Towards the Design of a Smartphone-Based Biofeedback Breathing Training: Indentifying Diaphragmatic Breathing Patterns From a Smartphones\u27 Microphone

Asthma, diabetes, hypertension, or major depression are non-communicable diseases (NCDs) and impose a major burden on global health. Stress is linked to both the causes and consequences of NCDs and it has been shown that biofeedback-based breathing trainings (BBTs) are effective in coping with stress. Here, diaphragmatic breathing, i.e. deep abdominal breathing, belongs to the most distinguished breathing techniques. However, high costs and low scalability of state-of-the-art BBTs that require expensive medical hardware and health professionals, represent a significant barrier for their widespread adoption. Health information technology has the potential to address this important practical problem. Particularly, it has been shown that a smartphone microphone has the ability to record audio signals from exhalation in a quality that can be compared to professional respiratory devices. As this finding is highly relevant for low-cost and scalable smartphone-based BBTs (SBBT) and – to the best of our knowledge - because it has not been investigated so far, we aim to design and evaluate the efficacy of such a SBBT. As a very first step, we apply design-science research and investigate in this research-in-progress the relationship of diaphragmatic breathing and its acoustic components by just using a smartphone’s microphone. For that purpose, we review related work and develop our hypotheses based on justificatory knowledge from physiology, physics and acoustics. We finally describe a laboratory study that is used to test our hypotheses. We conclude with a brief outlook on future work

Biofeedback and Anxiety Reduction: An Occupational Therapy Intervention for Persons with Long Covid

Background: People who acquire Covid 19 may have symptoms lasting over three months, called Long Covid. It is estimated that one in five people in the United States has Long Covid. Unpleasant symptoms of Long Covid are many including anxiety. People with Long Covid have a difficult time engaging in everyday activities and have a poor quality of life. Occupational therapy practitioners using heart rate variability (HRV) biofeedback may help decrease anxiety in the Long Covid population. There is limited research on occupational therapy and HRV biofeedback. Purpose: The problem the study addressed was to investigate the use of HRV biofeedback therapy delivered by an occupational therapy practitioner and its effectiveness to decrease anxiety levels in people with Long Covid. The research aimed to discover if HRV biofeedback decreased anxiety and increased the quality of life in persons with Long Covid. Methods: This study was quasi-experimental in the form of a pre-test-post-test design. Each participant was administered two pre-tests/posttests, the Generalized Anxiety Disorder (GAD) and the Quality of Life Scale (QOLS). Each participant received eight biofeedback sessions. Results: The results of this study found that HRV biofeedback demonstrated potential in decreasing anxiety and improving quality of life in persons with Long Covid. This study yielded a low sample size; therefore, more data needs to be collected in order to determine if the results are statistically significant. A minimal clinical difference of a 4-point change is considered clinically meaningful. Ten out of eleven participants in this study reported a decrease in anxiety based on the post-test outcome measures of the GAD-7 which is considered clinically meaningful. An increase of 8-9 points in the QOLS is considered a 60% improvement in quality of life. Nine out of eleven participants reported an improvement in their quality of life and as a group mean score, which was found to be clinically meaningful. Conclusions: The results of this pilot study appear promising for the use of HRV biofeedback to reduce anxiety and improve QOLS in persons with Long Covid. The study will continue to collect data until there are sufficient participants to perform paired-sample t-tests to determine the statistical significance of pre-test/post-test scores for both the GAD-7 and the QOLS

Effects of a 4-Week Heart Rate Variability Biofeedback Intervention on Psychological and Performance Variables in Student-Athletes: A Pilot Study

PURPOSE: To examine the effects of a 4-week biofeedback intervention on coherence, psychological, and performance variables in collegiate student-athletes. METHODS: Thirteen student-athletes were randomly assigned to the intervention (one weekly biofeedback session for 4-weeks) or control group (no sessions). Data were collected at pre and post-intervention using weekly averaged coherence scores, psychological measures for depression, arousal, stress, resiliency, and performance outcome measures. RESULTS: A 3 (Time) x 4 (Week average) repeated measures ANOVA was independently conducted to examine differences between time and weekly coherence average for coherence scores. No significant differences were found for “at rest”, pre, or post-practice coherence scores. A 2 (treatment group) x 4 (Week) repeated measures ANOVAs were independently conducted to examine differences between treatment groups and week average for performance, resilience, and recovery. Significant differences were found for performance by time (p = .029). For the psychological variables, 2 (treatment group) X 2 (Time) repeated measures ANOVAs were independently conducted to examine differences between treatment group and time for CESD, AD-ACL, CSSS, and the ASSQ sleep score and no significant differences were found. CONCLUSIONS: Overall the biofeedback intervention did not improve coherence, psychological, or performance variables between the groups. While the biofeedback intervention did not show significant changes in this pilot study, there is potential for future research to address male participants and a change in timing during the season

Generic processing of real-time physiological data in the cloud

There is an emerging market in the collection of physiological data for analysis and presentation to end-users via web technologies for applications including health and fitness, telemedicine and self-tracking. As technology has improved, real-time streaming of physiological data, providing end-to-end user feedback has become feasible, allowing for innovative applications to be developed. Currently, there is no standardised method of collecting physiological data over the web for analysis and feedback to an end-user in real-time; existing platforms only support specific devices and application domains. This paper proposes a generic methodology and architecture for the collection, analysis and presentation of physiological data. It defines a standard method of encapsulating data from heterogeneous sensors, performing transformations on it and analysing it. The approach is evaluated through an implementation of the architecture using cloud computing technologies and an appropriate case study

Artificial neural network (ANN) enabled internet of things (IoT) architecture for music therapy

Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate medical response. In this article, we propose a novel ANN enabled IoT architecture to integrate music therapy with BAN and ANN for providing immediate assistance to patients by automating the process of music therapy. The proposed architecture comprises of monitoring the body parameters of patients using BAN, categorizing the disease using ANN and playing music of the most appropriate type over the patient’s handheld device, when required. In addition, the ANN will also exploit Music Analytics such as the type and duration of music played and its impact over patient’s body parameters to iteratively improve the process of automated music therapy. We detail development of a prototype Android app which builds a playlist and plays music according to the emotional state of the user, in real time. Data for pulse rate, blood pressure and breath rate has been generated using Node-Red, and ANN has been created using Google Colaboratory (Colab). MQTT broker has been used to send generated data to Android device. The ANN uses binary and categorical cross-entropy loss functions, Adam optimiser and ReLU activation function to predict the mood of patient and suggest the most appropriate type of music

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

Treat me well : affective and physiological feedback for wheelchair users

This work reports a electrocardiograph and skin conductivity hardware architecture, based on E-textile electrodes, attached to a wheelchair for affective and physiological computing. Appropriate conditioning circuits and a microcontroller platform that performs acquisition, primary processing, and communication using Bluetooth were designed and implemented. To increase the accuracy and repeatability of the skin conductivity measuring channel, force measurement sensors were attached to the system certifying measuring contact force on the electrode level. Advanced processing including Rwave peak detector, adaptive filtering and autonomic nervous system analysis based on wavelets transform was designed and implemented on a server. A central design of affective recognition and biofeedback system is described.Fundação para a Ciência e a Tecnologia (FCT