Heart rate (HR) and respiratory rate (RR) are essential physiological indicators of people's physical function and exercise performance. Advancement in sensor technology has rendered earable devices with in-ear microphones feasible for vital sign monitoring. However, it is rather challenging to monitor heart rate and respiration simultaneously with a single earable device especially when a person is doing exercises. This is because intense physical activities can lead to significant noise interference which can easily obscure physiological signals. To address this challenge, this paper presents VitalEar, an exercise physiological monitoring system based on in-ear microphones, designed to estimate HR and RR while addressing complex motion interference and variability in users and activities. VitalEar employs Empirical Wavelet Transform (EWT) to decompose heartbeats into periodic and harmonic coefficients, enhancing noise reduction in the ECG spectrogram reconstruction model. Additionally, VitalEar incorporates a DCN-LSTM-based breathing curve reconstruction model to mitigate background noise and variability in user and activity. The experiments show that VitalEar achieves an average MAE of 5.61 BPM and 2.31 RPM, MAPE of 4.16% and 10.58% for HR and RR estimation, respectively. Compared to related work, our approach offers significant advantages in robustness against intense physical activities

In mixed vehicular platooning, heterogeneity from varying vehicle types and control systems leads to unpredictable traffic behaviors and frequent inconsistencies. These variances worsen the spatio-temporal discord within platoons, leading to persistent virtual bottlenecks that substantially impede traffic flow and increase energy consumption. To address these challenges, a centralized decision-making framework utilizing interflow hub-and-spoke graph reinforcement learning (InterHub-GRL) is implemented. This strategy enhances the precision of spatio-temporal interaction models among heterogeneous elements within the platooning, promoting collaborative decision-making, ensuring energy efficiency, and alleviating congestion. The proposed framework captures dynamic interactions within and between platoons in non-Euclidean spaces. Integrating spatio-temporal weighted graphs with a multi-head attention mechanism significantly improves the model’s ability to process both local and global data. Comparative algorithm experiments, generalizability assessments, and permeability ablation studies using the I-24 dataset demonstrate that this strategy achieves a 10% increase in throughput and a 9% reduction in energy consumption compared to baseline metrics. Notably, increasing the penetration rate of connected and autonomous vehicles (CAVs) markedly enhances traffic throughput while managing energy consumption.

The increasing popularity of earable devices stimulates great academic interest to design novel head gesture-based interaction technologies. But existing works simply consider it as a singular activity recognition problem. This is not in line with practice since users may have different body movements such as walking and jogging along with head gestures. It is also beneficial to recognize body movements during human-device interaction since it provides useful context information. As a result, it is significant to recognize such composite activities in which actions of different body parts happen simultaneously. In this paper, we propose a system called CHAR to recognize composite head-body activities with a single IMU sensor. The key idea of our solution is to make use of the inter-correlation of different activities and design a multi-task learning network to extract shared and specific representations. We implement a real-time prototype and conduct extensive experiments to evaluate it. The results show that CHAR can recognize 60 kinds of composite activities (12 head gestures and 5 body movements) with high accuracies of 89.7% and 85.1% in sufficient data and insufficient data cases, respectively.

With the rise of mobile devices, bel canto practitioners increasingly utilize smart devices as auxiliary tools for improving their singing skills. However, they frequently encounter timbre abnormalities during practice, which, if left unaddressed, can potentially harm their vocal organs. Existing singing assessment systems primarily focus on pitch and melody and lack real-time detection of bel canto timbre abnormalities. Moreover, the diverse vocal habits and timbre compositions among individuals present significant challenges in cross-user recognition of such abnormalities. To address these limitations, we propose TimbreSense, a novel bel canto timbre abnormality detection system. TimbreSense enables real-time detection of the five major timbre abnormalities commonly observed in bel canto singing. We introduce an effective feature extraction pipeline that captures the acoustic characteristics of bel canto singing. By applying temporal average pooling to the Short-Time Fourier Transform spectrogram, we reduce redundancy while preserving essential frequency-domain information. Our system leverages a transformer model with self-attention mechanisms to extract correlation and semantic features of overtones in the frequency domain. Additionally, we employ a few-shot learning approach involving pre-training, meta-learning, and fine-tuning to enhance the system’s cross-domain recognition performance while minimizing user usage costs. Experimental results demonstrate the system’s strong cross-user domain recognition performance and real-time capabilities.