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.

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.

Sleep apnea impacts more and more people all over the world, and obstructive sleep apnea of which is most frequent. Hence, researches on snoring detection and related suppression methods are extremely urgent. In this paper, a novel low-cost flexible patch with MEMS microphone and accelerometer is developed to detect snore event and sleeping posture, and a small vibration motor embedded in the patch is designed to suppress snoring. Theoretical analyses of short-time energy, Piecewise Average Filtering (PAF) and MFCCs processing are described in detail, and the improved MFCCs are put forward and used as the input of the CNN. Furthermore, the snore recognition method based on the combination of similarity analysis and CNN analysis is presented, followed by the snoring suppression method. Experimental results demonstrate that the main features of the sound signals can be extracted effectively by PAF and MFCCs processing, and the data compression ratio is about 99.41%. Besides, the locations of the eigenvectors can be found accurately based on short-time energy analysis. The numbers of high similarity of snoring signals within 30 seconds are larger than 3, while those of non-snoring signals are often less than 3. If the preliminary screening with similarity analysis is passed, CNN analysis will be conducted to judge whether there are snoring events. The accuracy of snore recognition with CNN analysis is calculated to be as high as 99.25%. Finally, the average snoring time measured by the smart patch with snoring suppression is reduced to 15min from 135min, which indicates that the proposed snore recognition and suppression methods are effective.

Objective: The Chinese medicine Jianpi-Huayu decoction (健脾化瘀方, JPHY) can alleviate cancer-related fatigue in patients with liver cancer. However, its mechanism remains unclear. In this study, we used BALB/c mice with liver cancer model to investigate whether JPHY alleviates cancer-related fatigue by regulating Th1/Th2 immune balance; and the possible association with the IL-27/STAT1 signaling pathway. Methods: We established a mouse model of liver cancer fatigue. Mice were gavaged with physiological saline, low, medium, or high concentrations of JPHY respectively; and intraperitoneal injection of fludarabine (STAT1 pathway inhibitor) with JPHY for 21 days. We recorded the general condition of the mice, and assessed fatigue using scoring criteria and Exhausted Swimming Test. We calculated the spleen and thymus indices, performed H&E staining and immunohistochemical analysis on liver tumor tissues to observe the tumor proliferation marker ki67. We quantified the secretion levels of IFN-γ and IL-2 produced by Th1 cells in serum and splenic lymphocytes, as well as the secretion of IL-4, IL-10 by Th2 cells, and IL-27 in the signaling pathway through ELISA analysis. We evaluated the expression levels of p-STAT1 and STAT1 in spleen tissues using Western blot analysis. Results: JPHY exhibits a therapeutic effect on hepatocellular carcinoma-induced splenomegaly in murine models by upregulating the pro-inflammatory cytokines IFN-γ and IL-2 and downregulating the anti-inflammatory cytokines IL-4 and IL-10. Moreover, JPHY suppresses ki67 expression, reduces tumor-related inflammation infiltration, and ameliorates cancer-associated fatigue. Additionally, the expression of phosphorylated protein p-STAT1 is down-regulated. Conclusion: JPHY may improve the Th1/Th2 immune balance through its anti-inflammatory effects and promotion of IL-27-induced STAT1 phosphorylation, thereby alleviating fatigue in mice with liver cancer.

More and more people around the world suffer from insomnia, hence sleep-aid methods are very important and urgent. In this paper, a flexible sleep-aid eye mask based on Acupoint Electric Pulse Stimulation (AEPS) combined bioelectrical signal feedback is proposed. Besides, the massage sleep-aid principle of Yintang and Anmian acupoints is described. Afterwards, AEPS circuit, Bioelectrical Signal Acquisition (BSA) circuit and a smart flexible sleep-aid eye mask are put forward. The sleep-aid effect evaluation methods based on the key features of EEG and ECG signals, as well as the real-time sleep-aid control method, are presented in detail. Experimental results demonstrate that the BSA circuit and feature extraction method with Discrete Wavelet Transform (DWT) analysis are effective. After 4 kinds of experimental tests with 10 subjects, compared with the results of No Stimulation (NS), the values of HRMEAN and Ebetam of AEPS of Yintang acupoint (AEPS-Y), AEPS of Anmian acupoint (AEPS-A), and AEPS of Yintang and Anmian acupoints (AEPS-YA) decrease, while the values of RRMEAN, SDNN, RMSSD, LF, HF, RLH, Ealpham and RAB of AEPS-Y, AEPS-A and AEPS-YA increase, which verifies that the sleep-aid controls of AEPS-Y, AEPS-A and AEPS-YA are all effective. In addition, the sleep-aid effect of AEPS-Y is better than that of AEPS-A or AEPS-YA. Compared with that without AEPS-Y, the median sleep latency with AEPS-Y of the flexible eye mask is reduced by about 43.08%. To sum up, this flexible eye mask can be widely used for sleep-aid control at home, which is low-cost and comfortable.

Heart Rate Variability (HRV) is an important indicator for assessing the function of the cardiac Autonomic Nervous System (ANS), and it is important for early detection and prevention of cardiovascular diseases, stress management and mental health. Besides, different sleep postures have different effects on respiration and ventilation, and inappropriate sleep postures may lead to organ compression and Obstructive Sleep Apnea (OSA). Therefore, HRV and sleep posture detection are very significant. However, there is a lack of the high-comfortable, low-cost and high-accuracy detection method. In this paper, a novel detection method for HRV and sleep posture based on a flexible sleep monitoring belt is proposed. The test platform including a Flexible Sleep Monitoring Belt (FSMB) and a Bioelectrical Signal Acquisition Circuit (BSAC), as well as the test flow, is described in detail. The BSAC composed of a series of amplifiers and filters is designed to acquire the ElectroCardioGraphy (ECG) signal, while the FSMB mainly composed of a MEMS Inertial Measurement Unit (IMU) and a pressure sensor array is designed to acquire the BallistoCardioGraphy (BCG) or GyroCardioGraphy (GCG) signal. Besides, the HRV features of ECG, BCG and GCG signals are extracted by the Wavelet Packet Transform (WPT) analysis, and the short-time energies of the triaxial accelerations and angular velocities are extracted as the features for sleep posture detection. For facilitating the realization with edge computing, a lightweight Convolutional Neural Networks (CNN) model is proposed to recognize the sleep posture. Experimental results indicate that the detection accuracy of HRV with BCG signal is slightly bigger than that with GCG signal, reaching 91.1% compared with the result of ECG signal. In addition, the detection accuracy of sleep posture with the proposed CNN model achieves 96.44%. Therefore, the proposed detection method of HRV and sleep posture based on the FSMB is effective and feasible.