The reliability of MEMS inertial devices applied in complex environments involves interdisciplinary fields, such as structural mechanics, material mechanics and multi-physics field coupling. Nowadays, MEMS inertial devices are widely used in the fields of automotive industry, consumer electronics, aerospace and missile guidance, and a variety of reliability issues induced by complex environments arise subsequently. Hence, reliability analysis and design of MEMS inertial devices are becoming increasingly significant. Since the reliability issues of MEMS inertial devices are mainly caused by complex mechanical and thermal environments with intricate failure mechanisms, there are fewer reviews of related research in this field. Therefore, this paper provides an extensive review of the research on the reliability of typical failure modes and mechanisms in MEMS inertial devices under high temperature, temperature cycling, vibration, shock, and multi-physical field coupling environments in the last five to six years. It is found that though multiple studies exist examining the reliability of MEMS inertial devices under single stress, there is a dearth of research conducted under composite stress and a lack of systematic investigation. Through analyzing and summarizing the current research progress in reliability design, it is concluded that multi-physical field coupling simulation, theoretical modeling, composite stress experiments, and special test standards are important directions for future reliability research on MEMS inertial devices.

Emotions are complex, and people vary greatly in their accuracy in recognizing their own emotions and those of others. With advances in computer science and neuroscience, there is a desire to use automated techniques to help people identify emotions. Bio-electrical signals have been proven effective for emotion detection, but the acquisition of conventional electrocardiogram (ECG) and EEG requires medical-specific equipment, which is very expensive, uncomfortable, and inconvenient due to the large number of electrodes and the hair-covered scalp. In this article, a novel emotion recognition method based on the feature fusion of single-lead EEG and ECG signals is proposed, using the long short term memory (LSTM)-MLP-based model and the CNN-based model for feature fusion and classification, respectively, with fivefold cross-validation for validation. The ECG and EEG signals of 15 participants were collected in five states: 1) happy; 2) relaxed; 3) calm; 4) sad; and 5) afraid, each of which was stimulated using the participants’ own proposed music. Various time-domain features, frequency-domain features, and nonlinear features were extracted from the ECG and EEG signals. Experimental results demonstrate that the accuracy of emotion recognition and classification of signals captured by the proposed device can reach 92.08% using the CNN model. While using the LSTM-MLP feature fusion model, the accuracy figure can be improved to 95.07%. The results of the ablation experiment indicate that the feature fusion approach does improve the accuracy of recognition. It is demonstrated that the proposed device and emotional recognition approach are effective and feasible.

People spend about one third of their lifetime in sleep, and sleep quality has a great impact on people’s health. Therefore, vital signs and sleep quality monitoring are more and more important. In this article, a novel smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor is developed to detect vital signs, snore events, and sleep stages. Besides, the related algorithms and methods for data preprocessing, heart and respiration rates detection, snoring recognition, and sleep stages classification are proposed in detail. Then, a series of sleep experiments is performed based on the experimental platform for the smart flexible belt, and the test results measured by PolySomnoGraphy are used as the golden standards for comparison. The experimental results demonstrate that the detection accuracies of heart rate and respiration rate of the belt are about 1.5 and 0.7 bpm, respectively. The accuracy of 97.2% is achieved by the proposed snoring recognition method. In addition, as for 2-stage analysis, the sensitivities of awake and asleep stages are 90.2% and 100%, respectively; meanwhile, the corresponding accuracy of sleep stages prediction is as high as 95.1%. However, as for 4-stage analysis, the sensitivities of awake, rapid eye movement, light sleep, and deep sleep stages are 90.2%, 77.1%, 78.1%, and 73.5%, respectively; meanwhile, the corresponding accuracy of sleep stages prediction decreases to 79.7%. In general, the test results indicate that vital signs detection, snoring recognition, and sleep stages classification based on the sleep monitoring belt are feasible and effective. Hence, this smart flexible belt can be widely used for sleep monitoring at home due to low cost and high performance.

Infrared thermal imaging technology that has unique advantages in obtaining target information at night and in harsh weather conditions has been widely used in the military, security, environmental monitoring, and other fields. Existing visible light object detection methods often face challenges of high false alarm rates and low accuracy when detecting small infrared objects. Moreover, current small infrared object detection methods mostly require much detailed annotation data, and it is challenging to visually depict the positions of small targets on the original complex background images. To address these issues, we propose a multi-branch backbone and adaptive spatial feature fusion detection head-based YOLO network called MBFormer-YOLO to achieve the high-accuracy detection of small infrared targets. We design a multi-branch backbone with structural re-parameterization capability called MBFormer, which has strong capabilities to fit object features. In the neck part, MBFormer-YOLO utilizes convolutional block attention modules and C3-SwinTransformer modules to suppress noise and redundant information. We develop a 4-level adaptive spatial feature fusion detection head to improve the detection accuracy of the proposed network. Many experiments are conducted on the SIRST-V2 dataset to validate the effectiveness of MBFormer-YOLO. The results show that MBFormer-YOLO achieves an 11.3% increase in AP50 and 2% in AP50-95 over the baseline model YOLOv8n and surpasses other advanced object detection models.

With the rapid development of economy, science, and technology, the aging issues become more and more serious. People aged above 65 have a risk of 28%–35% to fall. Among them, bedside falls happen most frequently. Therefore, the capability to detect fall events of the elderly is very important. In this article, a novel noncontact fall detector based on a MEMS low-resolution infrared sensor and a low-cost radar sensor is developed to detect bedside fall. Besides, IR image processing algorithms based on the adaptive filter, successive approximation, double boundary scans, and mathematical morphology processing are proposed in detail. Partition processing algorithm is used to suppress the influence of residual or existed heat sources on the bed or ground. Then, the statistical features of the center, area, temperature and duration, as well as stable flag and fall action flag, are extracted for fall recognition. Finally, a three-layer radial basis function neural network is applied to distinguish the fall events from the nonfall events. Considering the influence factors of ambient temperature, brightness, gender, dressing, fall posture, fall location, and scenario, a total of 640 tests are conducted and 5-fold cross validation is used to evaluate the classification performance. Experimental results indicate that the averages of the recall, precision, F1-Score, and detection accuracy are measured to be 91.25%, 94.76%, 92.97%, and 93.13%, respectively, which demonstrates that the proposed fall detection method is effective. Besides, the detection accuracy decreases from 96.88% to 85.94% as the ambient temperature rises. Hence, this noncontact fall detector can be widely applied for bedside fall detection at home, which is low cost, nonwearable, unobtrusive, noninvasive, and privacy preserved.

The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F₁-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.