To address the issues of low detection accuracy and poor real-time performance in current fall detection systems, a fall monitoring system based on multisensor information fusion has been designed. The system is centered around the ESP32 microprocessor and utilizes sensors embedded in smartphones, pressure film sensors, and MPU6050 sensors for data collection. Health data is displayed in real-time through a mini-program interface, providing monitoring and alert functions. A collaborative cloud-edge fall detection method has been proposed, combining a local multi-threshold algorithm with an improved SSA-LSTM-Transformer algorithm and data fusion weights in the cloud. This algorithm has been validated on a public dataset, achieving an accuracy rate of 99.13%. Finally, system validation was performed through experiments, and the results showed that the system′s fall detection accuracy is 97.67%. It effectively detects falls and provides real-time positioning and alerts.