SNORES - Towards a Less-intrusive Home Sleep Monitoring System using WSN
1
SNORES - Towards a Less-intrusive Home Sleep Monitoring System using WSN Motivation •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 Jun Han, Jae Yoon Chong, Sukun Kim Independent Research Initiative in SEnsor Networks (IRISEN) www.irisen.org/snores • 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. PSG includes various parameters: -EEG, EOG, EMG, ECG, Nasal/Oral Airflow, Chest/Abs Belt, Accelerometers, and Pulse Oxymetry(SP02) Architecture Overview S ensor N etworks O riented RE search in S leep (SNORES) 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. Plot C: Audio data of a subject snoring and its LPF data •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 ** 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. •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 •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” Plot D: Overall Analysis of SNORES software Current Challenges
description
** 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. - PowerPoint PPT Presentation
Transcript of SNORES - Towards a Less-intrusive Home Sleep Monitoring System using WSN
SNORES - Towards a Less-intrusive Home Sleep Monitoring System using WSN
Motivation•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
Jun Han, Jae Yoon Chong, Sukun KimIndependent Research Initiative in SEnsor Networks (IRISEN)
www.irisen.org/snores
• 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.
PSG includes various parameters:-EEG, EOG, EMG, ECG, Nasal/Oral Airflow, Chest/Abs Belt, Accelerometers, and Pulse Oxymetry(SP02)
Architecture Overview
Sensor Networks Oriented REsearch in Sleep (SNORES)
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.
Plot C: Audio data of a subject snoring and its LPF data
•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
** 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.
•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
•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”
Plot D: Overall Analysis of SNORES software
Current Challenges
