Heart rate (HR), HR variability (HRV), and respiratory rate (RR) are vital physiological signals that can reveal the physiological and psychological states of human beings. Recent research studies have demonstrated that these physiological signals can be estimated in a noncontact way from visible or infrared facial videos based on remote photoplethysmography (rPPG). However, most existing methods only use only one data modality and output a single type of physiological signal at a time. To overcome these restrictions, we propose a multitask framework named the synchronously multi-physiological signals estimation network (SMP-Net) based on multimodal fusion to realize the noncontact multiphysiological signals estimation. First, a joint attention feature fusion (JAFF) module is designed to encode and fuse features from visible and infrared videos. The JAFF module considers different modalitywise, spatial, and channelwise information on features comprehensively. Then, a task-oriented feature refinement (TOFR) module is developed to extract task-oriented features by refining shared features to improve the estimation performance. Finally, the proposed SMP-Net is validated on the multimodal multi-vital sign (MMVS), VIPL-HR, and UBFC-rPPG datasets and outperforms the state-of-the-art methods. Our proposed SMP-Net achieves 1.12 bpm mean absolute error (MAE) for HR estimation, 0.58 Pearson correlation coefficient for rPPG estimation, and 2.08 MAE for RR estimation on the MMVS dataset. On the VIPL-HR and UBFC-rPPG datasets, the MAE of HR estimation is 2.03 and 0.59 bpm, respectively. The proposed SMP-Net is of great significance for continuous noncontact estimation of multiphysiological signals.

Negative emotional states, such as anxiety and depression, pose significant challenges in contemporary society, often stemming from the stress encountered in daily activities. Stress (state or level) recognition is a crucial prerequisite for effective stress management and intervention. Presently, wearable devices have been employed to capture physiological signals and analyze stress states. However, their constant skin contact can lead to discomfort and disturbance during prolonged monitoring. In this paper, a peak attention-based multitasking framework is presented for non-contact stress recognition. The framework extracts rPPG signals from RGB facial videos, utilizing them as inputs for a novel multi-task attentional convolutional neural network for stress recognition (MTASR). It incorporates peak detection and HR estimation as auxiliary tasks to facilitate stress recognition. By leveraging multi-task learning, MTASR can utilize information related to stress physiological responses, thereby enhancing feature extraction efficiency. For stress recognition, two binary classification tasks are applied: stress state recognition and stress level recognition. The model is validated on the UBFC-Phys public dataset and demonstrates an accuracy of 94.33% for stress state recognition and 83.83% for stress level recognition. The proposed method outperforms the dataset's baseline methods and other competing approaches.

Searching for a signal-emitting source in a turbulent medium is challenging due to sporadic and intermittent measurements. Individual searching agents through sharing information and collaboration can achieve a very effective, flexible and noise-tolerant search in turbulent environments. In this work, by extending the single-agent infotaxis to multi-agent systems, a collaborative infotaxis strategy is proposed to synthesize the spatio-temporal sensing capabilities of a group of agents and optimize the search time. Each agent adjusts its coupling strength with conspecifics and incorporates the conspecific’s observations with dynamic heterogeneous weights according to cognition differences. The cognition difference between agents is measured by the dissimilarity of probability maps. When each agent determines its actions by maximizing the synthesized information of the group, efficient and flexible search behaviors of the group are manifested in various scenarios. The proposed strategy can provide an efficient approach to the investigation of the collaborative behaviors of biological species.

The infotaxis scheme is a search strategy for a diffusive source, where the sensor platform is driven to reduce the uncertainty about the source through climbing the information gradient. The infotaxis scheme has been successfully applied in many source searching tasks and has demonstrated fast and stable searching capabilities. However, the infotaxis scheme focuses on gathering information to reduce the uncertainty down to zero, rather than chasing the most probable estimated source when a reliable estimation is obtained. This leads the sensor to spend more time exploring the space and yields a longer search path. In this paper, from the context of exploration-exploitation balance, a novel search scheme based on minimizing free energy that combines the entropy and the potential energy is proposed. The term entropy is implemented as the exploration to gather more information. The term potential energy, leveraging the distance to the estimated sources, is implemented as the exploitation to reinforce the chasing behavior with the receding of the uncertainty. It results in a faster effective search strategy by which the sensor determines its actions by minimizing the free energy rather than only the entropy in traditional infotaxis. Simulations of the source search task based on the computational plume verify the efficiency of the proposed strategy, achieving a shorter mean search time.

Blood pressure (BP) measurement is of great significance for the diagnosis of cardiovascular and chronic diseases. However, the existing BP measurement methods are uncomfortable to use and cannot provide continuous monitoring. To overcome these limitations, this article proposes a cloud-edge collaborative noncontact BP estimation model (CEBPM), which estimates BP from facial and palm videos within a cloud-edge collaboration framework. At the edge server, the feature extraction is conducted by the proposed special preprocessing procedure to improve the signal-to-noise ratio and reduce the network load for transmitting features. On the cloud server, the BP estimation network (BP-Net) is integrated with elaborately designed attention blocks (BPABs) and used for BP estimation in the online mode. Specifically, the attention block with temporal, spatial, and channel attention branches is introduced to obtain effective features. The experimental results demonstrate that the proposed model outperforms other competing methods of continuous BP estimation. The proposed collaborative noncontact BP estimation model can be leveraged in the regularization of BP monitoring in daily health management, especially for elderly and hypertension patients.

Heart rate is a vital physiological indicator that can be estimated in a non-contact manner through remote photoplethysmography (rPPG) using facial videos. Video quality is essential for the extraction of the rPPG signal and the accurate estimation of heart rate. Insufficient frame rate and resolution can cause loss of rPPG information, compromising heart rate estimation accuracy. To address this problem, a two-stage non-contact heart rate estimation method is proposed, which incorporates space-time video super-resolution (STVSR) technology for low-frame-rate and low-resolution video scenes. In the first stage, an STVSR network (FrameReconNet) and an rPPG information recovery network (rPPGRecovNet) are cascaded to achieve simultaneous video reconstruction and rPPG information recovery, guided by a hybrid loss function. In the second stage, an end-to-end network (rPPGExtractNet) is used to extract rPPG signals from videos reconstructed by FrameReconNet and estimate the final heart rate value. To address the limitation of the original STVSR network, which extracts only local features from short frame sequences and leads to rPPG discontinuity between adjacent sequences, a novel local-global-local (LGL) temporal feature aggregation and partition operation is proposed. This ensures consistent global temporal information across all frame sequences in a facial video. The experimental results on the VIPL and MMVS datasets demonstrate that the proposed method is an effective non-contact heart rate estimation method, which can recover rPPG information from low-frame-rate and low-resolution facial videos. The proposed method achieves comparable heart rate estimation accuracy with the fewest parameters, balancing the requirements of accuracy and real-time performance.