A Noncontact Fall Detection Method for Bedside Application With a MEMS Infrared Sensor and a Radar Sensor

With the rapid development of economy, science, and technology, the aging issues become more and more serious. People aged above 65 have a risk of 28%–35% to fall. Among them, bedside falls happen most frequently. Therefore, the capability to detect fall events of the elderly is very important. In this article, a novel noncontact fall detector based on a MEMS low-resolution infrared sensor and a low-cost radar sensor is developed to detect bedside fall. Besides, IR image processing algorithms based on the adaptive filter, successive approximation, double boundary scans, and mathematical morphology processing are proposed in detail. Partition processing algorithm is used to suppress the influence of residual or existed heat sources on the bed or ground. Then, the statistical features of the center, area, temperature and duration, as well as stable flag and fall action flag, are extracted for fall recognition. Finally, a three-layer radial basis function neural network is applied to distinguish the fall events from the nonfall events. Considering the influence factors of ambient temperature, brightness, gender, dressing, fall posture, fall location, and scenario, a total of 640 tests are conducted and 5-fold cross validation is used to evaluate the classification performance. Experimental results indicate that the averages of the recall, precision, F1-Score, and detection accuracy are measured to be 91.25%, 94.76%, 92.97%, and 93.13%, respectively, which demonstrates that the proposed fall detection method is effective. Besides, the detection accuracy decreases from 96.88% to 85.94% as the ambient temperature rises. Hence, this noncontact fall detector can be widely applied for bedside fall detection at home, which is low cost, nonwearable, unobtrusive, noninvasive, and privacy preserved.