Wearable sensor-based human activity recognition using hybrid deep learning techniques

Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset

Potential Application of Living Microorganisms in the Detoxification of Heavy Metals

Heavy metal (HM) exposure remains a global occupational and environmental problem that creates a hazard to general health. Even low-level exposure to toxic metals contributes to the pathogenesis of various metabolic and immunological diseases, whereas, in this process, the gut microbiota serves as a major target and mediator of HM bioavailability and toxicity. Specifically, a picture is emerging from recent investigations identifying specific probiotic species to counteract the noxious effect of HM within the intestinal tract via a series of HM-resistant mechanisms. More encouragingly, aided by genetic engineering techniques, novel HM-bioremediation strategies using recombinant microorganisms have been fruitful and may provide access to promising biological medicines for HM poisoning. In this review, we summarized the pivotal mutualistic relationship between HM exposure and the gut microbiota, the probiotic-based protective strategies against HM-induced gut dysbiosis, with reference to recent advancements in developing engineered microorganisms for medically alleviating HM toxicity

Postbiotics in Human Health: A Narrative Review

In the 21st century, compressive health and functional foods are advocated by increasingly more people in order to eliminate sub-health conditions. Probiotics and postbiotics have gradually become the focus of scientific and nutrition communities. With the maturity and wide application of probiotics, the safety concerns and other disadvantages are non-negligible as we review here. As new-era products, postbiotics continue to have considerable potential as well as plentiful drawbacks to optimize. “Postbiotic” has been defined as a “preparation of inanimate microorganisms and/or their components that confers a health benefit on the host”. Here, the evolution of the concept “postbiotics” is reviewed. The underlying mechanisms of postbiotic action are discussed. Current insight suggests that postbiotics exert efficacy through protective modulation, fortifying the epithelial barrier and modulation of immune responses. Finally, we provide an overview of the comparative advantages and the current application in the food industry at pharmaceutical and biomedical levels

Effects of Phenyl Hydrogen Polysiloxane Molecular Structure on the Performance of LED Packaging Silicone Rubber

Phenyl hydrogen polysiloxanes of different structures were prepared, and LED packaging silicone rubbers were made from these polysiloxanes. The hardness, elongation, tensile strength and anti-yellowing of the LED packaging silicone rubbers on lamps were characterized. The results show that phenyl hydrogen polysiloxanes of resin structures had lower molecular weight, and that the cured silicone rubbers exhibited higher hardness, tensile strength, crosslink density and lower gas permeability. Phenyl hydrogen polysiloxanes of oil structures had higher molecular weight, and the cured silicone rubbers from these polysiloxanes exhibited lower volume expansion coefficient, higher hot and cold impact cycles and less change in hardness during the aging process. The study results further showed that the degree of change in hardness, yellowing, gas permeability, sulfuration and volume expansion coefficient was reduced by using phenyl hydrogen polysiloxanes of resin-oil structures as crosslinking agents, enabling the combined performance advantages of both resin-structure and oil-structure rubbers. DOI: http://dx.doi.org/10.5755/j01.ms.24.2.18494</p

The Microbiota&ndash;Gut&ndash;Brain Axis in Depression: The Potential Pathophysiological Mechanisms and Microbiota Combined Antidepression Effect

Depression is a kind of worldwide mental illness with the highest morbidity and disability rate, which is often accompanied by gastrointestinal symptoms. Experiments have demonstrated that the disorder of the intestinal microbial system structure plays a crucial role in depression. The gut&ndash;brain axis manifests a potential linkage between the digestion system and the central nervous system (CNS). Nowadays, it has become an emerging trend to treat diseases by targeting intestinal microorganisms (e.g., probiotics) and combining the gut&ndash;brain axis mechanism. Combined with the research, we found that the incidence of depression is closely linked to the gut microbiota. Moreover, the transformation of the gut microbiota system structure is considered to have both positive and negative regulatory effects on the development of depression. This article reviewed the mechanism of bidirectional interaction in the gut&ndash;brain axis and existing symptom-relieving measures and antidepression treatments related to the gut microbiome

Non-Linear Chaotic Features-Based Human Activity Recognition

Human activity recognition (HAR) has vital applications in human–computer interaction, somatosensory games, and motion monitoring, etc. On the basis of the human motion accelerate sensor data, through a nonlinear analysis of the human motion time series, a novel method for HAR that is based on non-linear chaotic features is proposed in this paper. First, the C-C method and G-P algorithm are used to, respectively, compute the optimal delay time and embedding dimension. Additionally, a Reconstructed Phase Space (RPS) is formed while using time-delay embedding for the human accelerometer motion sensor data. Subsequently, a two-dimensional chaotic feature matrix is constructed, where the chaotic feature is composed of the correlation dimension and largest Lyapunov exponent (LLE) of attractor trajectory in the RPS. Next, the classification algorithms are used in order to classify and recognize the two different activity classes, i.e., basic and transitional activities. The experimental results show that the chaotic feature has a higher accuracy than traditional time and frequency domain features

A Novel Lower-Limb Coordination Assessment Scheme Using Multi-Scale Nonlinear Coupling Characteristics With sEMG

Motor dysfunction (e.g., incoordination of upper or lower limb) significantly limits the individuals' ability of daily living, and thus the provisioning of a motion coordination assessment method becomes of vital importance. As a quantitative indicator, intermuscular coupling strength could assess limb coordination. Since surface electromyography (sEMG) signals cover nonlinear coupling characteristics, in this article, a lower-limb motion coordination assessment scheme with intermuscular coherence (IMC) analysis and optimization variational mode decomposition (VMD) is proposed. First, sEMG signals are decomposed into several intrinsic mode functions (IMFs) using optimized VMD. Then, the nonlinear coupling feature vector in the beta-band frequency domain (15-35 Hz) is extracted by the G-P algorithm from optimal IMF, thereby estimating the intermuscular coupling strength by IMC. In particular, differential evolution (DE) algorithm's global optimization capability and envelope entropy (EE)'s sparsity are adopted to provide the basis for optimization VMD and screening optimal IMF, respectively. Finally, signals are collected from four muscle pairs of the 12 male and 12 female subjects to assess lower-limb motions' coordination. Simultaneously, the independent sample $T$ test is leveraged to compare the effects of gender on groups' characteristics (e.g., age, height, and body mass). Results demonstrate not only the effectiveness of the proposed approach (i.e., $p< 0.01$ with both 'S-walking' and 'Running' motions, $p< 0.05$ with 'Q-walking') but also the difference in motion coordination relationship between male and female adults as well as the significance of muscle selection

A Novel Device-Free Counting Method Based on Channel Status Information

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread application. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed. The wavelet domain denoising is introduced to mitigate environment noise. Furthermore, the amplitude or phase covariance matrix is extracted as the eigenmatrix. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. At the same experimental environment, the accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC

Exosomes from Bone Marrow Microenvironment-Derived Mesenchymal Stem Cells Affect CML Cells Growth and Promote Drug Resistance to Tyrosine Kinase Inhibitors

Although major advances have been achieved in the treatment of chronic myeloid leukemia (CML) by using tyrosine kinase inhibitors, patients relapse after withdrawal and need long-term medication. This reflects the CML clones have not been eliminated completely. The precise mechanisms for the maintenance of CML cells are not yet fully understood. The bone marrow microenvironment constitutes the sanctuary for leukemic cells. Mesenchymal stem cells (MSC) are an important component of the bone marrow microenvironment (BM). It plays an important role in the development and drug resistance of CML. Accumulating evidence indicates that exosomes play a vital role in cell-to-cell communication. We successfully isolated and purified exosomes from human bone marrow microenvironment-derived mesenchymal stem cells (hBMMSC-Exo) by serial centrifugation. In the present study, we investigated the effect of hBMMSC-Exo on the proliferation, apoptosis, and drug resistance of CML cells. The results demonstrated that hBMMSC-Exo had the ability to inhibit the proliferation of CML cells in vitro via miR-15a and arrest cell cycle in the G0/G1 phase. However, the results obtained from BALB/c nu/nu mice studies apparently contradicted the in vitro results. In fact, hBMMSC-Exo increased tumor incidence and promoted tumor growth in vivo. Further study showed the antiapoptotic protein Bcl-2 expression increased, whereas the Caspase3 expression decreased. Moreover, the in vivo study in the xenograft tumor model showed that hBMMSC-Exo promoted the proliferation and decreased the sensitivity of CML cells to tyrosine kinase inhibitors, resulting in drug resistance. These results demonstrated that hBMMSC-Exo supported the maintenance of CML cells and drug resistance in BM by cell-extrinsic protective mechanisms. They also suggested that hBMMSC-Exo might be a potential target to overcome the microenvironment-mediated drug resistance

Mesenchymal Stem Cell-Derived Extracellular Vesicles: Roles in Tumor Growth, Progression, and Drug Resistance

Mesenchymal stem cells (MSCs) are ubiquitously present in many tissues. Due to their unique advantages, MSCs have been widely employed in clinical studies. Emerging evidences indicate that MSCs can also migrate to the tumor surrounding stroma and exert complex effects on tumor growth and progression. However, the effect of MSCs on tumor growth is still a matter of debate. Several studies have shown that MSCs could favor tumor growth. On the contrary, other groups have demonstrated that MSCs suppressed tumor progression. Extracellular vesicles have emerged as a new mechanism of cell-to-cell communication in the development of tumor diseases. MSCs-derived extracellular vesicles (MSC-EVs) could mimic the effects of the mesenchymal stem cells from which they originate. Different studies have reported that MSC-EVs may exert various effects on the growth, metastasis, and drug response of different tumor cells by transferring proteins, messenger RNA, and microRNA to recipient cells. In the present review, we summarize the components of MSC-EVs and discuss the roles of MSC-EVs in different malignant diseases, including the related mechanisms that may account for their therapeutic potential. MSC-EVs open up a promising opportunity in the treatment of cancer with increased efficacy