论文标题
Xai-bayeshar：具有整合不确定性和外形价值的人类活动识别的新型框架
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values
论文作者
论文摘要
使用IMU传感器，即加速度计和陀螺仪的人类活动识别（HAR）在智能家居，医疗保健和人机接口系统中具有多种应用。实际上，由于传感器退化，外星环境或传感器噪声，预计基于IMU的HAR系统将在测量中遇到变化，并将受到未知活动。鉴于解决方案的实际部署，对活动类评分的统计信心分析是重要的指标。因此，在本文中，我们提出了一个集成的贝叶斯框架Xai-bayeshar，通过递归跟踪特征嵌入向量及其相关的不确定性，从而提高了基于IMU的HAR解决方案的整体活动分类精度。此外，Xai-bayeshar使用预测不确定性充当数据分布（OOD）检测器，这些不确定性有助于评估和检测外来输入数据分布。此外，还评估了基于Shapley的基于Shapley的价值的性能，以了解嵌入矢量的特征的重要性，并因此用于模型压缩
Human activity recognition (HAR) using IMU sensors, namely accelerometer and gyroscope, has several applications in smart homes, healthcare and human-machine interface systems. In practice, the IMU-based HAR system is expected to encounter variations in measurement due to sensor degradation, alien environment or sensor noise and will be subjected to unknown activities. In view of practical deployment of the solution, analysis of statistical confidence over the activity class score are important metrics. In this paper, we therefore propose XAI-BayesHAR, an integrated Bayesian framework, that improves the overall activity classification accuracy of IMU-based HAR solutions by recursively tracking the feature embedding vector and its associated uncertainty via Kalman filter. Additionally, XAI-BayesHAR acts as an out of data distribution (OOD) detector using the predictive uncertainty which help to evaluate and detect alien input data distribution. Furthermore, Shapley value-based performance of the proposed framework is also evaluated to understand the importance of the feature embedding vector and accordingly used for model compression
