Respiratory motion analysis and abdominal breathing detection using inertial measurement units and machine learning
10.3760/cma.j.cn421666-20230830-00684
- VernacularTitle:基于惯性测量单元和机器学习的呼吸模式检测
- Author:
Le JIAO
1
;
Yuanyuan TAO
1
;
Huaping JIN
1
;
Qingqing ZHOU
1
;
Shasha LIU
1
;
Hongjun ZHU
1
Author Information
1. 苏州大学附属第一医院康复医学科，苏州 215006
- Publication Type:Journal Article
- Keywords:
Breathing exercises;
Machine learning;
Motion capture;
Inertial measurement units;
Abdominal breathing
- From:
Chinese Journal of Physical Medicine and Rehabilitation
2025;47(10):929-935
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.
