First, I want you to think about the principles for writing a good introduction of an academic text. Consider things like structure, logical flow, clarity, coherence, preciseness, conciseness, and other relevant aspects of a good academic text.

Then, I want you to read the text below.

Finally, I rewrite the given text, improving the overall writing quality. Try not to add or remove ideas from the text. Your goal is to improve the writing.

TEXT:
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Robotic arms are versatile machines widely used in industries that require precision, speed, and repetition. Their integration has transformed sectors like manufacturing, healthcare, and scientific research, improving efficiency, safety, and innovation. In industrial processes, robotic arms have increased production rates and minimized human error and fatigue. In healthcare, they enable highly precise and minimally invasive surgeries, improving patient outcomes.

Within the broad field of robotics, an emerging subfield is the development of supernumerary robotic limbs (SRLs). Unlike traditional robotic arms, SRLs aim to augment human capabilities by providing users with additional limbs that function as extensions of the user's body, working in tandem with natural limbs. SRLs can be worn or attached to the body and can operate autonomously or be controlled to synchronize with natural movements. This concept represents a significant leap in human-machine interaction, moving beyond the mere automation of tasks to the possibility of extending human bodily functions. For example, SRLs can help users perform tasks that would be difficult with natural limbs alone, such as holding an object with a robotic arm while manipulating it with both natural hands.

The use of SRLs could offer significant benefits, such as reducing physical strain and the risk of injury. By distributing the workload across multiple limbs, SRLs may significantly reduce the fatigue experienced by users during repetitive or physically demanding tasks. This distribution of effort can be particularly beneficial in industries where workers are at risk of developing musculoskeletal disorders due to repetitive motion or heavy lifting. Moreover, SRLs might provide enhanced capabilities for individuals with disabilities or limited mobility. In such cases, SRLs have the potential to compensate for impaired or missing limbs, providing these individuals with greater independence and an improved quality of life. Thus, SRLs hold promise not only for augmenting the capabilities of able-bodied individuals, but also for helping those with physical limitations.

Despite these promising advantages, several challenges must be addressed to make SRLs a viable and practical solution for widespread use. A major challenge is how to control these additional limbs without cognitively overwhelming the user. The control interface must be intuitive, allowing users to operate SRLs naturally without experiencing the cognitive overload that could result from trying to control multiple limbs simultaneously. Designing a control system that is responsive and easy to use is critical to ensuring that SRLs can be effectively integrated into the user's natural movements. In addition to the cognitive challenges, achieving seamless coordination between natural and robotic limbs may require precise timing and control mechanisms. Any delay or misalignment in the movements could hinder the effectiveness of the SRL and hinder the user's ability to perform tasks smoothly. Therefore, the development of robust algorithms and control systems capable of real-time adjustments is essential to ensure that SRLs can perform tasks in harmony with the user's body.

To address these challenges of intuitive control and seamless coordination, myoelectric control systems offer an interesting solution. Myoelectric control, which relies on the electrical signals generated during muscle contractions, has the potential to provide a natural interface for controlling supernumerary robotic limbs. By capturing the user’s muscle activity through surface electromyography (EMG) sensors, these systems can interpret the user’s motor intent in real time and translate it into movements of the robotic limb. This approach offers the advantage of enabling the user to control the SRL using familiar muscle movements, rather than relying on external control devices, possibly reducing the cognitive load associated with operating additional limbs.

Nowadays, machine learning methods are extensively employed to derive motor intentions from EMG signals [2]. In terms of the algorithms used, extracting motor intentions can be framed as either a classification or a regression task. Classification tasks involve approximating a mapping between the EMG signals and a set of discrete motion classes, such as in hand gesture recognition [4]. In contrast, regression tasks entail finding a mapping from EMG signals to continuous motor-related outputs, such as force, joint angle, or angular acceleration [5], [6]. Traditionally, myoelectric systems have predominantly used the feature engineering framework, in which features manually computed from EMG time segments serve as input data for algorithms like linear discriminant analysis for classification [7] or multilayer perceptron for regression [6]. More recently, however, deep learning models operating within the feature learning framework have gained popularity in the field while achieving state-of-the-art performances [8]. This approach allows models to directly extract relevant features from EMG data. Among these deep learning models, convolutional neural networks are among the most commonly studied for myoelectric control [8]–[10], with successful applications in both classification (e.g., [4]) and regression tasks (e.g., [5]).

While myoelectric control has been widely explored for traditional robotic prosthetics, its application in SRL is still in its infancy. Although few studies have specifically explored myoelectric control for SRLs, early research has shown that EMG-based systems may indeed provide intuitive and responsive control for these devices. However, its performance may depend on the accurate acquisition and analysis of the signals. Perhaps, a key challenge in SRL control is addressing the variability of EMG signals, which can be influenced by factors such as muscle fatigue, shifts in body position, and changes in muscle morphology during motion. These variabilities can impact the accuracy and reliability of EMG-based systems, making it essential to develop algorithms that can adapt to these dynamic conditions. Future research is likely to focus on improving myoelectric control through advanced signal processing techniques and robustness or adaptive machine learning models that can compensate for these variations in real time.
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