No abstract

The aim of the current study was to investigate the effect of low-intensity, Kinect™-based Kaimai-style Qigong exercise among older adults with type 2 diabetes mellitus (T2DM). A non-randomized controlled trial was conducted in a community center in Changchun, China, from July to September 2017. Participants ( N = 55) were assigned to the Qigong group or control group. Participants in the Qigong group received 30-minute Kinect-based Kaimai-style Qigong sessions three times per week for 12 weeks. Participants in the control group were waitlisted. Body mass index (BMI), glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and lipid level, as well as Mini-Mental State Examination and Berg Balance Scale scores, were measured at baseline and postintervention. The Qigong group showed decreases in weight ( p = 0.002), BMI ( p = 0.004), HbA1c ( p = 0.001), and FBG ( p = 0.018) after the intervention. The Qigong group also showed improvements in cognitive ( p ≤ 0.001) and balance ( p ≤ 0.001) function. The Kinect-based Kaimai-style Qigong intervention was effective in reducing HbA1c and improving balance and cognitive function in older adults with T2DM. [ Journal of Gerontological Nursing, 45 (2), 42–52.]

Abstract.As an effective survey method of upper limb disorder, rapid upper limb assessment (RULA) has a wide application in industry period.However, it is very difficult to rapidly evaluate operator's postures in real complex work place.In this paper, a real-time RULA method is proposed to accurately assess the potential risk of operator's postures based on the somatosensory data collected from Kinect sensor, which is a line of motion sensing input devices by Microsoft.First, the static position information of each bone point is collected to obtain the effective angles of body parts based on the calculating methods based on joints angles. Second, a whole RULA score of body is obtained to assess the risk level of current posture in real time. Third, those RULA scores are compared with the results provided by a group of ergonomic practitionerswho were asked to observe the same static postures. All the experiments were carried out in an ergonomic lab. The results show that the proposed method can detect operator's postures more accurately. What's more, this method is applied in a real-time condition which can improve the evaluating efficiency.

Indoor air pollution is a serious problem that harms people's health. Indoor environment monitoring system could make contribution to detecting and improving indoor air quality. However, besides the high cost, the existing air quality detectors also get tricky problems with real-time monitoring and autonomous mobile which lead to a low practicability in most cases. Therefore this paper put forward an intelligent mobile indoor environment monitoring system based on Arduino's control using various sensors to detect air quality. The results showed that the data detected by our system got high positive correlation property with data from standard technical detecting apparatus and no obvious differences appeared. The system was more advanced and effective which could lead to great application value.