1 Location and Activity Tracking with the Cloud Taj Morton, Alex Weeks, Samuel House, Patrick Chiang, and Chris Scaffidi School of Electrical Engineering and Computer Science Oregon State University

2 Aging in place Do the math –Nursing home ~ $250/day per person –Assisted living communities ~ $115/day –In-home health aides ~ $20/day Objective: help people live at home as long as possible –Summon health aides when needed Introduction  Contribution  Discussion

3 But how can we detect when home aides are needed? Much work exists on monitoring health with sensors –E.g., monitoring gait to detect… Decline in cognitive ability Decline in proprioception Increase in risk of falls Decrease in general fitness level Decrease in cardiovascular health Increase in risk of depression (… as well as monitoring with other sensors) Introduction  Contribution  Discussion

4 Prior work: High-accuracy gait monitoring with IMU+RFID Attach inertial monitoring unit (IMU) to shoe IMU monitors gait using accelerometer –Obtains fiducial updates from nearby RFIDs (e.g., RFIDs placed on doorways) Excellent capabilities –Accuracy of 47 cm –Cost of $100 –Size of 4cm Introduction  Contribution  Discussion

5 Advantages of IMU+RFID sensor over existing technology GPSRSSIUWBRFIDIMUIMU +RFID LIMITATIONNot indoors Too coarse Limited range Too coarse Drifts with time Accuracy> RoomRoom0.1m> Room 0.1m Cost/buildingLow$0-100> $1k$40$0$50 Size1cm 3cm 1cm Commercial products GoogleEkauhauUbisenseNear- Field None Introduction  Contribution  Discussion

6 Challenge and approach Needed: a means for the IMU+RFID sensor to send data out of the home –Other gait sensing technologies also require a similar means of transmitting data to facilitate remote monitoring Approach: –Transmit data from sensor to a cell phone via bluetooth –Transmit data from cell phone to cloud via wireless Introduction  Contribution  Discussion

7 Overall system architecture Introduction  Contribution  Discussion

8 Cloud-based servers Software architecture Data upload client (cell phone app) Data processor (stores data) Location analysis (and gait if needed) Data storage and access objects Visualizer service Data sharing service Amazon SimpleDB Web browser Other applications Introduction  Contribution  Discussion

9 Visualization currently supported Introduction  Contribution  Discussion

10 Low-latency (< 2 seconds) up to ~ 2.1k data samples per second ~ 300 samples per second (gyro+accel, total) for high-resolution tracking Lower-resolution tracking requires lower sampling rates Introduction  Contribution  Discussion

11 Key design advantages Cell phone app as well as most of our software components on the cloud are “sensor-agnostic” –Can forward and store any data that we send to it Modular design to facilitate adding new analyses –In contrast to existing systems on the cloud that can only store data for you (so you need to compute elsewhere) Full parallelization among users –Number of users is directly proportional to the number of servers allocated (linear scalability) Introduction  Contribution  Discussion

12 Future directions The sensor –Improved accuracy (using an environment model) –Reduced power consumption and size (integrated circuit) –Integrate with other wearable sensors The cloud –Improve scalability by further optimizing algorithms –Provide additional analyses and visualizations –Integrate protection for security and privacy Applications –Test reliability with long-term field study –Use system as a tool for health monitoring studies Introduction  Contribution  Discussion

13 Thank you… To EMBC for this opportunity To you for your interest Questions? Introduction  Contribution  Discussion