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The dexterity and coordinated movement of the hand play a very important role in people’s daily life and interpersonal communication. For patients with hand amputation, there are many inconveniences in life. However, prosthetic limb can help them to carry out daily life in accordance with their own intention to meet the basic grasp and interpersona...
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Context 1
... (ADC), which meets the needs of multi-channel EMG signal acquisition and is equipped with a lithium battery. The signal input port uses a standard 20pin-JTAG port, which improves compatibility and versatility. The board is also equipped with a Micro-SD card slot, which can store the collected data in the SD card for later or offline analysis. Figure. 2 The hardware circuit relies on the control of the USR-C322 chip to make each functional part work in sequence. It communicates with the ADS1299 and SD card through SPI to transmit and collect data; it communicates with the battery monitoring chip LTC2942 through the I2C bus; and the entire circuit is built in the USR-C322. The WIFI ...
Context 2
... the sake of portability and easy operation, the wearable hardware acquisition device is as simple as possible to simplify the scale of the hardware circuit. The appearance is shown in Figure 2. The signal acquisition device is a rectangular box with a size of 6.4cm * 3.7cm * 1.7cm, with the built-in signal acquisition board and the 3.7V Li-ion battery. ...
Citations
... However, the inclusion of the Deep Learning algorithm LSTM has provided a much better accuracy than XGBoost. Table V shows that LSTM provided the maximum accuracy compared to the 12 research work that is cited [10] - [13] , [15] - [16] , [25] - [26] , [36] - [39]. ...
In this paper four Machine Learning (ML) Algorithms have been implemented for the classification of four hand gestures using electromyography (EMG) dataset. The classifiers opted are Support Vector Machine (SVM), Random Forest (RF), Bagged tree and Extreme Gadient Boosting (XGBoost). The prediction accuracy of the machine learning algorithms were subsequently compared with Long Short-Term Memory (LSTM) which is a Deep learning based classification technique. Among the machine learning algorithms, XGBoost provided the highest accuracy of approximately 97% while LSTM provided a superior accuracy close to 99% which promises to provide the physiologically natural upper-limb movement control. In addition to the pursuit of improved accuracy in the research, the effect of removing the noisiest channel in the accuracy of the algorithms has been examined in order to decrease the volume of data processing.
The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.
