# Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches

arXiv: https://arxiv.org/abs/2212.05062 · HTML: https://mlsanity.com/paper/2212.05062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Online Human Activity Recognition using Low-Power Wearable Devices](https://mlsanity.com/paper/1808.08615/) (2018)
- [Learning Attribute Representation for Human Activity Recognition](https://mlsanity.com/paper/1802.00761/) (2018)
- [Human activity recognition based on time series analysis using U-Net](https://mlsanity.com/paper/1809.08113/) (2018)
- [Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks](https://mlsanity.com/paper/1702.06212/) (2017)
- [Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer](https://mlsanity.com/paper/1711.00096/) (2017)

## Built on by (most cited first, 1 of 1)

- [Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems](https://mlsanity.com/paper/2405.04241/) (2024)

## Abstract (arXiv)

Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.