Human sleep motion recognition based on multi-sensor measurement data fusion

Sensor-based sleep motion recognition (SMR) can effectively monitor human health and quality of life, but traditional methods have privacy and security risks. Therefore, this paper uses accelerometers and electrocardiograms (ECG) sensors to measure human sleep behavior data and proposes an SMR method based on the Fibonacci ladder search-multilayer extreme learning machine (FLS-MLELM). Firstly, an improved quantum genetic algorithm and a multiple bidirectional long short-term memory network are utilized to extract ECG-heart rate variability signals and motion acceleration signals from human sleep, respectively. Secondly, the FLS algorithm is employed to optimize the hyperparameters of MLELM, thereby improving the recognition accuracy of the model. Finally, in these two scenarios, the recognition accuracy of this method reached 95.54% (Chinese PLA General Hospital-SMR dataset) and 95.07% (self-collected SMR datasets), improving by 1.59%–25.72% and 2.31%–24.95%, respectively, compared to traditional algorithms. This proves that the proposed recognition method has potential application prospects in smart sleep-monitoring systems.