MMTA: Multi Membership Temporal Attention for Fine-Grained Stroke Rehabilitation Assessment

 

What will it take to get thru your thick skulls that 'assessments' do nothing for recovery unless THEY POINT DIRECTLY TO EXACT RECOVERY PROTOCOLS?

This did nothing towards that, so useless!

You do have an incredible word salad that DOES NOTHING FOR SURVIVORS! You're all fired for stupidity! Hope you like being disabled when you become the 1 in 4 per WHO that has a stroke! 

MMTA: Multi Membership Temporal Attention for Fine-Grained Stroke Rehabilitation Assessment

• Ismail Helvaci, Halil ;
 
• Huber, Justin ;
 
• Bae, Jihye ;
 
• Samson Cheung, Sen-ching

Abstract

To empower the iterative assessments involved during a person's rehabilitation, automated assessment of a person's abilities during daily activities requires temporally precise segmentation of fine-grained actions in therapy videos. Existing temporal action segmentation (TAS) models struggle to capture sub-second micro-movements while retaining exercise context, blurring rapid phase transitions and limiting reliable downstream assessment of motor recovery. We introduce Multi-Membership Temporal Attention (MMTA), a high-resolution temporal transformer for fine-grained rehabilitation assessment. Unlike standard temporal attention, which assigns each frame a single attention context per layer, MMTA lets each frame attend to multiple locally normalized temporal attention windows within the same layer. We fuse these concurrent temporal views via feature-space overlap resolution, preserving competing local contexts near transitions while enabling longer-range reasoning through layer-wise propagation. This increases boundary sensitivity without additional depth or multi-stage refinement. MMTA supports both video and wearable IMU inputs within a unified single-stage architecture, making it applicable to both clinical and home settings. MMTA consistently improves over the Global Attention transformer, boosting Edit Score by +1.3 (Video) and +1.6 (IMU) on StrokeRehab while further improving 50Salads by +3.3. Ablations confirm that performance gains stem from multi-membership temporal views rather than architectural complexity, offering a practical solution for resource-constrained rehabilitation assessment.

Publication:

 

eprint arXiv:2603.00878

 

Pub Date:

 

March 2026

 

DOI:

 

10.48550/arXiv.2603.00878 

 

arXiv:

 

arXiv:2603.00878 

 

Bibcode: