RespEnh: A Technique for Enhancing Respiration Sensing in Interference Scenarios With Wi-Fi Signal

Wi-Fi-based non-contact respiratory monitoring technology plays a significant role in smart elderly care by eliminating the need for users to wear additional hardware devices. Although respiration sensing has shown impressive accuracy in ideal scenarios, achieving precise respiration monitoring in interfering scenarios, such as when other users are engaged in activities, remains challenging. This difficulty arises primarily from the weak nature of the respiratory signal, which is susceptible to interference from activities. To address this issue, this paper introduces the RespEnh system, a respiratory monitoring system that utilizes human location information to mitigate interference. Firstly, We enhance the quality of respiratory signal by leveraging the frequency diversity of Wi-Fi signal, effectively visualizing respiratory patterns even amidst interference. Secondly, we apply short time window shift to remove walking noise in the time domain while preserving respiratory signal characteristics. Finally, we integrate multi-antenna signals to enhance spatial domain performance. Experimental results demonstrate that our system achieves a relative anti-interference performance of 2.4m, even when breathing as far away as 6.4m. It effectively monitors person’s respiration, with a detection rate of 80% within an error range of 1bpm.