Edge-Server Web Architecture for Real-Time Biomedical Sensor Networks

With the increasing adoption of edge computing and wearable sensing in biomedical applications, efficient architectures for real-time acquisition, processing, visualization, and storage of sensor data are becoming increasingly important. This paper presents the design and evaluation of an edge-computing web architecture for a multi-sensor biomedical monitoring system intended to support Liver Frailty Index (LFI)-related measurements and future LFI research. The study evaluates the supporting software platform; clinical validation of sensor-derived LFI measurements and verification of biomechanical algorithms were not part of this research. The physical acquisition layer uses wearable inertial measurement units (IMUs) connected through a distributed gateway architecture, while the main focus of the study is the software and edge-server layers. FastAPI, default Django, and a reduced-middleware Django configuration were evaluated on ASUS PN53, Raspberry Pi 5, and NVIDIA Jetson Orin Nano using controlled Hypertext Transfer Protocol (HTTP) and WebSocket workloads. FastAPI achieved the highest HTTP throughput and lowest latency for every tested endpoint and platform; for the ping endpoint, mean throughput was 7185, 3181, and 1250 requests/s on the ASUS PN53, Raspberry Pi 5, and Jetson Orin Nano, respectively. Under 1000 concurrent WebSocket connections, FastAPI maintained P95 message round-trip times of 5.3, 13, and 20 ms on the same platforms, while the Django configurations showed substantially stronger degradation at high concurrency. A separate eight-stream synthetic workload showed that all three platforms sustained the 1440 samples/s physical reference rate and approximately 5000 samples/s with at least 99% of the requested message rate; the Jetson Orin Nano also sustained approximately 10,000 samples/s at the highest tested level. These results demonstrate that server-framework selection strongly affects edge responsiveness and that the proposed FastAPI-based architecture provides headroom for the specified synthetic server-side workload only, rather than a demonstrated end-to-end clinical performance guarantee.