This paper presents a wearable system able to transmit a real-time electrocardiogram (ECG) from a chest device to a photoplethysmogram (PPG) device on a wristband using human body communication (HBC). The proposed communication system was designed based on wet electrodes body channel measurements and was implemented using off-the-shelf components and conductive fabric rather than the original wet electrodes. This work attempts to build a bridge between standard surface potential monitoring and smart clothing technologies. The transmission of the ECG using the body as a transmission media from the chest to wrist reached an average accuracy of 97.1% tested for three typical cardiovascular monitoring positions. This work demonstrates a fully wearable HBC system that can be considered for smart clothing technology. The effective data transfer rate was 468kbps using on-off-keying (OOK) centered at 30MHz.

A human body communication system connecting a wearable electrocardiogram device and photoplethysmogram device system will be demonstrated. This live demonstration system is composed of two wearable sensors and real-time signals display on a laptop. During the live demonstration, the system will demonstrate the effective transmission of ECG data via a body communication link with a parallel Bluetooth comparison as reference. The PPG device system acts as body communication receiver and the final display will present sample-synchronized ECG and PPG signals creating a on-body sensors sub-network.

An AI-edge affective computing platform with wearable electroencephalogram, electrocardiogram, and photoplethys-mogram sensors will be demonstrated [1]. This live demonstration is composed of three physiological sensors, real-time displayed signals, and emotion classification results on a laptop. During the live demonstration, The visitor can monitor his/her emotion classification result every second and the physiological signals displayed on GUI on the laptop.

In this paper, we presented an affective computing engine using the Long-term Recurrent Convolutional Network (LRCN) on electroencephalogram (EEG) physiological signal with 8 emotion-related channels. LRCN was chosen as the emotional classifier because compared to the traditional CNN, it integrates memory units allowing the network to discard or update previous hidden states which are particularly adapted to explore the temporal emotional information in the EEG sequence data. To achieve a real-time AI-edge affective computing system, the LRCN model was implemented on a 16nm Fin-FET technology chip. The core area and total power consumption of the LRCN chip are respectively 1.13×1.14 mm2and 48.24 mW. The computation time was 1.9µs and met the requirements to inference every sample. The training process cost 5.5µs per sample on clock frequency 125Hz which was more than 20 times faster than 128μ achieved with GeForce GTX 1080 Ti using python. The proposed model was evaluated on 52 subjects with cross-subject validation and achieved average accuracy of 88.34%, and 75.92% for respectively 2-class, 3-class.

In this paper, we developed and integrated an AI-edge emotion recognition platform using multiple wearable physiological signals sensors: Electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG) sensors. The emotion recognition platform used two combined machine learning approaches based on two systems input and preprocessing: An EEG-based emotion recognition system and an ECG/PPG-based system. The EEG-based system is a convolution neural network (CNN) that classifies three emotions, happiness, anger and sadness. The inputs of the CNN are extracted from the EEG signals using short-time Fourier transform (STFT), and the average accuracy for a subject-independent classification reached 76.94%. The ECG/PPG-based system used a similar CNN with an extracted features vector as input. The subject-dependent ECG/PPG classification system reached an average accuracy of 76.8%. The proposed system was integrated using the RISC-V processor and FPGA platforms to implement realtime monitoring and classification on edge. A 3-to-1 Bluetooth piconet was deployed to transmit all physiological signals on a single platform access point and to make use of low power wireless technologies.