14.4 Applications in prosthetics and rehabilitation

EMG signals are revolutionizing prosthetic control and rehabilitation. By detecting muscle activation patterns, these signals enable intuitive control of prosthetic devices, allowing users to perform various tasks like grasping objects and manipulating tools.

EMG-based control strategies involve signal acquisition, preprocessing, and pattern recognition. Challenges include signal variability and user adaptation. Future trends point towards advanced sensing technologies, adaptive algorithms, and integration with other modalities for improved functionality and user experience.

EMG in Prosthetics and Rehabilitation

EMG signals for prosthetic control

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  Frontiers | Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft ... View original

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  Frontiers | Control of Leg Movements Driven by EMG Activity of Shoulder Muscles | Frontiers in ... View original

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  Frontiers | An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic ... View original

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  Frontiers | Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft ... View original

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Top images from around the web for EMG signals for prosthetic control

• 

  Frontiers | An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic ... View original

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• 

  Frontiers | Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft ... View original

  Is this image relevant?

• 

  Frontiers | Control of Leg Movements Driven by EMG Activity of Shoulder Muscles | Frontiers in ... View original

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• 

  Frontiers | An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic ... View original

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• 

  Frontiers | Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft ... View original

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• EMG signals establish a direct interface between the user's muscles and the prosthetic device enabling intuitive control based on the user's intended movements (hand gestures, arm movements)
• EMG signals detect muscle activation patterns which are then translated into control commands for the prosthetic device (open/close hand, flex/extend elbow)
• EMG-based control allows users to perform various tasks with their prosthetic devices such as grasping objects, reaching for items, and manipulating tools (picking up a cup, turning a doorknob)
• EMG signals can be utilized in assistive technologies for individuals with limited mobility to control wheelchairs, operate computer interfaces, and manage environmental control systems (adjusting thermostat, turning lights on/off)

EMG-based control strategies

• Signal acquisition and preprocessing involves placing EMG electrodes on specific muscle groups, amplifying the raw EMG signals, filtering out noise, and digitizing the signals for further processing (biceps, triceps, forearm muscles)
• Feature extraction and pattern recognition techniques are applied to the preprocessed EMG signals:
  • Time-domain features calculate metrics such as Mean Absolute Value (MAV), Root Mean Square (RMS), and Zero Crossings (ZC) to quantify the EMG signal characteristics
  • Frequency-domain features like Mean Frequency (MNF) and Median Frequency (MDF) analyze the spectral content of the EMG signals
  • Machine learning algorithms including Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN) are used for classifying EMG patterns into distinct control commands
• Control strategies for prosthetic devices include:
  1. On/off control: Simple threshold-based activation of prosthetic functions where EMG signals above a certain threshold trigger a specific action (hand open/close)
  2. Proportional control: Continuous control of prosthetic movements based on the amplitude of the EMG signals, allowing for more fine-grained control (wrist rotation speed)
  3. Pattern recognition control: Identification of specific muscle activation patterns associated with different prosthetic functions, enabling more intuitive and natural control (different hand grips)
• Feedback mechanisms provide information to the user about the state and performance of the prosthetic device through visual, auditory, or tactile cues (LED indicators, beeps, vibrations)
  • Closed-loop control incorporates sensory feedback to improve user experience and control accuracy by adjusting the prosthetic device's behavior based on the user's actions and the environment (force feedback, slip detection)

Challenges of EMG-based control

• Signal variability and robustness pose challenges as EMG signals can be affected by factors such as muscle fatigue, electrode displacement, and skin conditions (sweating, dryness)
  • Robust signal processing and control algorithms are needed to handle signal variability and maintain reliable control performance
• Donning and doffing of prosthetic devices requires consistent electrode placement for reliable EMG signal acquisition, which can be challenging during daily use (alignment marks, snap connectors)
• Computational complexity and real-time processing requirements demand EMG-based control algorithms to operate with minimal latency while balancing the trade-off between computational complexity and control performance (embedded systems, optimization techniques)
• User adaptation and learning curve necessitate training and practice for users to effectively control their prosthetic devices using EMG signals
  • User-centered design and individualized control strategies are important to accommodate different user preferences and capabilities (customizable control parameters, adaptive algorithms)

Future trends in EMG processing

• Intramuscular EMG and high-density EMG arrays offer improved spatial resolution and selectivity in EMG signal acquisition, enabling more advanced control strategies and increased functionality (targeted muscle control, fine motor skills)
• Adaptive and self-calibrating control systems incorporate algorithms that automatically adapt to changes in EMG signal characteristics over time, reducing the need for manual recalibration and improving long-term performance (machine learning, online adaptation)
• Integration with other sensing modalities such as inertial measurement units (IMUs), force sensors, and computer vision enhances context awareness and control accuracy by fusing complementary information (gesture recognition, object detection)
• Advances in machine learning and artificial intelligence, particularly deep learning algorithms, enable more sophisticated EMG pattern recognition and personalized control strategies based on individual user characteristics and preferences (transfer learning, reinforcement learning)
• Wireless and wearable EMG systems with miniaturized, low-power, and wireless EMG sensors allow for unobtrusive and continuous monitoring, opening up possibilities for home-based rehabilitation and remote monitoring applications (smart textiles, IoT devices)