Wireless Monitoring and Data Management during Exercise 

Countermeasures in SpaceflightCountermeasures in Spaceflight1Andrea Hanson, 1Elliot Lee, 1Bryan Absher, 2Tim Streeper, 2Tom Lang, 1Peter Cavanagh 

1Department of Orthopaedics and Sports Medicine, University of Washington, Seattle, WA2Department of Radiology and  Biomedical Imaging, University of California, San Francisco, CA

82nd Aerospace Medical Association Scientific MeetingAnchorage, AKMay 8‐12, 2011

Disclosure Information82nd Ann al Scientific Meeting82nd Annual Scientific Meeting

Speaker’s Name Here

I h h f ll i fi i l l i hi di lI have the following financial relationships to disclose:• Stockholder in ZIN Medical: Peter Cavanagh

I will not discuss off‐label use and/or investigational use in my presentation

IntroductionIntroduction

• Integration of 3 projectsIntegration of 3 projects– Monitoring Bone Health by Daily Load Stimulus

An Integrated Musculoskeletal Countermeasure– An Integrated Musculoskeletal Countermeasure Battery for Long‐Duration Lunar Missions 

– BioNet: A data management software architecture– BioNet: A data management software architecture

BackgroundBackground

• Crewmembers aboard the International Space Station (ISS) p ( )are not currently maintaining optimal bone health during long‐duration missions. 

A t t l t 1 5% BMD t th hi th• Astronauts lose up to 1.5% BMD at the hip per month (Lang et al., 2004).

• Proximal femoral strength is reduced by ~2.0%/month (Keyak et g y / ( y

al., 2009) .

• Of the ~2.5 hrs of exercise time assigned, only ~40 minutes l d d i ll i i iloaded, potentially osteogenic activity (Cavanagh et al., 2010).

• It is unclear if the partial gravity environments of the moon or Mars will be osteoprotective.Mars will be osteoprotective.

Research FocusResearch Focus

1.1. To characterize loading in actual or simulated To characterize loading in actual or simulated reduced gravity environments in order to assess reduced gravity environments in order to assess the potential contribution of mechanical load to the potential contribution of mechanical load to b h lth i tb h lth i tbone health maintenance.bone health maintenance.

2.2. To use wireless tools to assist in continuous bone To use wireless tools to assist in continuous bone health monitoring during long duration space health monitoring during long duration space missions.missions.

3.3. To develop effective, crewTo develop effective, crew‐‐friendly friendly countermeasures to musculoskeletal disuse countermeasures to musculoskeletal disuse atrophy.atrophy.

CONCEPTCONCEPTRequired Dose of Loading (Daily Loading Stimulus DLS) for OptimalLoading Stimulus DLS) for Optimal Bone Health

Exercise CMIVAEVA

Research FocusResearch Focus

1.1. To characterize loading in actual or To characterize loading in actual or simulated reduced gravity environments in simulated reduced gravity environments in order to assess the potential contribution order to assess the potential contribution of load to maintenance of bone health.of load to maintenance of bone health.

2.2. To use wireless tools to assist inTo use wireless tools to assist in2.2. To use wireless tools to assist in To use wireless tools to assist in continuous bone health monitoring during continuous bone health monitoring during long duration space missions.long duration space missions.long duration space missions.long duration space missions.

3.3. To develop effective, crewTo develop effective, crew‐‐friendly friendly countermeasures to musculoskeletalcountermeasures to musculoskeletalcountermeasures to musculoskeletal countermeasures to musculoskeletal disuse atrophy.disuse atrophy.

Activity Tracking Device (ATD)Activity Tracking Device (ATD)

76mm

24mm

76mm

10mmthick

Li‐PolyBattery

Z

Z+Total mass: 25 grams

Y

X

Tri-axial accelerometer (± 13g)Tri-axial rate gyro (± 2000º/sec)

Sample RateFoot: 1024Hz Waist: 256Hz

Activity Tracking Device Placement

Sensor 1 Sensor 2

Boot insoleShoe insoleAnkle mount

Mid-lower backNear hipCOMAnkle mount COM

ATD TestingATD Testing

Enhanced Zero-Gravity Locomotion Simulator (eZLS) @ NASA Glenn

Combined Countermeasure Device (CCD) @ UCSF

Combined Countermeasure Device (CCD)

• Activity List (n=16)– Biking– Hip Abduction– Hip Adductionp– Hip Flexion (L & R)– Knee Flexion (L & R)– Knee Extension (L & R)( )– Heel Raises– Squats– WalkingWalking– Lying (Front & Back)– Sitting– StandingStanding

*Bike attachment not shown

PI: Tom Lang, PhD, UCSF

Phase IPhase I

• Data collected in isolated segments for eachData collected in isolated segments for each activity (n=5). 

• Developed and trained (<1% error) an• Developed and trained (<1% error) an Artificial Neural Network (ANN) with 23 inputs derived from each activity segmentderived from each activity segment. 

Artificial Neural NetworkArtificial Neural Network•Single layer, feed-forward backpropagation algorithm (MATLAB)•Hidden nodes=20•Log-sigmoid transfer function

Input #1

Train: 70%Validate: 15%Test: 15%

Input #2

Input #3 Test: 15%Input #3

Input #N

…

Phase IIPhase II

• All 16 activities collected in continuous fashionAll 16 activities collected in continuous fashion (n=5).

• Decision tree used to characterize static• Decision tree used to characterize static activities.

O i i l ANN d d f 5• Original ANN used to process data from 5 new subjects.

Results

• Highl Certain (>90%) 62 14% S h t C t i (50 70%) 1 91%• Highly Certain (>90%) = 62.14%

• Moder. Certain (70‐90%) = 2.29%• Somewhat Certain (50‐70%) = 1.91%

• Unknown = 33.65%

Results

Recognition Rate of ANN = 91.6% ± 3.7% (n=5)

• Highl Certain (>90%) 62 14% S h t C t i (50 70%) 1 91%• Highly Certain (>90%) = 62.14%

• Moder. Certain (70‐90%) = 2.29%• Somewhat Certain (50‐70%) = 1.91%

• Unknown = 33.65%

Combined Approach to MonitoringBone HealthBone Health

Task 1

Task 2

WirelessInterface

.

Total DLS

Wireless Interface

ActivityRecognition

.

.

Task n

SQUAT

Complete AlgorithmComplete Algorithm=∑Measured DLS + ∑Library DLS Values

KEY DATA MANAGEMENT QUESTIONSKEY DATA MANAGEMENT QUESTIONS

The ATD system will output 12 channels of dataThe ATD system will output 12 channels of data continuously for up to 12 hours

• How can we more efficiently manage data ll i i i ?collection in remote settings?

• How can we minimize crew time dedicated to data transfer?

• What can we do to better safeguard data gcollected during space missions?

www.bioserve.colorado.edu/

Open source, software framework that provides Plug‐and‐Play operation for wireless devices and y p

networks to communicate to a centralized system.• Interoperability‐allows voice, video, and data to be p ystored, transported, and displayed.

• No vendor lock‐in.

• Independent developer support (C, Python).

• Works in conjunction with the Delay Tolerant j yNetworking (DTN) protocol (a.k.a. Interplanetary Internet).

BioNet•Hardware Abstractors (HAB) publish data to BioNet. •User applications (APP) subscribe to data in BioNet.

UW APP-Displays raw data stream on a screen

UW HAB-Interfaces with ATD (bluethooth)

SummarySummary

• Demonstrated capacity to wirelessly monitorDemonstrated capacity to wirelessly monitor and successfully classify a continuous stream of exercise activity on the CCDof exercise activity on the CCD.

• Successful data management with BioNet in the lab settingthe lab setting.

• These accomplishments offer new possibilities f i i i dfor remote exercise monitoring and immediate subject feedback.

Thank You!Thank You!2010 AsMA Fellows Scholarship Committee

This work was supported by the National Space Biomedical

Research InstituteG t BL 1301 & MA01603Grants BL-1301 & MA01603

GRCUniversity of CaliforniaSan Francisco