Non-contact automatic respiration monitoring in restrained rodents

Prenatal anxiety recognition model integrating multimodal physiological signal

Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.

Noncontact Respiratory Monitoring Using Depth Sensing Cameras: A Review of Current Literature

There is considerable interest in the noncontact monitoring of patients as it allows for reduced restriction of patients, the avoidance of single-use consumables and less patient-clinician contact and hence the reduction of the spread of disease. A technology that has come to the fore for noncontact respiratory monitoring is that based on depth sensing camera systems. This has great potential for the monitoring of a range of respiratory information including the provision of a respiratory waveform, the calculation of respiratory rate and tidal volume (and hence minute volume). Respiratory patterns and apneas can also be observed in the signal. Here we review the ability of this method to provide accurate and clinically useful respiratory information.

Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera

Respiratory rate is a well-known to be a clinically important parameter with numerous clinical uses including the assessment of disease state and the prediction of deterioration. It is frequently monitored using simple spot checks where reporting is intermittent and often prone to error. We report here on an algorithm to determine respiratory rate continuously and robustly using a non-contact method based on depth sensing camera technology. The respiratory rate of 14 healthy volunteers was studied during an acute hypoxic challenge where blood oxygen saturation was reduced in steps to a target 70% oxygen saturation and which elicited a wide range of respiratory rates. Depth sensing data streams were acquired and processed to generate a respiratory rate (RR_depth). This was compared to a reference respiratory rate determined from a capnograph (RR_cap). The bias and root mean squared difference (RMSD) accuracy between RR_depth and the reference RR_cap was found to be 0.04 bpm and 0.66 bpm respectively. The least squares fit regression equation was determined to be: RR_depth = 0.99 × RR_cap + 0.13 and the resulting Pearson correlation coefficient, R, was 0.99 (p < 0.001). These results were achieved with a 100% reporting uptime. In conclusion, excellent agreement was found between RR_depth and RR_cap. Further work should include a larger cohort combined with a protocol to further test algorithmic performance in the face of motion and interference typical of that experienced in the clinical setting.

Machine vision analysis on abnormal respiratory conditions of mice inhaling particles containing cadmium

Inhalable environmental toxicants can induce pulmonary malfunction resulting abnormal respiratory conditions. The traditional methods currently available to detect the respiratory condition of animals rely on differential pressure transducers and signal amplifiers. In comparison, current machine vision application requires little hardware. But it is unsuitable for respiratory condition tests of experimental animals reflecting respiratory toxicities of inhalable pollutants. In this study, we establish a new automatic method of machine vision analysis using a model that has mice inhaling aqueous aerosol with different concentrations of CdCl₂ (0, 1, 3, 5 mM 2 h/day) for 7 days as simulant occupational exposure of inhalable Cd and analyze respiratory conditions such as respiratory rate, rhythm index, drive index and exchange index. Additionally, the models with different degrees of lung damage in mice are further tested and verified by the concentrations of cadmium accumulated in the lungs and the analyses on pulmonary porosity, fibrosis and inflammation. Machine vision analysis can identify the abnormal respiratory conditions of mice. Respiratory rate and rhythm index increase after exposure to cadmium. In the individuals with mild lung damage, respiratory drive index and exchange index in treatment group are higher than that in the control group, and in individuals with severe lung damage, these indices are similar to that of the control group. These abnormal respiratory conditions related to variable lung damage in mice demonstrate that the respiration is synchronously influenced by inhalable Cd and respiratory compensation according to normal physiological regulation, suggesting the present method is effective.

An automated behavior analysis system for freely moving rodents using depth image

A rodent behavior analysis system is presented, capable of automated tracking, pose estimation, and recognition of nine behaviors in freely moving animals. The system tracks three key points on the rodent body (nose, center of body, and base of tail) to estimate its pose and head rotation angle in real time. A support vector machine (SVM)-based model, including label optimization steps, is trained to classify on a frame-by-frame basis: resting, walking, bending, grooming, sniffing, rearing supported, rearing unsupported, micro-movements, and "other" behaviors. Compared to conventional red-green-blue (RGB) camera-based methods, the proposed system operates on 3D depth images provided by the Kinect infrared (IR) camera, enabling stable performance regardless of lighting conditions and animal color contrast with the background. This is particularly beneficial for monitoring nocturnal animals' behavior. 3D features are designed to be extracted directly from the depth stream and combined with contour-based 2D features to further improve recognition accuracies. The system is validated on three freely behaving rats for 168 min in total. The behavior recognition model achieved a cross-validation accuracy of 86.8% on the rat used for training and accuracies of 82.1 and 83% on the other two "testing" rats. The automated head angle estimation aided by behavior recognition resulted in 0.76 correlation with human expert annotation. Graphical abstract Top view of a rat freely behaving in a standard homecage, captured by Kinect-v2 sensors. The depth image is used for constructing a 3D topography of the animal for pose estimation, behavior recognition, and head angle calculation. Results of the processed data are displayed on the user interface in various forms.