An AI-Edge Platform with Multimodal Wearable Physiological Signals Monitoring Sensors for Affective Computing Applications

Yang, Cheng-Jie; Fahier, Nicolas; He, Chang-Yuan; Li, Wei-Chih; Fang, Wai-Chi

doi:10.1109/iscas45731.2020.9180909

Cited by 28 publications

(10 citation statements)

References 11 publications

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“…Custom architectures can be allocated on FPGAs, leading to a high level of parallel signal processing. This technology is often used for applications demanding fast and power efficient machine learning operations [ 43 ]. Figure 15 depicts two FPGA development boards compatible with the PPG EduKit.…”

Section: Ppg Edukit Platformmentioning

confidence: 99%

PPG EduKit: An Adjustable Photoplethysmography Evaluation System for Educational Activities

Morillo

Cause

Baciu

et al. 2022

Sensors

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The grown interest in healthcare applications has made biomedical engineering one of the fastest growing disciplines in recent years. Photoplethysmography (PPG) has gained popularity in recent years due to its versatility for noninvasive monitoring of vital signs such as heart rate, respiratory rate, blood oxygen saturation and blood pressure. In this work, an adjustable PPG-based educational device called PPG EduKit, which aims to facilitate the learning of the PPG technology for a wide range of engineering and medical disciplines is proposed. Through the use of this educational platform, the PPG signal can be understood, modified and implemented along with the extraction of its relevant physiological information from a didactic, intuitive and practical way. The PPG Edukit is evaluated for the extraction of physiological parameters such as heart rate and blood oxygen level, demonstrating how its features contribute to engineering and medical students to assimilate technical concepts in electrical circuits, biomedical instrumentation, and human physiology.

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Section: Ppg Edukit Platformmentioning

confidence: 99%

PPG EduKit: An Adjustable Photoplethysmography Evaluation System for Educational Activities

Morillo

Cause

Baciu

et al. 2022

Sensors

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“…This system was evaluated on an Intel i7 CPU and an Intel DE5 FPGA board. Reference [13] introduced an emotion recognition platform based on physiological signals preprocessed on a RISC-V implemented on a Kintex-7 FPGA connected to a Spartan-6 FPGA where two parallel CNNs run classification for EEG and ECG/PPG signals to detect emotions. Then, a PC receives the CNN outputs to identify the detected emotion.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Neural Network Acceleration on a Hybrid CPU-FPGA Architecture: A Case Study

Ajili

Hara-Azumi

2022

IEEE Access

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Internet of Things and deep learning (DL) are merging into one domain and enabling outstanding technologies for various classification tasks. Such technologies are based on complex networks that mainly target powerful platforms with rich computing resources, such as servers. Therefore, for resource-constrained embedded systems, new challenges of size, performance (i.e., latency, throughput, and accuracy), and power consumption-aware networks should be addressed, particularly when edge devices handle multimodal data (i.e., different types of real-time sensing data). In this case study, we focus on DeepSense, a time-series multimodal DL framework combining convolutional neural networks (NN) and recurrent NN to process accelerometer and gyroscope data for human activity recognition. We present a field-programmable gate array (FPGA)-based acceleration for DeepSense incorporated into a hardware/software co-design approach to achieve better latency and energy efficiency using the Xilinx Vitis AI framework. The architecture of DeepSense has drawbacks that cannot be easily alleviated by Vitis AI; therefore, we introduced a new methodology of adjusting the framework and its components (i.e., the deep learning processing unit (DPU)) to achieve a custom design suitable for such time-series multimodal NN. We implemented the accelerator on two FPGA boards and performed a quantitative evaluation by varying the DPU parameter settings to support our design approach. We demonstrated the effectiveness of our implementation against the original software implementation on mobile devices by achieving up to 2.5× and 5.2× improvement in latency and energy consumption, respectively. Through this case study, we provide crucial insights into the FPGAbased accelerator design of multimodal NN and essential aspects to consider for further improvements and adaptation in other application domains.

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“…Self-assessment tests were not administered to subjects. More recently, several studies focused on channel reduction for improving the wearability of the emotion detection systems [46][47][48][49][50][51][52][53][54][55] . Marín-Morales at al.…”

Section: Related Workmentioning

confidence: 99%

“…Further not standardized stimuli are personal memories. For example, the study 53 presents a very interesting data fusion approach for emotion classification based on electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG). The EEG signals were acquired through an 8-channel device.…”

Section: Related Workmentioning

confidence: 99%

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

Apicella

Arpaïa

Mastrati

et al. 2021

Preprint

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An emotional-valence detection method for a very–high wearable EEG-based system is proposed. Valence detection occurs along the interval scale theorized by the circumplex model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. Wearability is guaranteed by a wireless cap with conductive-rubber dry electrodes and 8 data acquisition channels. Experimental validation was realized on 25 volunteers without depressive disorders. The metrological reference was built by combining the rating of a standardized set of pictures from dataset Oasis and results from the Self Assessment Manikin questionnaire. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e. Hemispheric Asymmetry Theories) and (ii) automated. A pipeline of a custom 12-band Filter Bank and Common Spatial Pattern algorithm is the method proposed for automated feature extraction. Four machine learning classifiers were tested for validating the proposed method in discriminating two classes (high or low emotional valence). An intra-individual average accuracy, 96.2 %, was obtained by a shallow artificial neural network, while K-Nearest Neighbors allowed obtaining 80.3 % of inter-individual accuracy. Considering the wearability, and the well-founded metrological reference, results are the state of the art in emotional valence detection.

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An AI-Edge Platform with Multimodal Wearable Physiological Signals Monitoring Sensors for Affective Computing Applications

Cited by 28 publications

References 11 publications

PPG EduKit: An Adjustable Photoplethysmography Evaluation System for Educational Activities

PPG EduKit: An Adjustable Photoplethysmography Evaluation System for Educational Activities

Multimodal Neural Network Acceleration on a Hybrid CPU-FPGA Architecture: A Case Study

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

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