Modelling EMG driven wrist movements using a bio-inspired neural network

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition

BACKGROUND

Gesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human-machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications.

METHODS

This study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects.

RESULTS

The results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training.

CONCLUSIONS

The study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.

Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles

Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.

Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals

Bio-signal based hand motion recognition plays a critical role in the tasks of human-machine interaction, such as the natural control of multifunctional prostheses. Although a large number of classification technologies have been taken to improve the motion recognition accuracy, it is still a challenge to achieve acceptable performance for multiple modality input. This study proposes a multi-modality deep forest (MMDF) framework to identify hand motions, in which surface electromyographic signals (sEMG) and acceleration signals (ACC) are fused at the input level. The proposed MMDF framework constitutes of three main stages, sEMG and ACC feature extraction, feature dimension reduction, and a cascade structure deep forest for classification. A public database "Ninapro DB7" is used to evaluate the performance of the proposed framework, and the experimental results show that it can achieve a significantly higher accuracy than that of competitors. Besides, our experimental results also show that MMDF outperforms other traditional classifiers with the input of the single modality of sEMG signals. In sum, this study verifies that ACC signals can be an excellent supplementary for sEMG, and MMDF is a plausible solution to fuse mulit-modality bio-signals for human motion recognition.

Severity Assessment of Cervical Spondylotic Myelopathy Based on Intelligent Video Analysis

Cervical spondylotic myelopathy (CSM) has a high incidence in the middle-aged and elderly people. According to clinical research, there is a connection between hand dexterity and cervical nerves. So the surgeon makes a preliminary assessment of the severity of CSM based on a 10-second grip and release (G&R) test. At present, the statistics of G&R test rely on the surgeon's manual counting. When a patient's hand motion speed is too fast, the surgeon's manual counting is prone to error, leading to potential misdiagnosis. On the other hand, in recent years, artificial intelligence has been developed rapidly, where three-dimensional convolutional neural networks (3D-CNNs) have been widely used in video analysis. This work proposes a hand motion analysis model using a 3D-CNN combined with a de-jittering mechanism to assess the severity of CSM on 10-second G&R videos. We collect 1500 10-second G&R videos recorded by 750 subjects to establish a dataset. The proposed model using 3D-MobileNetV2 as the classifier obtains a Levenshtein accuracy of 97.40% and an average GPU inference time of 3.31 seconds for each 10-second G&R video. Such accuracy and inference speed ensure that the proposed model can be used as a screening examination tool for CSM and a medical assistance tool to help decision making during CSM treatment planning.