most citedExplaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative Measures

27 citations · 48 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2025

Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations

Kimji N. Pellano, Inga Strümke, Daniel Groos +3

Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skeleton-based Graph Convolutional Ne…

cs.LG2024★ 10 cited

Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition

Felix Tempel, Daniel Groos, Espen Alexander F. Ihlen +2

Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy. This is especially important in hi…

cs.CV2024★ 27 cited

Explaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative Measures

Felix Tempel, Espen Alexander F. Ihlen, Lars Adde +1

In Human Activity Recognition (HAR), understanding the intricacy of body movements within high-risk applications is essential. This study uses SHapley Additive exPlanations (SHAP)…

cs.CV2024★ 1 cited

Lightweight Neural Architecture Search for Cerebral Palsy Detection

Felix Tempel, Espen Alexander F. Ihlen, Inga Strümke

The neurological condition known as cerebral palsy (CP) first manifests in infancy or early childhood and has a lifelong impact on motor coordination and body movement. CP is one o…

cs.CV2024★ 10 cited

AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search

Felix Tempel, Inga Strümke, Espen Alexander F. Ihlen

This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs). HAR has gained att…

cs.LG2024

From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition

Kimji N. Pellano, Inga Strümke, Espen Alexander F. Ihlen

The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human-computer interac…