This paper extends a previously proposed fall prediction algorithm to a real-time (online) setting, with implementations in both hardware and simulation. The system is validated on the full-sized bipedal robot Digit, where the real-time version achieves performance comparable to the offline implementation while maintaining a zero false positive rate, an average lead time (defined as the difference between the true and predicted fall time) of 1.1s (well above the required minimum of 0.2s), and a maximum lead time error of just 0.03s. It also achieves a high recovery rate of 0.97, demonstrating its effectiveness in real-world deployment. In addition to the real-time implementation, this work identifies key limitations of the original algorithm, particularly under omnidirectional faults, and introduces a fine-tuned strategy to improve robustness. The enhanced algorithm shows measurable improvements across all evaluated metrics, including a 0.05 reduction in average false positive rate and a 1.19s decrease in the maximum error of the average predicted lead time.

翻译：本文扩展了先前提出的跌倒预测算法，将其应用于实时（在线）场景，并在硬件和仿真中均实现了部署。该系统在等身双足机器人Digit上进行了验证，其实时版本在保持零误报率的同时，性能与离线实现相当，平均预警时间（定义为真实跌倒时间与预测跌倒时间之差）达到1.1秒（远高于0.2秒的最低要求），最大预警时间误差仅为0.03秒。该系统还实现了0.97的高恢复率，证明了其在实际部署中的有效性。除了实时实现外，本研究还指出了原始算法在关键限制，尤其是在全向故障下的不足，并引入了一种微调策略以提升鲁棒性。增强后的算法在所有评估指标上均显示出可量化的改进，包括平均误报率降低0.05，以及平均预测预警时间的最大误差减少1.19秒。