Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals

Feasibility of a portable respiratory training system with a gyroscope sensor

OBJECTIVES

A portable respiratory training system with a gyroscope sensor (gyroscope respiratory training system [GRTS]) was developed and the feasibility of respiratory training was evaluated.

METHODS

Simulated respiratory waveforms from a respiratory motion phantom and actual respirator waveforms from volunteers were acquired using the GRTS and Respiratory Gating for Scanners system (RGSC). Respiratory training was evaluated by comparing the stability and reproducibility of respiratory waveforms from patients undergoing expiratory breath-hold radiation therapy, with and without the GRTS. The stability and reproducibility of respiratory waveforms were assessed by root mean square error and gold marker placement-based success rate of expiratory breath-hold, respectively.

RESULTS

The absolute mean difference for sinusoidal waveforms between the GRTS and RGSC was 2.0%. Among volunteers, the mean percentages of errors within ±15% of the respiratory waveforms acquired by the GRTS and RGSC were 96.1% for free breathing and 88.2% for expiratory breath-hold. The mean root mean square error and success rate of expiratory breath-hold (standard deviation) with and without the GRTS were 0.65 (0.24) and 0.88 (0.89) cm and 91.0% (6.9) and 89.1% (11.6), respectively.

CONCLUSIONS

Respiratory waveforms acquired by the GRTS exhibit good agreement with waveforms acquired by the RGSC. Respiratory training with the GRTS reduces inter-patient variability in respiratory waveforms, thereby improving the success of expiratory breath-hold radiation therapy.

ADVANCES IN KNOWLEDGE

A respiratory training system with a gyroscope sensor is inexpensive and portable, making it ideal for respiratory training. This is the first report concerning clinical implementation of a respiratory training system.

Sensor Selection for Tidal Volume Determination via Linear Regression-Impact of Lasso versus Ridge Regression

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O (n2)). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.

Optimal Positioning of Inertial Measurement Units in a Smart Shirt for Determining Respiratory Volume

Tidal volume can be estimated using the surface motions of the upper body induced by respiration. However, the precision and instrumentation of such estimation must be improved to allow widespread application. In this study, respiration induced changes in parameters that can be recorded with inertial measurement units are examined to determine tidal volumes. Based on the data of an optical motion capture system, the optimal positions of inertial measurement units (IMU) in a smart shirt for sets of 4, 5 or 6 sensors were determined. The errors observed indicate the potential to determine tidal volumes using IMUs in a smart shirt.Clinical Relevance- The measurement of respiratory volumes via a low-cost and unobtrusive smart shirt would be a major advance in clinical diagnostics. In particular, conventional methods are expensive, and uncomfortable for conscious patients if measurement is desired over an extended period. A smart-shirt based on inertial sensors would allow a comfortable measurement and could be used in many clinical scenarios - from sleep apnoea monitoring to homecare and respiratory monitoring of comatose patients.