Monitoring wheelchair propulsion patterns: feasibility and validity of using wearable sensors

 Your incompetent? doctor doesn't have you using the stupid manual wheelchairs to get around in the hospital, right? One handed and one legged driving is idiotic beyond belief.  This is what you should be using; and you'll recover your hands and arms while doing it. 

The reason legs recover faster is that in order to get around you have to use them, lever wheelchairs would provide the same results. Much better arm and hand recovery. Is your hospital so fucking blind they can't see that? Or are they so incompetent they don't know about it? 

lever wheelchair (12 posts to May 2016) 

rowing wheelchair (7 posts to May 2016)

 

Do you prefer your doctor, hospital and board of director's incompetence NOT KNOWING? OR NOT DOING?

The latest here:

Monitoring wheelchair propulsion patterns: feasibility and validity of using wearable sensors

• Ramin Fathian,
• Aminreza Khandan,
• Nasim Rahmanifar,
• Chester Ho &
• Hossein Rouhani 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Currently, there is a need to understand the characteristics of manual wheelchair propulsion patterns in the daily life of users and the impact of these patterns on repetitive strain injury of the shoulders. This study aimed to develop a method for remote and long-term monitoring of wheelchair propulsion techniques. We used a hand-mounted inertial measurement unit (IMU) to identify propulsion patterns in manual wheelchair users. IMU data was collected from 12 participants (7 males and 5 females), including 8 experienced and 4 inexperienced manual wheelchair users. We applied continuous wavelet transform (CWT) for feature extraction and used Support Vector Machine (SVM) and Multilayer Perceptron (MLP) Neural Network for pattern classification. SVM with a linear kernel achieved 89% accuracy, 78% F1-score, 78% precision, and 78% recall. SVM with a polynomial kernel achieved 94% accuracy, 88% F1-score, 88% precision, and 89% recall, while the MLP reached 95% accuracy, 89% F1-score, 89% precision, and 89% recall. Neither the participants’ wheelchair experience nor their gender significantly affected the performance of the classifiers. These findings suggest that the proposed IMU and propulsion patterns classification method can be used across different user profiles for remote and long-term monitoring of wheelchair propulsion patterns to better understand shoulder overuse risk in daily life.

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.