A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

Jeng, Po Yuan; Wang, Li-Chun; Hu, Chaur Jong; Wu, Dean

doi:10.3390/s21010258

Cited by 24 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ET classifier obtains the highest mean F1score when the sensor is placed on the right thigh (i.e., 97.3%) and right wrist (i.e., 78.6%) locations, while AdaLSTM achieves the highest F1-Score values for the chest (i.e., 95.3%) and the right wrist(i.e., 74.2%) among all the algorithms. The linear classifiers such as LDA [7] and SVM [22] achieve higher F1-Score (i.e ranged 90.4%-97.2%) than the proposed models when applied to data collected from the sensor on the thighs and the chest. The linear relationship between the lying posture and accelerometer readings causes the superiority of state-of-the-art for these locations.…”

Section: Integrated Datasetmentioning

confidence: 94%

“…Moreover, Jeng et. al [22] proposed a sleep position detection algorithm that achieved 90% accuracy in detecting postures supine, prone and laterals using the data collected from an accelerometer sensor on the wrist of the users. Their proposed model applied a support vector machine classifier with a linear kernel and a random forest of 100 trees on the mean value of the signal.…”

Section: Single-sensor Lying Posture Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Pervasive Lying Posture Tracking

Alinia¹,

Samadani²,

Milošević³

et al. 2020

Preprint

View full text Add to dashboard Cite

Automated lying-posture tracking is important in preventing bed-related disorders such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research about how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article, we propose a comprehensive approach to design a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are most effective in accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F-Score that ranges from 95.2% to 97.8% with 0.03 to 0.05 coefficient of variation. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

show abstract

Section: Integrated Datasetmentioning

confidence: 94%

Section: Single-sensor Lying Posture Trackingmentioning

confidence: 99%

Pervasive Lying Posture Tracking

Alinia¹,

Samadani²,

Milošević³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Random Forest Algorithm: This is a supervised learning algorithm; it is a combination of multiple Decision Trees; this ensemble algorithm works for classification and regression problems [ 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

Real-Time Tracking of Human Neck Postures and Movements

Kumar

Elias

2021

Healthcare

View full text Add to dashboard Cite

Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders.

show abstract

“…In the second level, SVM and RF algorithms are used to further classify SPC into eight and NonSPC into four motions. iSleePost [33] is another non-invasive system which uses inertial sensors attached to the wrist and chest positions of the users. The chest-attached sensor is only used in the training phase and is no longer needed after the model is trained.…”

Section: Wearable-based Approaches For Posture Monitoringmentioning

confidence: 99%

“…However, machine learning requires more resources which is a problem for wearable devices given their small sizes and battery requirements. Some solutions have tried to tackle this problem by shifting the training phase offline [33] while other have used light weight techniques to ease the burden on the wearable devices.…”

Section: Use Of Machine Learningmentioning

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

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

Cited by 24 publications

References 28 publications

Pervasive Lying Posture Tracking

Pervasive Lying Posture Tracking

Real-Time Tracking of Human Neck Postures and Movements

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

Contact Info

Product

Resources

About