MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Distillation

In recent decades, the rising aging population has increased the importance of fall detection (FD) systems as assistive technology. To enhance accuracy, deep learning (DL) techniques are widely applied to these systems. A typical FD system involves placing edge devices (EDs) on individuals to collect real-time data, which is transmitted to a cloud center (CC) or processed by the ED itself. However, this architecture faces challenges such as limited ED model size and data transmission latency to the CC. Mobile edge computing (MEC) has been explored to overcome these challenges, which allows computations at MEC servers deployed between EDs and the CC. A multilayer mobile edge computing (MLMEC) framework is proposed to manage the tradeoff between accuracy and latency. MLMEC divides the architecture into multiple stations, each equipped with a neural network model. If the front-end equipment lacks deterministic detection capability, data are transmitted to a station with robust back-end computing for detection. The knowledge distillation (KD) approach is employed to improve front-end detection accuracy. The KD approach allows high-power back-end stations to provide additional learning experiences, enhancing precision and reducing latency and processing load. Simulation results demonstrate improved accuracy with the KD approach (11.65% average improvement for the SisFall dataset and 2.78% for the FallAllD dataset). The proposed MLMEC with the KD approach outperforms without the KD approach regarding data latency rate by 54.15% for the FallAllD dataset and 46.67% for the SisFall dataset. In summary, the MLMEC FD system exhibits improved accuracy and reduced latency.