Sleep Apnea Wearables Project: AI Solution for Sleep Apnea Detection

This project explores the use of sleep tracking wearables to address the problem of sleep apnea. It begins with a problem statement highlighting the disorder's characteristics and the need for cost-effective monitoring solutions, especially in geographically separated scenarios. The project identifies target users, including heart patients, cancer patients, and individuals with diabetes or depression, who may suffer from sleep apnea. A scenario is presented, depicting a 50-year-old man experiencing symptoms and seeking a wearable solution. The system requirements are outlined, emphasizing compact battery management, multi-parameter bio-sensing, and a high-resolution display. Key tasks are described, focusing on the ability of wearable devices to detect biological signals, record pulse waves, and facilitate Home Sleep Apnea Testing (HSAT). The project references the SCOPER scheme for evaluating HSAT frameworks and the potential for clinicians to accurately estimate sleep behavior using smart devices. The conclusion emphasizes the growing importance of wearable devices in sleep apnea tracking due to the increasing prevalence of the condition. The project concludes with a bibliography of relevant research papers.

Running head: Sleep Tracking Wearables to solve the problem of Sleep Apnea
Sleep Tracking Wearables to solve the problem of Sleep Apnea
Milestone 1
Name of the Student
Name of the University
Author Note

Sleep Tracking Wearables to solve the problem of Sleep Apnea 1
Table of Contents
Problem statement:.....................................................................................................................2
Target users:...............................................................................................................................2
Scenario:.....................................................................................................................................2
System requirements:.................................................................................................................2
Conclusion:................................................................................................................................4
Bibliography:..............................................................................................................................5

2Sleep Tracking Wearables to solve the problem of Sleep Apnea
Problem statement:
According to U.S. department of Health & Human services, the Sleep Apnea is a disorder
that is characterised by the disruption in taking breath like pauses of periods of shallow
breath taking at the time of sleeping. This disruption may occur so many times as well as it
can last for as a few mins typically after snoring loudly (Lin et al., 2016). The monitoring and
diagnosis of Sleep Apnea needs cost effective wireless technologies, specially in the areas
where the patients and doctors are separated geographically.
Target users:
Sleep Apnea can cause many of the issues related to memory loss, heart disease,
cancer, clinical depression, diabetes (Liang & Ploderer, 2016). The target users are heart
patients, cancer patients and people who are suffering from diabetes and depression.
Scenario:
Mr. Bob is a 50 year old man who comes to the office with complaints of shortness of
breath. Bob has gained ten lbs from the last six months as well as he feels that the breathing
of him is more difficult at the time of walking up to the flight of stairs. Bob suffers from sleep
Apnea and the doctor suggested him to take a wearable to solution. He was a smoker and
quite smoking almost ten years ago.
System requirements:
The smart watches can be utilized in the study of home science as they are able to
detect biological signals without the requirement of external sensors. The requirement for the
system is as follows:
 Compact management of battery with ultra-low standby for longer run time.

3Sleep Tracking Wearables to solve the problem of Sleep Apnea
 Multi parameter bio sensing for monitoring the physical and health activity of the user
(Penzel, Schöbel & Fietze, 2018).
 Vivid and a high resolution OLED display with haptic feedback.
Key tasks:
The manufacturers of wearable devices will have to add more external sensors to the
devices will allow them in accessing other signals. Sometimes the smart watches can be
utilized in the study of home science as they are able to detect biological signals without the
requirement of external sensors. The internal sensors that are available in the smart watches
are having the ability to record the pulse wave of user that an algorithm can utilize for
extracting the values of heart rate and saturation of Oxygen (Weatherall et al., 2018). The
capability of testing of HSAT (Home Sleep Apnea Testing) are getting more specific and
accurate to Sleep Apnea. The SCOPER scheme can be helpful for evaluating and categorising
HSAT frameworks through validating the important most functions to diagnose the Sleep
Apnea. The clinicians are able to estimate the sleep behaviour accurately in the sleep centre
for upto 42 mins by the help of smart phones or devices.
There are several functions that are indicated in the acronym are as follows:
 Sleep (S)
 Cardiovascular system (C)
 Oxygen saturation (O)
 Body position (P)
 Respiratory effort (E)
 Airflow (R).

4Sleep Tracking Wearables to solve the problem of Sleep Apnea
Conclusion:
The wearable or wearable device is basically one of the electronic devices that is
having a micro controller and it can be directly worn on the body or even it can be
incorporated in the clothing. The devices are having internet connectivity and they are a part
of IoT (Internet of Things). The application and development of sleep Apnea tracker is
hugely due as the prevalence of sleep Apnea is increasing.

5Sleep Tracking Wearables to solve the problem of Sleep Apnea
Bibliography:
Liang, Z., & Ploderer, B. (2016, November). Sleep tracking in the real world: a qualitative
study into barriers for improving sleep. In Proceedings of the 28th Australian
Conference on Computer-Human Interaction (pp. 537-541).
Lin, Y. Y., Wu, H. T., Hsu, C. A., Huang, P. C., Huang, Y. H., & Lo, Y. L. (2016). Sleep
apnea detection based on thoracic and abdominal movement signals of wearable
piezoelectric bands. IEEE journal of biomedical and health informatics, 21(6), 1533-
1545.
Penzel, T., Schöbel, C., & Fietze, I. (2018). New technology to assess sleep apnea: wearables,
smartphones, and accessories. F1000Research, 7.
Weatherall, J., Paprocki, Y., Meyer, T. M., Kudel, I., & Witt, E. A. (2018). Sleep tracking
and exercise in patients with type 2 diabetes mellitus (step-D): pilot study to
determine correlations between Fitbit data and patient-reported outcomes. JMIR
mHealth and uHealth, 6(6), e131.