Wearable Technology: the wave of the future

Transcription

1 Wearable Technology: the wave of the future Omid Dehzangi Computer and Information Science University of Michigan - Dearborn Wearable Sensing and Signal Processing Lab

2 Outline Introduction to wearable technology Vision and mission Application and high level model design Wearable platform design and development My research contributions Brain-computer interface Activity of daily living (ADL) monitoring My current research plans 2

3 Technology Trends Wearable Analog Personal Today s computer Computers Transistors Smaller Smarter Digital Slimmer Hands Processing free Brought Faster Natural to Interface homes Hand held 3

4 Wearable Technology ABI Research has projected that by 2016, wearable wireless device sales will reach more than 100 million devices annually. The market for wearable sports, fitness, and healthcare monitoring devices cover 80% of it. The market for wearable technologies in healthcare "is projected to exceed $2.9 billion in 2016 (at least half of all wearable technology) Photo courtesy of 4

5 Wearable Computers Flex, Pressure and Piezo-electric Sensors Dry-contact EEG Inertial Sensors Sensors Galvanic Skin Response Feedback GUI-based feedback Vibrotactile Modules Haptics Phantom Photo Courtesy of SenseGraphics Deep Brain Stimulation Photo Courtesy of mindmodulation.com 5

6 Wearable Computers Sensors Processing Unit Signal Processing Communication Information Fusion Prediction/Detection Feedback Data Analytics Big data analysis Data mining Machine learning Predictive modeling Statistical analysis 6

7 Outline Introduction to wearable technology Vision and mission Application and high level model design Wearable platform design and development 7

8 Research Vision Applications Model design Wearable platform Algorithms and analytics System integration Technologies Demonstrate the linkage between discovery and societal benefit Validate real pains and necessities and identify effective high level solutions Design and develop in multiple technical levels Resolve upcoming challenges in practice Generate transitioning technologies 9

9 Wireless Health Ubiquitous monitoring and intervention for the applications of health-care and wellness Courtesy of Misha Pavel, Program Director, National Science Foundation 9

10 Outline Introduction to wearable technology Vision and mission Application and high level model design Wearable platform design and development My current research contributions Brain-computer interface Activity monitoring and motion detection 10

11 Application Case Study WEARABLE BRAIN COMPUTER INTERFACE Applications 11

12 Brain Computer Interface Brain Computer Interface Provide a non-muscular avenue for the user to communicate with others and to control external devices Infer user s intentions using brain activities Applications Assist locked-in individuals to interact with cyber and physical system Gaming Diagnosis and treatment for neurological disorder 12

13 Wearable EEG Systems Smaller form factor (size of a credit card vs. bulky amps) Quicker setup time (seconds vs. 30 mins) Faster software training (5 mins vs. 30 mins) Quicker EEG signal detection (seconds vs. minutes) No need for EEG tech 13

14 Wearable EEG-Based BCI Custom-designed mobile EEG-based BCI Dry-contact electrodes Low-noise front-end (ADS1299) Low power processing (MSP430) Low component count Bluetooth low energy (TI BLE) communication module 14

15 Wearable BCI Units 15

16 Canonical Correlation Analysis(CCA) Video Picture taken from ref

17 ACTIVITY OF DAILY LIVING MONITORING Application Case Study USING GAIT AND SWAY BIOFEEDBACK TO REDUCE FALLS IN THE ELDERLY Dehzangi, Omid, Biggan, John, Birjandtalab Golkhatmi, Javad, Ray, Christopher, Jafari, Roozbeh, An Inertial Sensor-Based Method for Early Detection and Prevention of Excessive Sway in Older Adults via Gait Analysis and Vibrotactile Biofeedback, Gait & Posture journal. Dehzangi, Omid, Zhao, Zheng, Biggan, John, Ray, Christofer, Jafari, Roozbeh, The Impact of Vibrotactile Biofeedback on the Excessive Walking Sway and the Postural Control in Elderly, Wireless Health 2013, November 1-3, Baltimore, Maryland, Applications 17

18 Postural control and gait analysis 18

19 Sway Biofeedback for Fall Prevention Fall is a considerable health concern in the elderly Wearable kinematic biofeedback system to detect pre-cursors of falls based on the sway of the upper body and other gait parameters, and activate biofeedback 19

20 Hardware Architecture BLE Microprocessor I 2 C Motion Sensor: Gyro & Accelerometer Laptop UART BLE transceiver 20

21 Software Architecture Accelerometer X,Y,Z X,Y,Z Accelerometer Gyroscope X,Y,Z X,Y,Z Self-adaptive Angle Threshold Setting Sensor Calibration Calibrated Accelerometer X,Y,Z Calibrated Gyroscope X,Y,Z Drift Detection Euler Angles (roll, pitch) DCM Feedback loop PI controller 21

22 The developed system (a) (b) (a) Our designed wearable low-power motion sensor board, (b) Our biofeedback system, consisting of two motion sensor boards for the chest and the ankle along with the vibratory feedback modules. 22

23 Experiments Subjects: 24 older adults (age: M = 75.5, SD = 4.32 years; 10 females) Procedure: 23

24 Results and Analysis Mean difference in the sway range. The results of the statistical test on the sway range The test Control Experimental P-value Difference in the sway range 0.59± ±

25 Gait phase analysis Initial sway Mid sway Terminal sway Mid stance The selected phases of a gait cycle. Identification of the gait phases on the ACC X readings 25

26 ACC X reading Gait phase analysis 1.52 x Sample DTW extracted strides based on ACC X readings (Experimental) (Control) 26

27 Gait phase analysis Initial sway Mid sway Terminal sway Mid stance Mean difference in the variance of the gait phases between pre- and post- training. The results of the Chi-square test on the gait phases The gait phases Control Experimental P-value Initial sway 0.17± ± Mid sway -1.54± ± Terminal sway -1.29± ± Mid stance -0.18± ±

28 Outline Introduction to wearable technology Vision and mission Application and high level model design Wearable platform design and development My current research contributions Brain-computer interface Activity of daily living (ADL) monitoring My Current Research Plans 28

29 Application Case Study WEARABLE DRIVER MONITORING Applications 29

30 Goal To form relationships between biological state of the driver with his/her driving behavior 37

31 Multi-Modal Driver Monitoring and Modeling via Heterogeneous Wearable Body Sensor Network Motivation: Body sensor networks are capable of generating a reliable human state model System photograph: a) b) c) Integration of heterogeneous wearable monitoring technology, on-board sensing units, and wireless networking capabilities : a) The full body sensor network, b) the portable EEG system, c) the OBD-II device 31

32 Multi-Modal Driver Monitoring and Modeling via Heterogeneous Wearable Body Sensor Network Hypotheses: Hypothesis 1: Specific driver mental and physical states can generate abnormal driving behaviors and a high level of driving impairment. Hypothesis 2: Driver biological states will have an impact on his/her biometric measures while driving. Biometric markers that correspond to changes in performance of the impaired driver subjects will aid in explaining the underlying impact on driving outcome. Hypothesis 3: There are signature patterns in the biometric readings from the normal behavior of the driver that can be non-invasively extracted and employed for control, identification & authentication, and interaction with other smart infrastructures. The Proposed Platform: 1. Minimally intrusive: Driver behavior is not affected by the devices that are used to acquire the necessary biomedical markers 2. Comprehensive: the system will extract the data collected from a large number of heterogeneous sensors and correlate the various readings for earlier detection 3. Ubiquitous and remotely available: The collected measurements will be transmitted to a remote location for longitudinal analysis and discover association in a long term 4. Real -time responsive: The information will be accessible in an online fashion to enable real- time processing and decision making 5. User friendly: Suitable user interface and visualization tools will be in place for a human user to be able to interpret the acquired information 39

33 BlueTooth Mobile Network Multi-Modal Driver Monitoring and Modeling via Heterogeneous Wearable Body Sensor Network Driver Sensors In-Vehicle Mobile Device Backend Processing EEG ECG GSR GPS DAQ-FrontEnd Data Matrix Data Mining DAQ-BackEnd Data Base Big Data Analysis Data Visualization OBD-II Real-time Processing Long-term analysis Traffic User Interface Bio-feedback 40

34 Platform User Interface 34

35 Characterizing Driver Distraction Characterizing Driver Distraction: Two rounds of driving: 1. Non-peak traffic period, 2. Peak traffic period Objective: Investigate the effect of the road condition on the driver distraction Hypothesis: Theta and beta power increase in the EEG spectrum is related to distraction effects 30 45

36 Characterizing Driver Distraction Subjects Round 1 Round 2 theta beta theta beta Sbj Sbj Sbj Avg Comparison of total theta and beta power (db 10 3 ) in the subjects averaged EEG in frontal component between round 1 and round

37 y y y y Characterizing Driver Distraction 0.15 pdf(obj,[x,y]) 0.15 pdf(obj,[x,y]) pdf(obj,[x,y]) x pdf(obj,[x,y]) x x x the effect of the driving condition on the driver distraction 48

38 Ideas to Pursue Towards proactive driver monitoring and safety platform as advancement in automated passenger vehicle infrastructure. Connect the vehicle occupants to the loop via development of D2V & D2I in automated vehicles to improve occupant safety, performance, health & wellness. The connected vehicle infrastructure will associate the driver with the smart city and/or smart home infrastructure to optimize his/her daily operations. Driver identification and authentication is an important outcome which will be performed non-invasively via extracting the signature biometrics from the normal behavior of the driver

39 Thanks for your attention 39