Sleep is a key marker of health, as it can either be a cause or a consequence. It is traditionally studied in clinical environments using dedicated medical devices. Recent technological developments, e.g., in sensing and data analysis, have led to new approaches for sleep monitoring and assessment, which are attracting increasing attention in the emerging domain of personalized smart healthcare. Nevertheless, a highlevel overview of technology-enabled research on sleep that can inform related communities of the latest developments is lacking. In this paper, we present a comprehensive review to examine the current status of various aspects of technology-based sleep research. We first characterize sleep behavior and key areas of sleep assessment, and we introduce a general review of the methodologies used in this domain. We review the major technological methods and trends associated with sleep monitoring, data collection and sleep behavior analysis, from which strengths and weaknesses are highlighted. Finally, we also discuss challenges and promising directions for future research.

Abstract-Sleep positions have an impact on sleep quality and therefore need to be further analyzed. Current research on position tracking includes only the four basic positions. In the context of wearable devices, energy efficiency is still an open issue. This research presents a way to detect eight positions with higher granularity under energy efficient constraints. Generalized Matrix Learning Vector Quantization is used, as it is a fast and appropriate method for environments with limited computation resources, and has not been seen for this kind of application before. The overall model trained on individuals performs with an averaged accuracy of 99.8%, in contrast to an averaged accuracy of 83.62% for grouped datasets. Real world application gives an accuracy of around 98%. The results show that energy efficiency will be feasible, as performance stays similar for lower sampling rate. This is a step towards a mobile solution which gives more insight in person's sleep behaviour.

Many chronic diseases show evidence of correlations with sleep-wake behaviour, and there is an increasing interest in making use of such correlations for early warning systems. This research presents an approach towards early chronic disease detection by mining sleep-wake measurements using deep learning. Specifically, a Long-Short-Term-Memory network is applied on actigraph data enriched with clinical history of patients. Experiments and analysis are performed targeting detection at an early and advanced disease stage based on different clinical data features. The results show for disease detection an averaged accuracy of 0.62, 0.73, 0.81, 0.77 for hypertension, diabetes, sleep apnea and chronic kidney disease, respectively. Early detection performs with an averaged accuracy of 0.49 for sleep apnea and 0.56 for diabetes. Nevertheless, compared to existing work, our approach shows an improvement in performance and demonstrates that predicting chronic diseases from sleep-wake behavior is feasible, though further investigation will be needed for early prediction.

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The work concentrates on combining discrete and continuous data in an algorithm to detect complex activity patterns.With the InvenSense MotionFit TM Software Development Kit (SDK) accelerometer and gyrometer data are recorded with the MPU-9150 sensor.[1] The raw data consisiting of processed daily acticities are preprocessed via a shifted window and different features are calculated. Afterwards activity recognition is done in MATLAB using the PMTK3 toolbox from Murphy et al. [2], where the classification algorithms are continuous Hidden Markov Models (cHMM).