Movement-Specific Analysis for FIM Score Classification Using Spatio-Temporal Deep Learning

TL;DR

This paper introduces a deep learning approach combining spatial-temporal graph convolutional networks, BiLSTM, and attention mechanisms to automate FIM score classification from simple exercises, reducing assessment burden.

Contribution

It presents a novel deep neural network architecture tailored for movement-specific FIM score estimation using simple exercises, capturing long-term dependencies and key joint contributions.

Findings

Achieved 70.09-78.79% balanced accuracy in classifying FIM items.

Effectively distinguished independent from assistance-requiring patients.

Identified movement patterns as predictors for FIM scores.

Abstract

The functional independence measure (FIM) is widely used to evaluate patients' physical independence in activities of daily living. However, traditional FIM assessment imposes a significant burden on both patients and healthcare professionals. To address this challenge, we propose an automated FIM score estimation method that utilizes simple exercises different from the designated FIM assessment actions. Our approach employs a deep neural network architecture integrating a spatial-temporal graph convolutional network (ST-GCN), bidirectional long short-term memory (BiLSTM), and an attention mechanism to estimate FIM motor item scores. The model effectively captures long-term temporal dependencies and identifies key body-joint contributions through learned attention weights. We evaluated our method in a study of 277 rehabilitation patients, focusing on FIM transfer and locomotion items. Our approach successfully distinguishes between completely independent patients and those requiring assistance, achieving balanced accuracies of 70.09-78.79 % across different FIM items. Additionally, our analysis reveals specific movement patterns that serve as reliable predictors for particular FIM evaluation items.