Sensor-integrated Deep Learning Model for Elderly Care Coordination in Industrial Transition Regions of China: Architecture, Calibration, and Validation

Rapid population aging and industrial restructuring make coordination between public elderly care services (PECS) and the elderly care industry (ECI) critical for improving healthcare outcomes in transition regions.In this study, a modified quadruple-subject coordination model (government-market-family-unit) was introduced to develop a sensor-integrated long short-term memory (LSTM)-deep neural network (DNN) model and evaluate the coupling coordination degree (CCD) of elderly healthcare systems.Multi-modal sensor data, including the global navigation satellite system (GNSS) parsing of National Marine Electronics Association (NMEA) 0183 Standard, the 2.4 GHz active RF identification tracking of electronic product code strings, mattress-embedded piezoresistive force sensors, dual-beam passive infrared (PIR) arrays, and 13.56 MHz near-field communication (NFC) handshakes, was collected across 14 prefecture-level cities in Liaoning Province, China.To fuse asynchronous data, a two-stage synchronization protocol employing rolling median filtering and bucket-aggregation grids was implemented before conducting sequence learning.Liaoning's CCD increased from 0.54 in 2018 (barely coordinated) to 0.79 in 2023 (moderately coordinated), progressing through preliminary, policy-driven, and optimization stages.The hybrid model eliminated gradient instability, reducing the root mean square error by 29.3% and achieving an 2 of 0.923 compared with models lacking physical sensing layers.Governance constraints were identified, including urban-rural resource imbalance and a 1.2-year policy execution latency.By integrating explicit time-delay variables and Kalman filtering and adaptive drift correction, the developed model provides a reproducible, scalable approach for sensor-based healthcare governance evaluation in industrial transition regions.