Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

Abstract

Wearable devices such as smartwatches, wristbands, GPS shoes are increasingly used for fitness and wellness as they allow users to monitor their daily health. These devices have sensors for accumulating user activity data. Clinical actigraph devices fall in the category of wearable devices worn on the wrist determined to estimate sleep parameters by recording movements during sleep. This study aims to predict sleep quality from wearable sensors using deep learning techniques. Three sleep indicators are proposed which are calculated using the data collected automatically from wearable devices. These sleep indicators are Daily Sleep Quality, Weekly Sleep Quality, and Sleep Consistency. Two deep learning models namely Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) have been implemented to predict sleep quality on the basis of the proposed indicators. Two datasets have been used to validate the work proposed in this study which include a dataset comprising sleep parameters using commercial wearable devices and another dataset consisting of sleep data using clinical actigraph device. Systematic Minority Oversampling Technique has been applied for data augmentation with the intent to increase data instances and to alleviate class imbalance. CNN is observed to outperform MLP in predicting sleep quality with the highest accuracy of 97.30%. This study also evaluates the worth of each sleep attribute using Information Gain algorithm in order to identify the most important attributes which contribute to the sleep quality. It has been concluded that in bed awake percentage contributes maximum to the Daily Sleep Quality, average sleep efficiency contributes maximum to the Weekly Sleep Quality and standard deviation of midpoint of in bed and out of bed times contributes maximum to the Sleep Consistency.

Depress-DCNF: A deep convolutional neuro-fuzzy model for detection of depression episodes using IoMT

“…The prevalence & high risk associated with depression in individuals, early screening efforts and preventive interventions have been studied widely in literature. Recent studies have leveraged the data from wearable devices and soft computing techniques to assess various psychological disorders such as sleep quality [11,16], depression [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], behavioral disorders [16], anxiety [17,19], and mental wellbeing [20,21]. Automated detection of depression and its severity assessment using activity monitoring and other multimodal cues is being researched extensively.…”

“…The curated flow of information delivered using the wearable technology in a digestible, easy-to-understand format, can deliver interventions for depression [9,10]. Actigraph sensor is one such widely used wearable sensor that is used to evaluate the physical activity and sleep of an individual [11]. It is a non-invasive method of monitoring the human rest/activity cycles.…”

Evaluation of Physical Education Teaching Quality Based on the Random Multivariate Matrix Convolution Neural Network Model

“…e enchantment of the detection accuracy of bodily coaching relies upon on the depth of the neural network. ere is a high-quality correlation between the elements and the illustration ability, the convolution neural community will calculate the facets of all incorrect motion data [19,20]. e deeper the remaining output, the more improved the characteristic extraction ability [21].…”

Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities

“…Table 2 lists five works which used wearable sensors to collect physiological signal from human for sleeping quality prediction purpose. Compared with questionnaires-type data set, the number of participants of wearable-sensor-data set is quite small [16,16,17,22,25]. Reference [16] reported the feasibility of prediction poor/good sleep from daily activities of adolescents in Qatar.…”