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原文摘要

Sensor-based human activity recognition runs on edge devices, trading performance against latency, memory, and energy. Yet new architectures are usually compared with few, often outdated baselines on few datasets with study-specific data preparation and hardware, leaving open which perform best at what cost. We present HARENA, a living benchmark with a public leaderboard, to which new architectures and datasets can be added under one protocol. Its initial suite evaluates 16 neural architectures and three classical baselines on 29 datasets, measuring performance on unseen subjects and deployment cost on a smartphone, smartwatch, and microcontroller. CNN-HAR, the suite's oldest deep architecture, leads overall, but 8 architectures lie within 2.5 points of its mean test macro-F1, while their cost differs by orders of magnitude. On the microcontroller, Random Forest leads on latency and peak RAM, LITEWAY-Light on flash, and TinyHAR is most accurate within the real-time budget. Rankings shift with dataset and channel count, so general claims require many datasets, which a growing benchmark can provide. We find no sign of benchmark overfitting, but performance drops by a median of 15.0 points on unseen subjects, about three times the typical spread between architectures, making generalization and adaptation the most promising future directions.