UNOBTRUSIVE RESPIRATORY MONITORING SYSTEM FOR INTENSIVE CARE

The video-based non-contact respiration detection technology can be used in many application scenarios to unobtrusively and ubiquitously monitor the physical state of living beings. The optical flow method in tandem with crossover point method is rather effective for respiration rate extraction. However, each method has one disadvantage: 1) the redundant feature points in the traditional optical flow method increase the computational effort and reduce the estimation accuracy; and 2) the traditional crossover point method suffers from crossover points unrelated to breathing movements. Two optimization points are proposed 1) optimize feature point space by combining spatio-temporal information; and 2) use negative feedback design to adaptively remove crossovers unrelated to respiratory movements. The algorithm performance is validated by LBRD-IC dataset. Field measurements have shown that our algorithm can measure respiratory signals when only one surveillance camera is present.