.png){kind=icon}
返回
X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model
.png){kind=icon}
OA
AI
DOI：10.1145/3580804.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
End-to-end deep learning models are increasingly applied to safety-critical human activity recognition (HAR) applications, e.g., healthcare monitoring and smart home control, to reduce developer burden and increase the performance and robustness of prediction models. However, integrating HAR models in safety-critical applications requires trust, and recent approaches have aimed to balance the performance of deep learning models with explainable decision-making for complex activity recognition. Prior works have exploited the compositionality of complex HAR (i.e., higher-level activities composed of lower-level activities) to form models with symbolic interfaces, such as concept-bottleneck architectures, that facilitate inherently interpretable models. However, feature engineering for symbolic concepts-as well as the relationship between the concepts-requires precise annotation of lower-level activities by domain experts, usually with fixed time windows, all of which induce a heavy and error-prone workload on the domain expert. In this paper, we introduce X-CHAR, an eXplainable Complex Human Activity Recognition model that doesn't require precise annotation of low-level activities, offers explanations in the form of human-understandable, high-level concepts, while maintaining the robust performance of end-to-end deep learning models for time series data. X-CHAR learns to model complex activity recognition in the form of a sequence of concepts. For each classification, X-CHAR outputs a sequence of concepts and a counterfactual example as the explanation. We show that the sequence information of the concepts can be modeled using Connectionist Temporal Classification (CTC) loss without having accurate start and end times of low-level annotations in the training dataset-significantly reducing developer burden. We evaluate our model on several complex activity datasets and demonstrate that our model offers explanations without compromising the prediction accuracy in comparison to baseline models. Finally, we conducted a mechanical Turk study to show that the explanations provided by our model are more understandable than the explanations from existing methods for complex activity recognition.
Keyword:
Activity recognition
Neural networks
Explainable AI
Interpretability
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
4.5
论文数:
1.1K
被引数:
7.2K
机构
引用论文
分享收藏Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening使用测试数据锐化的基于分而治之的一维CNN人体活动识别.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Clinically applicable deep learning for diagnosis and referral in retinal disease深度学习在视网膜疾病诊断和转诊中的临床应用NATURE MEDICINE
NATURE MEDICINEIF50
PREAI
分享收藏A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction基于交互的智能家居人体行为识别与预测SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Deep learning based multimodal complex human activity recognition using wearable devicesAPPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏Effect of Ag doping on structural, optical, complex impedance and photovoltaic properties of SnO2 nanoparticles prepared by co-precipitation method for dye sensitized solar cell applicationOptik
OptikIF0
PREAI
分享收藏
查看更多内容