Artificial Neural Network to Detect Human Hand Gestures for a Robotic Arm Control

Schabron, Bridget; Alashqar, Zaid; Fuhrman, Nicole; Jibbe, Khaled; Desai, Jaydip

doi:10.1109/embc.2019.8857264

Cited by 13 publications

(2 citation statements)

References 12 publications

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“…Because of these abilities, ANN has been applied to different machine learning tasks such as time-series prediction [ 64 ] and computer vision task [ 65 , 66 ], and has produced reliable results. In the COVID-19 prediction task, several works using the ANN structure have yielded excellent results [ 67 , 68 , 69 ].…”

Section: Approachmentioning

confidence: 99%

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Tran

Huang

2022

Sensors

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Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

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Section: Approachmentioning

confidence: 99%

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Tran

Huang

2022

Sensors

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“…Among them, Electroencephalography signals collected through 16 sensors, distributed over a headset which makes contact with the scalp of the individual [8]. Similarly, Myoelectric 209 signals combined with neural networks were utilized as method of training a prosthesis that recognize hand gestures [9]. Piezoelectric sensors were used to acquire signals from a human forearm with the purpose of controlling a prosthesis [10], and others [11]- [16].…”

Section: Introductionmentioning

confidence: 99%

Automated Forearm Prosthesis Controlling Using Fiber Bragg Grating Sensor

Júnior

Dureck

Kalinowski

et al. 2023

J. Microw. Optoelectron. Electromagn. Appl.

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This paper describes the automation of a forearm prosthesis using the signal collected by a Fiber Bragg Grating (FBG) sensor. The FBG sensor is applied to one subject's forearm to measure the deformation as a result of the index and middle fingers when moved individually. It is possible to control a one joint model prosthesis allied to a compliant hand mechanism through signal analyses. Each finger movement has its features, such as its amplitude, which opens the possibility of using those to control different parts of the prosthesis, joint rotation by the middle finger, and compliant hand movement by the index finger. This paper presents results regarding prosthesis assembling, Hypertext Transfer Protocol (HTTP) communication latency between prosthesis and computer and tests with pre-acquired and processed FBG signal data. The prosthesis wrist rotation movement is related to the middle finger signal, and its compliant mechanism actuation is due to index finger signal. The communication between prosthesis and the computer had a mean latency of 140 ms and a standard deviation of 18 ms. The results demonstrate the potential for using the sensor system and automated prosthesis in other applications involving real-time forearm sensing, multi-finger signal analysis, and prosthetic movement.

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