Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study

Ouhaichi, Hamza; Olsson, Helena Holmström; Bosch, Jan

doi:10.1007/978-3-030-33742-1_12

Cited by 2 publications

(1 citation statement)

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“…In [9], research was carried out to investigate the ability for a network to auto-configure its radio and IP stack by utilizing ML techniques. Using an IBM PPC and a ARMv7 processor, the SVM algorithm was implemented.…”

Section: Review Of Edge Computingmentioning

confidence: 99%

Real-time edge computing design for physiological signal analysis and classification

Suppiah,

Noori,

Abidi

et al. 2024

Biomed. Phys. Eng. Express

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Physiological Signals like Electromography (EMG) and Electroencephalography (EEG) can be analysed and decoded to provide vital information that can be used in a range of applications like rehabilitative robotics and remote device control. The process of acquiring and using these signals requires many compute-intensive tasks like signal acquisition, signal processing, feature extraction and machine learning. Performing these activities on a PC-based system with well-established software tools like Python and Matlab is the first step in designing solutions based upon these signals. In the application domain of rehabilitative robotics, one of the main goals is to develop solutions that can be deployed for the use of individuals who need them in improving their Acitivities-for-Daily Living (ADL). To achieve this objective, the final solution must be deployed onto an embedded solution that allows high portability and ease-of-use. Porting a solution from a PC-based environment onto a resource-constraint one such as a microcontroller poses many challenges. In this research paper, we propose the use of a ARM-based Corex M-4 processor. We explore the various stages of the design from the initial testing and validation, to the deployment of the proposed algorithm on the controller, and further investigate the use of Cepstrum features to obtain a high classification accuracy with minimal input features. The proposed solution is able to achieve an average classification accuracy of 95.34% for all five classes in the EMG domain and 96.16% in the EEG domain on the embedded board.

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Section: Review Of Edge Computingmentioning

confidence: 99%