Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises

 Oh, your incompetent? doctor and therapists haven't been using wearable sensors to objectively determine your balance and walking problems? So they can then provide EXACT REHAB PROTOCOLS THAT DELIVER RECOVERY! Oh, they incompetently haven't even attempted to figure out how to get you recovered, have they?

Let's check how long INCOMPETENCE HAS REIGNED!

Sensors and wearables have been out for years and obviously NOTHING HAS BEEN DONE. 

• motion sensors (16 posts to August 2012)

• motion-capture sensors (1 post to May 2017)

• inertial sensors (7 posts to November 2014)

• motion sensors(16 posts to August 2012)

• sensors (7 posts to March 2014)

• wearable arms (1 post to May 2013)

• wearable (17 posts to April 2012)

• wearable computing (3 posts to August 2013)

• wearable devices (34 posts to October 2016)

• wearable electronic device (1 post to July 2020)
  

• Wearable inertial measurement units (2 posts to June 2019)

• wearable sensors (16 posts to January 2018)

• 3d inertial sensors(1 post to August 2017)

• wearable shoe (1 post to December 2019)

  The latest here: 

Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises

• Safa Jabri,
• Jeremiah Hauth,
• Christopher DiCesare,
• Wendy Carender,
• Lauro Ojeda,
• Jenna Wiens,
• Leia Stirling,
• Xun Huan &
• Kathleen H. Sienko 

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

Background

Effective balance rehabilitation requires training at an appropriate level of exercise intensity given an individual’s needs and abilities. Typically balance intensity is assessed through in-clinic visual observation by physical therapists (PTs), which limits the ability to monitor and progress intensity during home-based components of training programs. The goal of this study was to train and evaluate machine learning models for estimating physical therapists’ perceived balance exercise intensity using data from full-body wearable sensors to support the development of home-based training exercise dosage monitoring.

Methods

Balance exercise participants (n = 47) participated in a single-day balance training session where they were filmed performing static standing exercises at various levels of intensity. Kinematic data from 13 full-body wearable inertial measurement units (IMUs) and self-ratings of balance intensity were also collected. An additional cohort of PT participants (n = 42) was recruited to watch the videos of the balance exercise participants and provide ratings of balance intensity. The mean PT rating for each video was used as a ground truth (GT) label of balance intensity. We trained and evaluated Convolutional Neural Networks (CNN)-based models to predict balance intensity based on performance as captured through the IMUs. Model performance was evaluated by calculating the root-mean-square error (RMSE) of predications. A sensitivity analysis was also performed to assess the effect of the number of IMUs used on model performance.

Results

Models trained on orientation derived from all 13 IMUs achieved good predictive performance as indicated by a RMSE of 0.66 [0.62, 0.69], which was within the threshold defined by typical inter-rater variabilities between PTs (RMSE of 0.74 [0.72, 0.76]). Sensitivity analysis indicated that model performance stabilized at four sensors with the best performance corresponding to sensors placed on both thighs and the lower and upper back.

Conclusions

Findings from this study indicated that balance intensity assessment can be achieved through wearable sensors and a CNN model, which could support the supervision and effectiveness of home-based balance rehabilitation.