Textile Reflexes – Wearable Breathing Trainer

The wearable breathing trainer is a project by Hellen van Rees together with the following partners: University of Twente, Medisch Spectrum Twente, Saxion University of Applied Science and Ocon. The project is made possible by the Pioneers in Healthcare fund and the Dutch Creative Industries Fund.

Dysfunctional breathing is defined as an inappropriate mode of breathing, causing respiratory and non-respiratory symptoms. A dysfunctional breathing pattern can present with a broad spectrum of symptoms mimicking a variety of respiratory diseases. Frequent symptoms are excessive upper chest and accessory muscle activity mimicking asthma, chest pain/tightness, hyperventilation, and breathlessness during submaximal exercise. Symptoms usually are aggravated by stress and recover by relaxation, and there is no decline in lung function. Children with dysfunctional breathing are frequently referred to a physical therapist, who educates children in self-assessment and works on improvement of breathing technique.

Figure 1. Concept design of the wearable breathing coach

Children with respiratory disorders are frequently referred to a physical therapist, who educates children in self-assessment and works on improvement of breathing technique. However, there is a lack of tools that can support children during breathing re-training and guide and support caretakers and healthcare professionals to monitor the progress of skills, such as selfassessment and breathing technique. Using a breathing trainer, made of robotic textile and equipped with sensor technology (see Figure 1) we aim to combine the measurement of relevant respiratory parameters with robotic textile and LED’s to provide a system which can both detect and analyse respiratory disorders and provide real-time haptic and visual feedback during breathing retraining.

The combination with a mobile application facilitates engagement and motivation for the child. This grants children, parents and health care providers valuable information regarding the origin and severity of symptoms allowing improved self-management. This allows accurate progress monitoring during training and aids both patient and healthcare provider with additional feedback.

The progress and initial results of this project have been exhibited and published on several occasions including the following:

  • Journal of Personalized Medicine (here)
  • Techmed Event (The Netherlands)
  • Ars Elektronica (Austria)