# Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures

arXiv: https://arxiv.org/abs/2210.01209 · HTML: https://mlsanity.com/paper/2210.01209/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 3 Oct 2022.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables](https://mlsanity.com/paper/1604.08880/) (2016)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [A Deep Learning Framework for Assessing Physical Rehabilitation Exercises](https://mlsanity.com/paper/1901.10435/) (2019)
- [A review of 3D human pose estimation algorithms for markerless motion capture](https://mlsanity.com/paper/2010.06449/) (2020)
- [Personalizing deep learning models for automatic sleep staging](https://mlsanity.com/paper/1801.02645/) (2018)
- [Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study](https://mlsanity.com/paper/1812.03880/) (2018)

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

- [Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation](https://mlsanity.com/paper/2505.24415/) (2025)
- [Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation](https://mlsanity.com/paper/2607.04842/) (2026)

## Abstract (arXiv)

(1) Background: The success of physiotherapy depends on the regular and correct performance of movement exercises. A system that automatically evaluates these could support the therapy. Previous approaches in this area rarely rely on Deep Learning methods and do not yet fully use their potential. (2) Methods: Using a measurement system consisting of 17 IMUs, a dataset of four Functional Movement Screening (FMS) exercises is recorded. Exercise execution is evaluated by physiotherapists using the FMS criteria. This dataset is used to train a neural network that assigns the correct FMS score to an exercise repetition. We use an architecture consisting of CNN, LSTM and Dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform a extensive hyperparameter optimization. In addition, we are comparing different CNN structures that have been specifically adapted for use with IMU data. Finally, the developed network is trained with the data of different FMS exercises and the performance is compared. (3) Results: The evaluation shows that the presented approach achieves a convincing performance in the classification of unknown repetitions of already known subjects. However, the trained network is yet unable to achieve consistent performance on the data of a previously unknown subjects. Additionally, it can be seen that the performance of the network differs significantly depending on the exercise it is trained for.