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Fall event, as one of the greatest risks to the elderly, its detection has been a hot research issue in the solitary scene in recent years. Nevertheless, most current researches are conducted in the ideal environments, without considering the challenge of complex background in real situation. Therefore, this paper aims to detect fall event detection in complex background based on visual data. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method is first used to accurately extract the moving objects in the noise background. Then, an attention guided Bi-directional LSTM model is proposed for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other stateof-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.

Background: Acupuncture has an unique role in preventing and managing mild cognitive impairment (MCI) in nonpharmaceutical therapies because of its small wound, mild pain, and high security for many years. However, there is no systematic review evaluating safety and efficacy of acupuncture for MCI in elderly people. Therefore, this study will provide a protocol to explore the effectiveness and safety of acupuncture for MCI in the elderly. Methods: Retrieval from 8 electronic databases was conducted to determine eligible trials published until May, 2019. Homogeneity qualified studies were included for data were extracted such as study country location, demographic characteristics, and measure outcomes, and were analyzed by a random effect model and sensitivity analyses to identify heterogeneity. Review Manager (Revman Version 5.3) software will be used for data synthesis, sensitivity analysis, meta regression, subgroup analysis, and risk of bias assessment. A funnel plot will be developed to evaluate reporting bias. Results: A total of 15 randomized control trials involving 1051 subjects were included. The results were as follows: Compared with the control group, the clinical efficacy rates of acupuncture was better, odds ratio = 2.52, 95% confidence interval (CI) (1.86, 3.42), P < .00001, mini-mental state examination scores (mean difference [MD] = 1.53, 95% CI [1.04, 2.01], P < .00001), Montreal cognitive assessment scores (MD = 2.05, 95% CI [1.17, 1.92], P < .00001), activity of daily living scale (MD = 1.71, 95% CI [–1.38, 4.79], P > .05), and clock drawing task scores (MD = 1.91, 95% CI [1.74, 2.08], P < .00001). Conclusion: This study shows that acupuncture is beneficial for improving aspects of cognitive function in elderly people with MCI, which suggests that acupuncture may be an effective alternative and complementary approach to existing therapies for elderly people. More rigorous experimental studies and longer follow-up studies should be conducted in the future.

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Aim The review aimed to evaluate the effects of pulmonary rehabilitation in elderly patients with chronic obstructive pulmonary disease (COPD). Background With an increase in published reports on pulmonary rehabilitation, there is a need for a meta‐analysis to measure the effects of pulmonary rehabilitation in elderly COPD patients. Design A systematic review and meta‐analysis. Data Sources The Cochrane library (Issue 4, 2018), Web of Science (1975 to April 2018), Embase (1974 to April 2018), Pubmed (1966 to April 2018), CINAHL (1982 to April 2018), JBI (The Joanna Briggs Institute) (1996 to April 2018), CNKI (China National Knowledge Infrastructure) (1979 to April 2018), CBM (SinoMed) (1982 to April 2018), and Wanfang Data (1900 to April 2018) were searched. Review Methods Six outcome indicators were utilized for the effects of pulmonary rehabilitation. Two reviewers selected trials, evaluated the quality, and extracted data. Meta‐analysis was performed by using the RevMan 5.3 software. Results Eight studies recruited 414 elderly patients. Pulmonary rehabilitation resulted in significantly improved exercise capacity and quality of life in elderly people but with no influence on oxygen saturation compared with the control group. Conclusion There is a need to provide more detailed pulmonary rehabilitation programs for elderly patients with COPD.

In recent years, more machine learning algorithms have been applied to odor classificatio n.These odor classification algorithms usually assume that the training datasets are static. However, for some odor recognition tasks, new odor classes continually emerge. That is, the odor datasets are dynamically growing while both training samples and number of classes are increasing over time.Motivated by this concern, this paper proposes a Deep Nearest Class Mean (DNCM) model based on the deep learning framework and nearest class mean method. The proposed model not only leverages deep neural network to extract deep features, but is also able to dynamically integrate new classes over time. In our experiments, the DNCM model was initially trained with 10 classes, then 25 new classes are integrated. Experiment results demonstrate that the proposed model is very efficient for incremental odor classification, especially for new classes with only a small number of training examples.

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