Design of a Speech Anger Recognition System on Arduino Nano 33 BLE Sense

Waqar, Dania Maryam; Gunawan, Teddy Surya; Morshidi, Malik Arman; Kartiwi, Mira

doi:10.1109/icsima50015.2021.9526323

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Although, it is limited to executing a single program at a time, It has a built-in microphone, acceleromter and a 9axis inertial sensor that makes it excellent for wearable devices. Arduino Nano Board 33 is used various applications such as speech recognition [37] and Smart Health [38]. STM32 Microcontrollers STM32 [39] is a family of 32-bit microcontroller integrated circuits produced by STMicroelectronics.…”

Section: Arduino Nano 33 Ble Sensementioning

confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

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Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

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Section: Arduino Nano 33 Ble Sensementioning

confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

View full text Add to dashboard Cite

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“…The Arduino Nano 33 BLE Sense, which incorporates the nRF52840 microcontroller, LSM9DS1 IMU, MP34DT05 microphone, and BLE connectivity, is a prominent example of such a device [46]. It has been widely used in research experiments, demonstrating its effectiveness in various applications that demand machine learning capabilities, low power consumption, and integrated sensor functionalities [47][48][49]. Alternatively, the Seeed Studio XIAO nRF52840 microcontroller provides comparable capabilities [50].…”

Section: Microcontrollermentioning

confidence: 99%

Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion Classification

Huang,

Garcia

2023

Applied Sciences

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An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices, such as wristbands, are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection and activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with a convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users.

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“…Upon completing the pre-processing, the speech samples were converted into spectrograms as the input to the proposed model. This study extracted the Mel Frequency Cepstral Coefficient features from the spectrograms [35], one of the most widely used audio features in speech processing applications. The MFCC is often used due to its ability to mimic the human hearing system and provide information on the human vocal tract's shape [35].…”

Section: A Pre-processing and Feature Extractionmentioning

confidence: 99%

“…This study extracted the Mel Frequency Cepstral Coefficient features from the spectrograms [35], one of the most widely used audio features in speech processing applications. The MFCC is often used due to its ability to mimic the human hearing system and provide information on the human vocal tract's shape [35]. The feature extraction process is implemented using the Librosa library [12].…”

Section: A Pre-processing and Feature Extractionmentioning

confidence: 99%