Implementing Hand Gesture Recognition Using EMG on the Zynq Circuit

Kerdjidj, Oussama; Amara, Kahina; Harizi, Farid; Boumridja, H.

doi:10.1109/jsen.2023.3259150

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…8 https://greenwaves-technologies.com/manuals/BUILD/HOME/html/ index.html 9 https://greenwaves-technologies.com/product/gap9 evk-gap9-evaluationkit-efused/ 10 https://greenwaves-technologies.com/product/gap9-resources/ 11 https://www.nordicsemi.com/Products/Development-hardware/Power-Profiler-Kit-2…”

Section: Resultsmentioning

confidence: 99%

Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Zanghieri,

Rapa,

Orlandi

et al. 2024

IEEE Sensors J.

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parallelism shortens latency Real-time embedded force estimation • accurate & all-fingers • robust cross-day High-Density sEMG Spike trains parallel ultra-low power MCU LIF Neurons 𝐝𝒙 𝐝𝒕 = 𝒙𝐝𝐫𝐢𝐯𝐞 − 𝒙 𝝉 deployment of selected LIFs' dynamics and fitted inference heuristic feature selection reduces computation workload fit of linear readout Abstract-Modeling hand kinematics and dynamics is a key goal for research on Human-Machine Interfaces, with surface electromyography (sEMG) being the most commonly used sensing modality. Though underresearched, sEMG regression-based modeling of hand movements and forces is promising for finer control than allowed by mapping to fixed gestures. We present an event-based sEMG encoding for multi-finger force estimation implemented on a microcontroller unit (MCU). We are the first to target the HYSER High-Density (HD)-sEMG dataset in multi-day conditions closest to a real scenario without a fixed force pattern. Our Mean Absolute Error of (8.4 ± 2.8)% of the Maximum Voluntary Contraction (MVC) is on par with State-of-the-Art (SoA) works on easier settings such as within-day, single-finger, or fixed-exercise. We deploy our solution for HYSER's hardest task on a parallel ultra-low power MCU, getting an energy consumption below 6.5 uJ per sample, 2.8× to 11× more energy-efficient than SoA single-core solutions, and a latency below 280 us per sample, shorter than HYSER's HD-sEMG sampling period, thus compatible with real-time operation on embedded devices.

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Section: Resultsmentioning

confidence: 99%

Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Zanghieri,

Rapa,

Orlandi

et al. 2024

IEEE Sensors J.

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“…x 2 +y 2 2σ 2 (8) with σ being the Gaussian standard deviation. For σ equal to 1.4, the convolution kernel takes the form shown in Figure 1d.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, if the traditional development approach of FPGAs using low-level hardware languages (such as Verilog and VHDL) is usually time-consuming and very inefficient, the use of high-level language synthesis (HLS) tools allows developers to program hardware solutions using C/C++ and OpenCL. This significantly improves the efficiency in FPGA developments [7,8]. Finally, FPGAs are nowadays integrated in multi-processor system-on-chips (MPSoC), in which computer and embedded logic elements are combined.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics

Ruiz-Beltrán,

Romero-Garcés,

González-García

et al. 2023

Sensors

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One of the main challenges faced by iris recognition systems is to be able to work with people in motion, where the sensor is at an increasing distance (more than 1 m) from the person. The ultimate goal is to make the system less and less intrusive and require less cooperation from the person. When this scenario is implemented using a single static sensor, it will be necessary for the sensor to have a wide field of view and for the system to process a large number of frames per second (fps). In such a scenario, many of the captured eye images will not have adequate quality (contrast or resolution). This paper describes the implementation in an MPSoC (multiprocessor system-on-chip) of an eye image detection system that integrates, in the programmable logic (PL) part, a functional block to evaluate the level of defocus blur of the captured images. In this way, the system will be able to discard images that do not have the required focus quality in the subsequent processing steps. The proposals were successfully designed using Vitis High Level Synthesis (VHLS) and integrated into an eye detection framework capable of processing over 57 fps working with a 16 Mpixel sensor. Using, for validation, an extended version of the CASIA-Iris-distance V4 database, the experimental evaluation shows that the proposed framework is able to successfully discard unfocused eye images. But what is more relevant is that, in a real implementation, this proposal allows discarding up to 97% of out-of-focus eye images, which will not have to be processed by the segmentation and normalised iris pattern extraction blocks.

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“…Other researchers [ 9 ] present a study based on EMG signals acquired from muscles and motion detection through a Human–Machine Interface, designing an upper limb prosthesis using an AI-based controller. In another work [ 10 ], hardware design for hand gesture recognition using EMG is developed and implemented on a Zynq platform, processing the acquired EMG signals with an eight-channel Myo sensor. Furthermore, in [ 11 ], various methods are applied to detect and classify muscle activities using sEMG signals, with ANNs showing the highest accuracy in recognizing movements among and within subjects.…”

Section: Introductionmentioning

confidence: 99%

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

Avilés-Mendoza,

Gaibor-León,

Asanza

et al. 2023

Biomimetics

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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.

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Implementing Hand Gesture Recognition Using EMG on the Zynq Circuit

Cited by 13 publications

References 39 publications

Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics

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

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