Abstract
Artificial intelligence has the potential to impact safety and efficiency in safety-critical domains such as construction, manufacturing, and healthcare. For example, using sensor data from wearable devices, such as inertial measurement units (IMUs), human gestures can be detected while maintaining privacy, thereby ensuring that safety protocols are followed. However, strict safety requirements in these domains have limited the adoption of AI, since accurate calibration of predicted probabilities and robustness against out-of-distribution (OOD) data is necessary. This paper proposes UAC (Uncertainty-Aware Calibration), a novel two-step method to address these challenges in IMU-based gesture recognition. First, we present an uncertainty-aware gesture network architecture that predicts both gesture probabilities and their associated uncertainties from IMU data. This uncertainty is then used to calibrate the probabilities of each potential gesture. Second, an entropy-weighted expectation of predictions over multiple IMU data windows is used to improve accuracy while maintaining correct calibration. Our method is evaluated using three publicly available IMU datasets for gesture detection and is compared to three state-of-the-art calibration methods for neural networks: temperature scaling, entropy maximization, and Laplace approximation. UAC outperforms existing methods, achieving improved accuracy and calibration in both OOD and in-distribution scenarios. Moreover, we find that, unlike our method, none of the state-of-the-art methods significantly improve the calibration of IMU-based gesture recognition models. In conclusion, our work highlights the advantages of uncertainty-aware calibration of neural networks, demonstrating improvements in both calibration and accuracy for gesture detection using IMU data.
Abstract (translated)
人工智能在建筑、制造和医疗保健等关键安全领域具有影响安全性和效率的潜力。例如，通过穿戴设备中的传感器数据（如惯性测量单元(IMUs)），可以检测人类手势并确保隐私的同时遵守安全规程。然而，在这些领域的严格安全要求限制了AI的应用，因为需要准确校准预测概率，并且必须具备应对分布外(OOD)数据的强大能力。本文提出了一种名为UAC(不确定性感知校准)的新型两步方法，旨在解决基于IMU的手势识别中的上述挑战。 首先，我们介绍了一种不确定性感知手势网络架构，该架构可以从IMU数据中预测手势概率及其相关不确定性。然后使用这种不确定性来校正每个潜在手势的概率。其次，在多个IMU数据窗口上利用熵加权的预测期望值以提高准确性的同时保持正确的校准效果。我们的方法在三个公开可用的手势检测IMU数据集中进行了评估，并与三种最先进的神经网络校准方法（温度缩放、熵最大化和拉普拉斯近似）进行比较。 实验结果表明，UAC优于现有方法，在分布内和分布外场景中均实现了更高的准确性和更好的校准效果。此外，我们发现，与其他方法不同的是，我们的方法显著提高了基于IMU的手势识别模型的校准性能。总之，这项工作突显了神经网络不确定性感知校准的优势，并展示了使用IMU数据进行手势检测时在准确性和校准方面的改进。
URL
https://arxiv.org/abs/2504.02895
