A Wearable Sleep Position Tracking System Based on Dynamic State Transition Framework

Jeon, Seung Ho; Park, Taejoon; Paul, Anand; Lee, Yang-Soo; Son, Sang H.

doi:10.1109/access.2019.2942608

Cited by 25 publications

(11 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given a quad-posture dataset, the comparative analysis identified the chest and either thighs as optimal body locations, and revealed comparable performance between handcrafted feature-based classifier and deep learning models. A different approach to sleep quad-posture classification was proposed in [30], where a probabilistic state transition from one posture to another is conditioned on the inertial profile of the pose change motion. The authors defined the transitioning motion profile through the extraction of 66 different features in time and frequency domains from raw data channels sourced from triple sensors attached to the chest and wrists.…”

Section: Related Workmentioning

confidence: 99%

Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies

Elnaggar¹,

Coenen²,

Hopkinson³

et al. 2022

Preprint

View full text Add to dashboard Cite

Sleep posture is linked to several health conditions such as nocturnal cramps and more serious musculoskeletal issues. However, in-clinic sleep assessments are often limited to vital signs (e.g. brain waves). Wearable sensors with embedded inertial measurement units have been used for sleep posture classification; nonetheless, previous works consider only few (commonly four) postures, which are inadequate for advanced clinical assessments. Moreover, posture learning algorithms typically require longitudinal data collection to function reliably, and often operate on raw inertial sensor readings unfamiliar to clinicians. This paper proposes a new framework for sleep posture classification based on a minimal set of joint angle measurements. The proposed framework is validated on a rich set of twelve postures in two experimental pipelines: computer animation to obtain synthetic postural data, and human participant pilot study using custom-made miniature wearable sensors. Through fusing raw geo-inertial sensor measurements to compute a filtered estimate of relative segment orientations across the wrist and ankle joints, the body posture can be characterised in a way comprehensible to medical experts. The proposed sleep posture learning framework offers plug-and-play posture classification by capitalising on a novel kinematic data augmentation method that requires only one training example per posture. Additionally, a new metric together with data visualisations are employed to extract meaningful insights from the postures dataset, demonstrate the added value of the data augmentation method, and explain the classification performance. The proposed framework attained promising overall accuracy as high as 100% on synthetic data and 92.7% on real data, on par with state of the art data-hungry algorithms available in the literature.

show abstract

Section: Related Workmentioning

confidence: 99%

Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies

Elnaggar¹,

Coenen²,

Hopkinson³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…), and illumination condition. The work conducted by Jeon et al [34] uses wearable devices attached to both wrists and the chest. The wearable device consists of IMU sensors.…”

Section: Wearable-based Approaches For Posture Monitoringmentioning

confidence: 99%

A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep

Hussain,

Sheng,

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep which is having negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and have now become an important tool for understanding sleep behavior. The gold standard method for sleep analysis is polysomnography (PSG) conducted in a clinical environment but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods which are cheap and easy to use for in-home sleep monitoring. In this paper, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest works done using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on 10 key factors, to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also present some publicly available datasets for different categories of sleep monitoring. In the end, we discuss several open issues and provide future research directions in the area of sleep monitoring.

show abstract

“…This research proposes that a multi-sensor device based on the IoMT infrastructure can be implemented as a wearable device comparable to the PSG system. Most comparable PSG devices merely monitor body motion by measuring variables such as motionlessness, vibration, rotation, and translation [25,26]. These measurements are then used to provide information regarding sleep status.…”

Section: System Architecturementioning

confidence: 99%

“…There are many methods used to collect data for sleep physiological signal monitoring, including electrooculography (EOG) and electrocardiogram (ECG) methods, which need to be deployed in complex medical environments [5,25,26,28,30]. Because these methods are relatively expensive and time consuming for patients, an increasing amount of research is focused on the possibility of using motion detectors, such as triple-axis accelerometers, to replace electrical sensing technology [3,25,30].…”

Section: Motion Detectionmentioning

confidence: 99%

“…The DFRobot HR sensor was developed based on PPG techniques and has many advantages, such as a small size (28 × 24 mm), plug-and-play connectivity, and easy integration with other wearable devices [4,26]. PPG techniques are simple, and low-cost optical methods that can be used to detect blood volume changes in the micro-vascular bed of tissues.…”

Section: Heartbeat Monitormentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of a Multifunction Wearable Device to Monitor Sleep Physiological Signals

et al. 2020

View full text Add to dashboard Cite

We present a wearable device built on an Adafruit Circuit Playground Express (CPE) board and integrated with a photoplethysmographic (PPG) optical sensor for heart rate monitoring and multiple embedded sensors for medical applications—in particular, sleep physiological signal monitoring. Our device is portable and lightweight. Due to the microcontroller unit (MCU)-based architecture of the proposed device, it is scalable and flexible. Thus, with the addition of different plug-and-play sensors, it can be used in many applications in different fields. The innovation introduced in this study is that with additional sensors, we can determine whether there are intermediary variables that can be modified to improve our sleep monitoring algorithm. Additionally, although the proposed device has a relatively low cost, it achieves substantially improved performance compared to the commercially available Philips ActiWatch2 wearable device, which has been approved by the Food and Drug Administration (FDA). To assess the reliability of our device, we compared physiological sleep signals recorded simultaneously from volunteers using both our device and ActiWatch2. Motion and light detection data from our device were shown to be correlated to data simultaneously collected using the ActiWatch2, with correlation coefficients of 0.78 and 0.89, respectively. For 7 days of continuous data collection, there was only one instance of a false positive, in which our device detected a sleep interval, while the ActiWatch2 did not. The most important aspect of our research is the use of an open architecture. At the hardware level, general purpose input/output (GPIO), serial peripheral interface (SPI), integrated circuit (I2C), and universal asynchronous receiver-transmitter (UART) standards were used. At the software level, an object-oriented programming methodology was used to develop the system. Because the use of plug-and-play sensors is associated with the risk of adverse outcomes, such as system instability, this study heavily relied on object-oriented programming. Object-oriented programming improves system stability when hardware components are replaced or upgraded, allowing us to change the original system components at a low cost. Therefore, our device is easily scalable and has low commercialization costs. The proposed wearable device can facilitate the long-term tracking of physiological signals in sleep monitoring and related research. The open architecture of our device facilitates collaboration and allows other researchers to adapt our device for use in their own research, which is the main characteristic and contribution of this study.

show abstract

A Wearable Sleep Position Tracking System Based on Dynamic State Transition Framework

Cited by 25 publications

References 46 publications

Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies

Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies

A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep

Design and Implementation of a Multifunction Wearable Device to Monitor Sleep Physiological Signals

Contact Info

Product

Resources

About