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Location and Activity Tracking with the Cloud

Taj Morton, Alex Weeks, Samuel House, Patrick Chiang, and Chris ScaffidiSchool of Electrical Engineering and Computer Science

Oregon State University

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Aging in placeAging 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

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But how can we But how can we detect when home aides are needed?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)

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Prior work: High-accuracy gait Prior work: High-accuracy gait monitoring with IMU+RFIDmonitoring 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

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Advantages of IMU+RFID sensor over Advantages of IMU+RFID sensor over existing technologyexisting technology

  GPS RSSI UWB RFID IMU IMU+RFID

LIMITATION Not indoors

Too coarse

Limited range

Too coarse

Drifts with time

Accuracy > Room Room 0.1m > Room > Room 0.1m

Cost/building Low $0-100 > $1k $40 $0 $50

Size 1cm 1cm 3cm 3cm 3cm 1cm

Commercial products

Google Ekauhau Ubisense Near-Field

None None

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Challenge and approachChallenge 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

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Overall system architectureOverall system architecture

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Cloud-based servers

Software architectureSoftware 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

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Visualization currently supportedVisualization currently supported

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Low-latency (< 2 seconds) Low-latency (< 2 seconds) up to ~ 2.1k data samples per secondup 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

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Key design advantagesKey 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)

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Future directionsFuture 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

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Thank you…Thank you…

• To EMBC for this opportunity• To you for your interest

Questions?

Introduction Contribution Discussion