# Kinematics-Based Predictions of External Loads during Handcycling

## TL;DR

This paper presents a machine learning approach to predict external loads during handcycling using wearable sensor data, aiming to improve safe exercise guidelines for wheelchair users.

## Contribution

A novel temporal convolutional network model is introduced for accurate prediction of six-degree-of-freedom loads during handcycling.

## Key findings

- The temporal convolutional network (TCN) outperformed other models in predicting external loads during handcycling.
- Predictions of in-plane forces and moments achieved high accuracy (r = 0.95–0.97).
- The model can predict loads across different exercise intensities, supporting diverse exercise protocols.

## Abstract

The increased risk of cardiovascular disease in people with spinal cord injuries motivates work to identify exercise options that improve health outcomes without causing risk of musculoskeletal injury. Handcycling is an exercise mode that may be beneficial for wheelchair users, but further work is needed to establish appropriate guidelines and requires assessment of the external loads. The goal of this research was to predict the six-degree-of-freedom external loads during handcycling from data similar to those which can be measured from inertial measurement units (segment accelerations and velocities) using machine learning. Five neural network models and two ensemble models were compared against a statistical model. A temporal convolutional network (TCN) yielded the best predictions. Predictions of forces and moments in-plane with the crank were the most accurate (r = 0.95–0.97). The TCN model could predict external loads during activities of different intensities, making it viable for different exercise protocols. The ability to predict the loads associated with forward propulsion using wearable-type data enables the development of informed exercise guidelines.