accurate hand gesture decoding across demographically diverse subjects. Additionally, we validate the quality of the collected data using state-of-the-art gesture decoding techniques.
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- Award ID(s):
- 2152260
- PAR ID:
- 10579350
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Tigrini, Andrea (Ed.)Hand gesture classification is crucial for the control of many modern technologies, ranging from virtual and augmented reality systems to assistive mechatronic devices. A prominent control technique employs surface electromyography (EMG) and pattern recognition algorithms to identify specific patterns in muscle electrical activity and translate these to device commands. While being well established in consumer, clinical, and research applications, this technique suffers from misclassification errors caused by limb movements and the weight of manipulated objects, both vital aspects of how we use our hands in daily life. An emerging alternative control technique is force myography (FMG) which uses pattern recognition algorithms to predict hand gestures from the axial forces present at the skin’s surface created by contractions of the underlying muscles. As EMG and FMG capture different physiological signals associated with muscle contraction, we hypothesized that each may offer unique additional information for gesture classification, potentially improving classification accuracy in the presence of limb position and object loading effects. Thus, we tested the effect of limb position and grasped load on 3 different sensing modalities: EMG, FMG, and the fused combination of the two. 27 able-bodied participants performed a grasp and release task with 4 hand gestures at 8 positions and under 5 object weight conditions. We then examined the effects of limb position and grasped load on gesture classification accuracy across each sensing modality. It was found that position and grasped load had statistically significant effects on the classification performance of the 3 sensing modalities and that the combination of EMG and FMG provided the highest classification accuracy of hand gesture, limb position, and grasped load combinations (97.34%) followed by FMG (92.27%) and then EMG (82.84%). This points to the fact that the addition of FMG to traditional EMG control systems offers unique additional data for more effective device control and can help accommodate different limb positions and grasped object loads.more » « less
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