Tai chi exercise may have positive effects on bone health in perimenopausal and postmenopausal women. This systematic review is the first to summarize evidence to clarify the efficacy of tai chi exercise in bone health. The benefits of tai chi exercise on bone health remain unclear; further studies are needed. Emerging randomized controlled trials (RCTs) exploring the efficacy of tai chi exercise on bone health among older women, but yielded inconclusive results. Our objective is to conduct a systematic review to evaluate evidence from RCTs to clarify the efficacy of tai chi exercise on bone mineral density (BMD), and bone turnover markers (BTM) in perimenopausal and postmenopausal women. Six electronic databases were searched, and reference lists of systematic reviews and identified studies from the search strategy were also screened. We included all RCTs that investigate tai chi exercise for bone health in perimenopausal and postmenopausal women. Data selection, extraction, and evaluation of risk of bias were performed independently by two reviewers. Ten trials detailed in 11 articles were included. Six of the 11 studies reported positive outcomes on bone health. Results of our meta-analysis showed a significant effect of tai chi exercise on BMD change at the spine compared with no treatment in perimenopausal and postmenopausal women. When tai chi exercise combined with a calcium supplement was compared with the calcium supplement alone, the result of BMD change at the spine showed no significant effect. Because the measurable effect observed was minimal, and due to the low quality of methodology of the studies, we conclude that the result is of limited reliability. Tai chi exercise may have benefits on bone health in perimenopausal and postmenopausal women, but the evidence is sometimes weak, poor, and inconsistent. Consequently, only limited conclusions can be drawn regarding the efficacy of tai chi exercise on bone health. Further well designed studies with low risk of bias are needed.
Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 × 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement.
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