Wearable accelerometer based extended sleep position recognition

Abstract

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.

Pervasive Lying Posture Tracking

“…Their algorithm first, classified the postures using the acceleration-moving variance method, into stable and non-stable time windows, then classified the features into the postures prone, supine, and laterals. Their model achieved an average accuracy of [ 26 ]. Moreover, Wrzus et al [ 10 ] developed a accurate classification model using chest and thigh accelerometry data based on the angular orientation of the upper body along the vertical axis to classify lying postures.…”

Computational Sleep Behavior Analysis: A Survey

“…These stable states are mainly related to sleep positions. Sleep postures are independent from sleep stages [14]; therefore, they provide additional insights into sleep behavior [15].…”

“…During periods without movement, four basic sleep postures can be distinguished, i.e., supine, prone, right, and left lateral. Sleep position tracking is predominately motivated by the prevention of pressure ulcers [15]- [17] or based on the influence on sleep apnea [15], [18]. For sleep apnea, sleeping on the back, i.e., supine position, relates to a higher apnea/hypoapnea index (AHI) compared to laying on the side [18].…”

“…Research mainly concentrates on monitoring the four basic sleep postures, but postures with a higher granularity, including leg positions, are also of interest [15]. Sensors in this field can be (1) applied in or on the bed [16], [17], [20]- [22], (2) wearables [19], [23] or (3) imaging devices [24].…”

“…Data are gathered from sensors such as 3-axis accelerometers, thermostats or photoplethysmography (PPG). Knowledge is generally obtained by applying data mining techniques for sleep position detection [15], [23], [46], sleep stage classification [4], [9], [11], [47], heart rate [29] and respiration rate [46] analysis, and body temperature monitoring [29], [48].…”