Author
John Gregory Hughes
Bio: John Gregory Hughes is an academic researcher from Wright State University. The author has contributed to research in topics: Sleep in non-human animals & Sleep disorder. The author has an hindex of 1, co-authored 3 publications receiving 3 citations.
Papers
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01 Jul 2020
TL;DR: Three methodologies based on electronic health records and heart rate variability (HRV) are proposed and results reveal that a deep neural network methodology can achieve an accuracy of 0.6 in predicting light, medium, and deep SQ using only ECG signals recorded during PSG.
Abstract: Sleep quality (SQ) is one of the most well-known factors in daily work performance. Sleep is usually analyzed using polysomnography (PSG) by attaching electrodes to the bodies of participants, which is likely sleep destructive. As a result, investigating SQ using a more easy-to-use and cost-effective methodology is currently a hot topic. To avoid overfitting concerns, one likely methodology for predicting SQ can be achieved by reducing the number of utilized signals. In this paper, we propose three methodologies based on electronic health records and heart rate variability (HRV). To evaluate the performance of the proposed methods, several experiments have been conducted using the Osteoporotic Fractures in Men (MrOS) sleep dataset. The experimental results reveal that a deep neural network methodology can achieve an accuracy of 0.6 in predicting light, medium, and deep SQ using only ECG signals recorded during PSG. This outcome demonstrates the capability of using HRV features, which are effortlessly measurable by easy-to-use and cost-effective wearable devices, in predicting SQ.
6 citations
TL;DR: The COVID-19 pandemic has increased isolation of caregivers making the development of an in-home assessment tool timely as discussed by the authors, which examined the usability of the Caregiver Assessment using Serious...
Abstract: The COVID-19 pandemic has increased isolation of caregivers making the development of an in-home assessment tool timely. This study examined the usability of the Caregiver Assessment using Serious ...
1 citations
23 Jul 2020
TL;DR: A daily use caregiverSleep survey (DUCSS) was developed to evaluate caregiver sleep and showed promising results in relation to depression, stress, and sleep disturbance in dementia caregiving.
Abstract: . Dementia caregiving is associated with depression, stress, and sleep disturbance. A daily use caregiver sleep survey (DUCSS) was developed to evaluate caregiver sleep. The tool was distri...
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21 citations
27 Aug 2021
TL;DR: In this paper, a RNN based framework is proposed which predicts posture and compare it with recommended posture of a person, if the person is sleeping in wrong posture, it generates an alert to correct the posture.
Abstract: Sleep quality is one of the most important features to predict a healthy living style. It relates to various physical and physiological diseases. Correct or most comfortable posture is responsible for having and predicting the quality of sleep anyone had. However, posture in which a person is sleeping is his/her personal requirement or desire. Correct posture is context sensitive information which needs to be consider when predicting sleep quality of any person. In this paper a RNN based framework is proposed which predicts posture and compare it with recommended posture of a person. If the person is sleeping in wrong posture, it generates an alert to correct the posture. The proposed framework is tested on standard dataset of sleep and pressure mat available at physionet.org. The proposed framework has an accuracy of above 90% in validation dataset.
1 citations
TL;DR: In this article , the authors developed a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model, which initially enables the wearables to gather sleep-activity-related data.
Abstract: Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the class of wearables worn on the wrist to compute the sleep parameters by storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have been employed for predicting sleep quality depending upon the wearables data from the period of being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next, data pre-processing is performed to transform the data into a uniform format. For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQP-ODL method are examined under different measures. An extensive comparison study shows the significant performance of the WSHMSQP-ODL model over other models.
1 citations
06 Jul 2022
TL;DR: A research and development of a low-cost system that performs user sleep monitoring, aiming to improve sleep quality, and concludes that the system has satisfactory performance for monitoring user sleep.
Abstract: In this paper, we present a research and development of a low-cost system that performs user sleep monitoring, aiming to improve sleep quality. The monitored parameters are: heart rate, luminosity, humidity, sound and temperature. The system consists of sensors, wireless devices and an Android application. The user can access the data using the Android application or the web. In terms of results, we conclude that the system has satisfactory performance for monitoring user sleep.
TL;DR: In this article, the authors proposed a data-aware program-wide diffing method across architectures and optimization levels to detect vulnerabilities in IoT devices whose source codes are not public, considering the architectures of IoT devices may vary, they rely on the defined anchor functions and call relationship to expand the comparison scope within the target file.
Abstract: Code reuse brings vulnerabilities in third-party library to many Internet of Things (IoT) devices, opening them to attacks such as distributed denial of service. Program-wide binary diffing technology can help detect these vulnerabilities in IoT devices whose source codes are not public. Considering the architectures of IoT devices may vary, we propose a data-aware program-wide diffing method across architectures and optimization levels. We rely on the defined anchor functions and call relationship to expand the comparison scope within the target file, reducing the impact of different architectures on the diffing result. To make the diffing result more accurate, we extract the semantic features that can represent the code by data flow dependence analysis. Earth mover distance is used to calculate the similarity of functions in two files based on semantic features. We implemented a proof-of-concept DAPDiff and compared it with baseline BinDiff, TurboDiff and Asm2vec. Experiments showed the availability and effectiveness of our method across optimization levels and architectures. DAPDiff outperformed BinDiff in recall and precision by 41.4% and 9.2% on average when making diffing between standard third-party library and the real-world firmware files. This proves that DAPDiff can be applicable for the vulnerability detection in IoT devices.