# PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training

arXiv: https://arxiv.org/abs/2112.11330 · HTML: https://mlsanity.com/paper/2112.11330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 69 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Alleviating Over-segmentation Errors by Detecting Action Boundaries](https://mlsanity.com/paper/2007.06866/) (2020)
- [Towards data-driven stroke rehabilitation via wearable sensors and deep learning](https://mlsanity.com/paper/2004.08297/) (2020)
- [Pragmatic classification of movement primitives for stroke rehabilitation](https://mlsanity.com/paper/1902.08697/) (2019)
- [Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution](https://mlsanity.com/paper/2111.02521/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Potential and Limitations of Vision-Language Models for Human Motion Understanding: A Case Study in Data-Driven Stroke Rehabilitation](https://mlsanity.com/paper/2511.17727/) (2025)
- [After-Stroke Arm Paresis Detection using Kinematic Data](https://mlsanity.com/paper/2311.16138/) (2023)

## Abstract (arXiv)

Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetitions. The optimal training content and quantity are currently unknown because no practical tools exist to measure them. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into component functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that these advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.