The University of Auckland

Project #17: Robust Sensor and App design for Biofeedback

Back

Description:

 

Stress related depressive disorders affect the sympathetic tone, which is strongly correlated to low heart rate variability (HRV). In these conditions the heart rate goes up and the variability is minimal. There is mounting clinical evidence to this support the link between the lack of HRV and the stress related disorders [1-4]. 

Biofeedback is a recently studied technique to maximise heart rate variability (HRV). Slow and rhythmic breathing rate of approximately 6 breaths a minute has been shown to achieve maximum HRV. This project seeks to develop a continuous feedback loop for patients to monitor the breathing rate and heart rate through appropriate sensors and give visual feedback using an App to patients. While some recent commercial sensors are available for combined heart and beating rate, these are very expensive. We propose a much simpler sensor for this and will compare this to the commercially available Zypher device. Students will learn about new sensor design and also software design for biofeedback. This project is suited to both computer systems and software engineering students. This project will be co-supervised by Dr. Giresh Kanji, who is a clinician with extensive experience in HRV.

This project will be a continuation of the 2019 version of the project. Here, a PPG sensor along with Arduino was used to design a sensor that can obtain the heart rate and the breathing rate accurately when the subject is resting. An App was also designed that used to provide the necessary visual feedback to facilitate maximal HRV through resonant breathing. We also have ethics approval for trial of this sensor on human subjects, which will be carried out in the 2020 project.

There are several limitations of the 2019 implementation. First one is related to the fact that the sensor works only when the subject is not moving. We need to develop robust techniques to develop the sensor when  the subject is moving by removing the motion artefacts. Also, the Arduino board can be eliminated so that the mobile device performs the necessary signal processing. 

References

1. Farina et al. “Heart rate and heart rate variability modification in chronic insomnia.” Patients Behavioral Sleep Medicine, 12 (2014): 290–306).

2. Andre Pittig et al. “Heart rate and heart rate variability in panic, social anxiety, obsessive–compulsive, and generalized anxiety disorders at baseline and in response to relaxation and hyperventilation.” International Journal of Psychophysiology 87, 1 (2013): 19-27.

3. Robert Carney et al. “Depression, heart rate variability, and acute myocardial infarction.” Circulation 104 (2001): 2024-2028.

4. Marcus Agelink et al. “Relationship between major depression and heart rate variability. Clinical consequences and implications for antidepressive treatment.” Psychiatry Research 113, 1–2 (2002): 139–49.

 

Outcome:

Prerequisites

None

Specialisations

Categories

Supervisor

Co-supervisor

Team

Lab

Lab allocations have not been finalised