SNORES - Towards a Less-intrusive Home Sleep Monitoring System using WSN

1. ** We would like to thank doctors at Comprehensive Sleep Center of Samsung Medical Center for their active support in aiding us with vital feedback and ideas. SNORES - Towards a Less-intrusive Home Sleep Monitoring System using WSN Sensor Networks Oriented REsearch in Sleep (SNORES) Jun Han, Jae Yoon Chong, Sukun Kim Independent Research Initiative in SEnsor Networks (IRISEN) www.irisen.org/snores Motivation • Audio sensor (FK648 condenser mic) – Snore Detection • Used denoised LPF to remove high frequency noise • Threshold and ceil data to detect for snoring and filter out “sleep-talking” • Large portion of population suffers from sleep disorder. • Not many people are aware of their sleep status. • Limitations of current hospital medical practices using Polysomnography (PSG) • Patient’s Limitations • Realistically difficult to visit hospitals for a PSG testing unless seriously ill due to price • Need to sleep in a new environment (hospital labs) • Hospital Limitations • Intrusive wiring: patients accidentally jerk wires away • Short testing period (1 night at max) – less accurate Plot C: Audio data of a subject snoring and its LPF data • PSG includes various parameters: • EEG, EOG, EMG, ECG, Nasal/Oral Airflow, Chest/Abs Belt, Accelerometers, and Pulse Oxymetry(SP02) Architecture Overview Plot D: Overall Analysis of SNORES software • Accelerometer (ADXL 330) • Non-intrusive: No physical contact to the subject • Limits the accuracy of results but preferred by the users • Placed on bed (near face and legs / below the pillow) to check for frequent movements and hypnic jerk of legs • Less-intrusive: Wears a belt on waist and on a leg to check for body movement during sleep + hypnic jerks of legs • More intrusive but more accurate • Belt on waist can further be investigated to be used to check for respiration purposes to detect for sleep apnea • Sensed data are forwarded to the gateway • Gateway forwards the data to a database server either locally or in a medical center. Stored data can be analyzed for diagnosis. • TelosB and Kmote running TinyOS-2 is used. Preliminary Results • Type of sensors • Humidity, Accelerometer, Audio Sensor are used • Humidity sensors (Sensirion SHT15) • Affective in detecting the movement of face during sleep when placed less than half a meter away. Current Challenges • Challenges facing home sleep monitoring systems: • Respiration detection (thermal / pressure sensors on faces) • Tilt sensors to detect Thoracic / Abdomen angles for breathing • Circadian Rhythm monitoring • Using accurate wireless temperature sensors to replace the traditional periodic rectal temperature measurements • Helps the user to sleep more comfortably while being checked upon periodically. • Integration of sensors on a shirt • bioshirt with ECG, audio, accelerometer sensors attached Plot A: Humidity graph (Relative Humidity, HPF data) Plot B: Ground truth taken from a LED webcam of a subject moving