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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.

For linear accelerometers, calibration with a precision centrifuge is a key technology, and the input acceleration imposed on the accelerometer should be accurately obtained in the calibration. However, there are often errors in the installation of sample that make the calibration inaccurate. To solve installation errors and obtain the input acceleration in the calibration of the accelerometer, a calibration method based on the rotation principle using a double turntable centrifuge is proposed in this work. The key operation is that the sub-turntable is rotated to make the input axis of the accelerometer perpendicular to the direction of the centripetal acceleration vector. Models of installation errors of angle and radius were built. Based on these models, the static radius and input acceleration can be obtained accurately, and the calibration of the scale factor, nonlinearity and asymmetry can be implemented. Using this method, measurements of the MEMS accelerometer with a range of ±30 g were carried out. The results show that the discrepancy of performance obtained from different installation positions was smaller than 100 ppm after calibrating the input acceleration. Moreover, the results using this method were consistent with those using the back-calculation method. These results demonstrate that the effectiveness of our proposed method was confirmed. This method can measure the static radius directly eliminating the installation errors of angle and radius, and it simplifies the accelerometer calibration procedure.