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Complementary metal oxide semiconductor (CMOS) cameras have been used to detect ionizing radiation when shaded from light or under the condition of static scenes. We propose a novel idea for simultaneous measurement of ionizing radiation and heart rate (HR) by using a smartphone with its CMOS camera lens covered by a finger in one measurement. Verified experiments were conducted: videos were recorded using a Xiaomi8 smartphone when the camera lens was covered by a finger and a light-tight tape for controlled experiment, with a ∼33 mCi 99mTc liquid radioactive source at six different distances (from 20 to 70 cm, step size 10 cm) from the camera. The exact HRs were measured using an oximeter at the same time. Image processing algorithm is proposed to extract radiation events and HR in the same videos. Results show that: (1) frame numbers containing radiation-related bright spots in two experiments have a linear relationship, and they are inversely proportional to the square of the distance between the camera and radiation source; (2) the HR difference between the video processing and the oximeter result is less than 2 beats per minute. In conclusion, it verifies that the proposed method is available to detect ionizing radiation and measure HR simultaneously with smartphone camera lens covered by a finger. We have been working on the development of an Android phone application based on the algorithms.

Nuclear energy is a clean and popular form of energy, but leakage and loss of nuclear material pose a threat to public safety. Radiation detection in public spaces is a key part of nuclear security. Common security cameras equipped with complementary metal oxide semiconductor (CMOS) sensors can help with radiation detection. Previous work with these cameras, however, required slow, complex frame-by-frame processing. Building on the previous work, we propose a nuclear radiation detection method using convolution neural networks (CNNs). This method detects nuclear radiation in changing images with much less computational complexity. Using actual video images captured in the presence of a common Tc-99m radioactive source, we construct training and testing sets. After training the CNN and processing our test set, the experimental results show the high performance and effectiveness of our method.