Robust Automated Human Activity Recognition and Its Application to Sleep Research

Sathyanarayana, Aarti; Ofli, Ferda; Fernández-Luque, Luis; Srivastava, Jaideep; Elmagarmid, Ahmed K.; Arora, Teresa; Taheri, Shahrad

doi:10.1109/icdmw.2016.0077

Cited by 17 publications

(11 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other application areas, deep learning has been used for human activity recognition [47,48] which is a similar technical problem. In a previous study, we combined human recognition of actigraphy data with other machine learning algorithms, but not deep learning [49]. …”

Section: Discussionmentioning

confidence: 99%

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sathyanarayana¹,

Joty²,

Fernández-Luque³

et al. 2016

JMIR Mhealth Uhealth

Self Cite

170

124

View full text Add to dashboard Cite

BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science.ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality.MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM).ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463).ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

show abstract

Section: Discussionmentioning

confidence: 99%

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sathyanarayana¹,

Joty²,

Fernández-Luque³

et al. 2016

JMIR Mhealth Uhealth

Self Cite

170

124

View full text Add to dashboard Cite

show abstract

“…Our results show that using a convolutional neural network on the raw wearables output improves the predictive value of sleep quality from physical activity, by an additional 8% compared to state-of-the-art non-deep learning approaches [1], which itself shows a 15% improvement over current practice [2]. Moreover, utilizing deep learning on raw data eliminates the need for data pre-processing and simplifies the overall workflow to analyze actigraphy data for sleep and physical activity research.…”

Section: Impact Of Physical Activity On Sleep: a Deep Learning Based ...mentioning

confidence: 90%

“…The process involves sleep experts manually configuring parameters prior to performing analysis. In our previous study [1] we automated this process by developing a robust automated human activity recognition algorithm (called RAHAR). RAHAR automatically pre-processes accelerometer data into meaningful activity levels.…”

Section: B Research Challenges Of Actigraphymentioning

confidence: 99%

Impact of Physical Activity on Sleep:A Deep Learning Based Exploration

Sathyanarayana,

Joty,

Fernandez-Luque

et al. 2016

Preprint

Self Cite

View full text Add to dashboard Cite

“…Recently, the authors of Kuo et al (2017) proposed a wearable actigraphy device with a low sampling rate for in-home sleep assessment. Several other works (Matsui et al 2017;Purta et al 2016;Sathyanarayana et al 2016Sathyanarayana et al , 2017Sun et al 2017) also ascertain the strength and simplicity of wearable devices in sleep monitoring. The authors of Rofouei et al (2011) developed a wearable neck cuff system for monitoring physiological signals in real time.…”

Section: Related Workmentioning

confidence: 94%

An Active Sleep Monitoring Framework Using Wearables

Hossain

Ramamurthy

Khan

et al. 2018

ACM Trans. Interact. Intell. Syst.

View full text Add to dashboard Cite

Sleep is the most important aspect of healthy and active living. The right amount of sleep at the right time helps an individual to protect his or her physical, mental, and cognitive health and maintain his or her quality of life. The most durative of the Activities of Daily Living (ADL), sleep has a major synergic influence on a person's fuctional, behavioral, and cognitive health. A deep understanding of sleep behavior and its relationship with its physiological signals, and contexts (such as eye or body movements), is necessary to design and develop a robust intelligent sleep monitoring system. In this article, we propose an intelligent algorithm to detect the microscopic states of sleep that fundamentally constitute the components of good and bad sleeping behaviors and thus help shape the formative assessment of sleep quality. Our initial analysis includes the investigation of several classification techniques to identify and correlate the relationship of microscopic sleep states with overall sleep behavior. Subsequently, we also propose an online algorithm based on change point detection to process and classify the microscopic sleep states. We also develop a lightweight version of the proposed algorithm for real-time sleep monitoring, recognition, and assessment at scale. For a larger deployment of our proposed model across a community of individuals, we propose an active-learning-based methodology to reduce the effort of ground-truth data collection and labeling. Finally, we evaluate the performance of our proposed algorithms on real data traces and demonstrate the efficacy of our models for detecting and assessing the fine-grained sleep states beyond an individual.

show abstract

Robust Automated Human Activity Recognition and Its Application to Sleep Research

Cited by 17 publications

References 58 publications

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sleep Quality Prediction From Wearable Data Using Deep Learning

Impact of Physical Activity on Sleep:A Deep Learning Based Exploration

An Active Sleep Monitoring Framework Using Wearables

Contact Info

Product

Resources

About