/
Automated camera-based fall detection of elderly persons Automated camera-based fall detection of elderly persons

Automated camera-based fall detection of elderly persons - PowerPoint Presentation

mitsue-stanley
mitsue-stanley . @mitsue-stanley
Follow
402 views
Uploaded On 2017-04-17

Automated camera-based fall detection of elderly persons - PPT Presentation

Alex Edgcomb Department of Computer Science and Engineering University of California Riverside httpwwwexaminercomarticlefallprevention Copyright 2014 Alex Edgcomb UC Riverside 1 of 37 ID: 538555

edgcomb fall alex 2014 fall edgcomb 2014 alex riverside copyright detection privacy video monitoring mbr head assistive tracking vahid

Share:

Link:

Embed:

Download Presentation from below link

Download Presentation The PPT/PDF document "Automated camera-based fall detection of..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Slide1

Automated camera-based fall detection of elderly persons

Alex EdgcombDepartment of Computer Science and EngineeringUniversity of California, Riverside

http://www.examiner.com/article/fall-prevention

Copyright © 2014 Alex Edgcomb, UC Riverside.

1

of 37Slide2

Outline

Elderly person falls and background work

SynchSM and moving-region-based fall detectionOther related work

Copyright © 2014 Alex Edgcomb, UC Riverside.

2

of 37Slide3

Falls in the elderly population need to be

detectedLeading cause of injury-related

hospitalization1 and death234% have fallen in the last year

314% have fallen more than

once3Post-fall long lie correlated with passing-away4

50% who experience a long lie pass-away within 6 months4

1Baker, S.P. and A.H. Harvey. Fall injuries in the elderly. Clinics in geriatric medicine, 1985.2Hoyert, D.L., K.D. Kochanek, and S.L. Murphy. Deaths: final data for 1997. National vital statistics reports, 1999.

3

Lord

S.R., J.A. Ward, P. Williams, and K.J. Anstey. An epidemiological study of falls in older community-dwelling

women.

Australian journal of public health, 1993.

4

Wild

, D., U.S. Nayak, and B. Isaacs. How dangerous are falls in old people at home? British medical journal (Clinical research ed.), 1981.

http://www.examiner.com/article/fall-prevention

http://www.presstv.ir/detail/218170.html

Copyright © 2014 Alex Edgcomb, UC Riverside.

3

of 37Slide4

Falls need to be

automatically detected.False alarm rates must be low.

5

Genworth 2013: Cost of care. https

://www.genworth.com/corporate

/about-genworth/industry-expertise/cost-of-care.html

In-home care

$3,400 – 5,800 / mo.

5

http://www.chcb.org/services-programs/medical-care/elder-care

http://

www.lifelinesys.com/content/

lifeline-products/auto-alert

http://www.mobilehelpnow.com/products.php

Copyright © 2014 Alex Edgcomb, UC Riverside.

4

of 37

Discontinued…

AmberSelect and Alert1

Discontinued b/c

false alarms too high

http://

www.primemedicalalert.com/fall-detection.html

Automated monitoring

Under $100 / mo.Slide5

Reasons for video-based assistive monitoring

Detect

manyevents and trends

Privacy

enhance-able

7,8,9

Body-worn

Pro: Anywhere

Con: Not always

worn

6

http://www.mobilehelpnow.com/products.php

Copyright © 2014 Alex Edgcomb, UC Riverside.

5

of 37

6

Bergmann

, J.H.M. and A.H. McGregor. Body-Worn Sensor Design: What Do Patients and Clinicians Want? Annals of Biomedical Engineering. Volume 39, pgs. 2299-2312, 2011.

7

Beach

, S., R. Schulz, K. Seelman, R. Cooper and E. Teodorski. Trade-Offs and Tipping Points in the Acceptance of Quality of Life Technologies: Results from a Survey of Manual and Power Wheelchair Users.

Intl.

Symposium on Quality of Life Technology, 2011.

8

Beach

, S., R. Schulz, J. Downs, J. Mathews, B Barron and K. Seelman. Disability, Age, and Informational Privacy Attitudes in Quality of Life Technology Applications: Results from a National Web Survey. ACM Transactions on Accessible

Computing,

2009

.

9

Demiris

, G., M.J. Rantz, M.A. Aud, K. D. Marek, H.W. Tyrer and M. Skubic, A.A. Hussam. Older adults’ attitudes towards and perceptions of ‘smart home’ technologies: a pilot study. Medical Informatics and The Internet in Medicine, 2004.Slide6

Approaches to camera-based fall detection

Increasing order of computational complexity

Minimum bounding rectangle (MBR)

Head tracking

3D projection

10

standing

laying

1

0

Auvinet

, E., F. Multon, A. Saint-Arnaud, J. Rousseau, and J.

Meunier. Fall

detection with multiple

cameras:

An

occlusion-resistant method based on 3-d silhouette vertical

distribution. Information

Technology

in

Biomedicine

, IEEE Transactions on 15, no. 2 (2011): 290-300.

Copyright © 2014 Alex Edgcomb, UC Riverside.

6

of 37

Image from paper by Auvinet

20Slide7

MBR-based fall detection

11Hung, D.H. and H. Saito. The Estimation of Heights and Occupied Areas of Humans from Two Orthogonal Views for Fall Detection. IEEJ Trans. EIS 133, no. 1, 2013.

12Miaou, S.-G., P.-H. Sung and C.-Y. Huang. A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information.Proceedings of the 1st Distributed Diagnosis and Home Healthcare Conference, 2006

.1

3Thome, N., S. Miguet and S. Ambellouis. A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach. IEEE Transactions on Circuits and Systems

for Video Technology, Vol. 18, No. 11, November 2008.

Copyright © 2014 Alex Edgcomb, UC Riverside.

Hung

Occupied area and height

1

1

Miaou – Height-to-width threshold

1

2

7

of 37

Thome – Height and width probabilistic model

1

3Slide8

Rougier – Head’s vertical velocity

1

4

standing

laying

Auvinet – Person’s height volume

16

Anderson – Combine two silhouettes

1

5

H

ead tracking and 3D projection

Copyright © 2014 Alex Edgcomb, UC Riverside.

1

4

Rougier

, C., J. Meunier, A. St-Arnaud, and J. Rousseau. Monocular 3D head tracking to detect falls of elderly people. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, pp. 6384-6387. IEEE, 2006

.

1

5

Anderson

, D.,

et al.

Linguistic summarization of video for fall detection using voxel person and fuzzy logic. Computer Vision and Image Understanding

113,

2009.

1

6

Auvinet

, E., F. Multon, A. Saint-Arnaud, J. Rousseau, and J. Meunier. Fall detection with multiple cameras: An occlusion-resistant method based on 3-d silhouette vertical distribution. Information Technology in Biomedicine, IEEE Transactions on 15, no. 2, 2011.

8

of 37Slide9

Outline

Elderly person falls and background workMoving-region and synchSM-based fall detection

Other related work

Copyright © 2014 Alex Edgcomb, UC Riverside.

9

of 37Slide10

Person tracking with in-home video via foregrounding

MBR builder

(adjacent pixel groups merged)

MBR filters

(dampen, smooth, & glitch-removal)

Background model

17,18

(Pixel-level GMM based on color)

Copyright © 2014 Alex Edgcomb, UC Riverside.

Stop learning background if insignificant amount of motion

Learn a second frame with MBR area replaced by background

17

Zivkovic

, Z. Improved adaptive Gaussian mixture model for background subtraction. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 2, pp. 28-31. IEEE, 2004

.

18

OpenCV

. http://opencv.org/. November 2013.

Current

frame

Foreground

Minimum bounding

rectangle (MBR)

Person tracking may occur on the camera itself

Computer vision person trackers tend to be 10x slower because of 3D projections and additional modeling

10

of 37

GMM = Gaussian mixture modelSlide11

Synchronous state machines: Good fit for fall detection

Copyright © 2014 Alex Edgcomb, UC Riverside.

11 of 37

Normal behavior

Rapid descent

Extended lay

Normal behavior

Fall suspected

Fall detected

Rapid descent

Extended lay

Not laying

hadFall = 0

hadFall = 0

hadFall = 1

Sitting, standing, or laying

Descent velocity

hadFallSlide12

Synchronous state machine (SynchSM) fall detection (1 of 3)

Copyright © 2014 Alex Edgcomb, UC Riverside. 12

of 37

MBR tracker

SynchSMs promote capturing specific, modular behavior.Slide13

SynchSM fall detection (2 of 3)

Copyright © 2014 Alex Edgcomb, UC Riverside. 13

of 37Slide14

SynchSM fall detection (3 of 3)

Copyright © 2014 Alex Edgcomb, UC Riverside.

14 of 37

* Camera may only contribute single-camera fall score if a person is observed by that camera.Slide15

SynchSM fall detection vs. state-of-the-art

Copyright © 2014 Alex Edgcomb, UC Riverside.

15 of 37

# of cameras

SynchSM

HungAuvinet

RougierAnderson

Miaou

Thome

1

0.960

-

-

0.955

-

0.900

0.820

2

0.990

0.958

-

-

1.000

-

0.980

3

0.998

-

0.806

-

-

-

-

4

1.000

-

0.997

-

-

-

-

5

1.000

-

0.999

-

-

-

-

6

1.000

-

1.000

-

-

-

-

7

-

-

1.000

-

-

-

-

8

-

-

1.000

-

-

-

-

Sensitivity

A dash (-) means unreported or not applicable, such as Hung’s algorithm that uses exactly two cameras.

# of cameras

SynchSM

Hung

Auvinet

Rougier

Anderson

Miaou

Thome

1

0.995

-

-

0.964

-

0.860

0.980

2

1.000

1.000

-

-

0.938

-

1.000

3

1.000

-

1.000

-

-

-

-

4

0.995

-

0.998

-

-

-

-

5

0.993

-

1.000

-

-

-

-

6

1.000

-

1.000

-

-

-

-

7

-

-

1.000

-

-

-

-

8

-

-

1.000

-

-

-

-

Specificity

22 recordings from University of Montreal data set

;

each recording has multiple labels

Trained on 1 recording by selecting smallest threshold for sit-lay that got perfect accuracy

Tested with all combinations of remaining 21 videos

Did not use OK-to-lay SMs.

MBR

MBR

MBR

MBR

Head

3D proj.

3D proj.Slide16

Fall behavior coverage

Fall

behaviorSynchSMHung

Auvinet

RougierAnderson

MiaouThome

Suspected fall eventY

Y

Y

Person orientation

Y

Y

Y

Y

Y

Y

Y

Fall sense

Y

Y

Y

Y

Y

Copyright © 2014 Alex Edgcomb, UC Riverside.

16

of 37

MBR

MBR

MBR

MBR

Head

3D proj.

3D proj.

Did not consider sudden downward movement

Did not give time for person to get upSlide17

Trade-off: Accuracy and efficiency (1 of 2)

Copyright © 2014 Alex Edgcomb, UC Riverside.

17 of 37

Higher is better. Closest to top-right is best.

Combined accuracy score = sensitivity

*

specificitySlide18

Trade-off: Accuracy and efficiency

(2

of 2)

Copyright © 2014 Alex Edgcomb, UC Riverside.

18 of 37

Higher is better. Closest to top-right is best.

Combined accuracy score = sensitivity

*

specificitySlide19

SynchSM fall detection on other data sets

Ran synchSM fall detection on 55 of my own recordings (26 falls, 29 non-falls) using 1 and 2 cameras.Perfect accuracyRan synchSM fall detection on 22.5 hours of normal activity videos using 1 and 2 cameras.

Perfect accuracyCopyright © 2014 Alex Edgcomb, UC Riverside.

19 of 37Slide20

With more resources, can synchSMs do better?Can head tracking improve fall detection?

Copyright © 2014 Alex Edgcomb, UC Riverside.

20 of 37Slide21

Fall detection accuracy: 2D/3D head tracking vs MBR tracking

87 video recordings, 1 min each1969 non-confounding recordings (35 fall, 34 non-fall)

18 confounding recordings (5 fall, 13 non-fall)Automated MBR: height, width, and topManual 2D head tracking by clicking on headManual 3D head tracking by estimating head height from groundSame synchSMs. Suspected fall used feature.

Perfect accuracy with non-confounding scenarios

Copyright © 2014 Alex Edgcomb, UC Riverside.

21 of 37

19Edgcomb, A. and F. Vahid. Video-based fall detection dataset with 2D and 3D head tracking, and moving-region tracking. http://www.cs.ucr.edu/~aedgcomb/3D_2D_head_an_MBR_videos.html, June 2014.

Head vertical position:

342 pixels

Head height:

5.5 feetSlide22

Confounding recordings:Head tracking vs MBR tracking (1 of 2)

Copyright © 2014 Alex Edgcomb, UC Riverside.

22 of 37

Confounding recordings

3D head

2D headMBR top

MBR heightMBR widthCrouch with box

Y

Kneel and move

chair

Y

Sit quickly

Y

Y

Sit then

toss up item

Y

Y

Sit then hands to side

Y

Y

Y

Hands up, down, then lay

Y

Y

Y

Y

Hands up, down, then sit

1

Y

Y

Y

Y

Y

Hands

up, down, then sit 2

Y

Y

Y

Y

Y

Sit then hands up/down

Y

Y

Y

Y

Y

Lay then toss up item

Y

Y

Y

Y

Y

Hands to side then sit

Y

Y

Y

Y

Y

Stand then toss up item

Y

Y

Y

Y

Y

Set cushion on couch

Y

Y

Y

Y

Y

Non-falls

Confused person orientation synchSM

Head tracking could tell that

head not near groundSlide23

Confounding recordings:Head tracking vs MBR tracking

(2 of 2)Copyright © 2014 Alex Edgcomb, UC Riverside.

23 of 37

Confounding recordings

3D head

2D headMBR top

MBR heightMBR widthFall w/ vacuum 1

Y

Y

Y

Y

Y

Fall w/ vacuum 2

Put book in shelf

Y

Y

Y

Y

Y

Look under couch

Y

Y

Y

Y

Y

Take picture off wall

Y

Y

Y

Y

Y

Falls

3D head

2D

head

MBR top

MBR height

MBR width

Sensitivity

0.80

0.80

0.80

0.80

0.80

Specificity

1.00

0.85

0.54

0.54

0.54

Summary

Head tracking allowed rule that head had to be near the ground.

MBR suitable for a variety of scenarios.

If confounding scenarios likely, then head tracking may be justified.

Confused person orientation synchSMSlide24

Outline

Elderly person falls and background workSynchSM and moving-region-based fall detection

Other related work

Copyright © 2014 Alex Edgcomb, UC Riverside.

24

of 37Slide25

Assistive monitoring for the elderly

Copyright © 2014 Alex Edgcomb, UC Riverside.

Assistive monitoring analyzes data from cameras and sensors for events and trends of interest.

25

of 37Slide26

Commercial in-home assistive monitoring

GrandCare

Many sensor-based anomaly detectionand if-then user programmability

QuietCare (Intel and GE)

Motion sensor-based

anomaly detection

BeCloseMany sensor-basedanomaly detection

Copyright © 2014 Alex Edgcomb, UC Riverside.

http://beclose.com

/

http://www.grandcare.com/sensors/

http://www.careinnovations.com

/

26

of 37Slide27

Master’s work – Monitoring and Notification Flow Language (MNFL)20,21

Copyright © 2014 Alex Edgcomb, UC Riverside.

20

Edgcomb

, A., and F. Vahid. Feature extractors for integration of cameras and sensors during end-user programming of assistive monitoring systems. In Proceedings of the 2nd Conference on Wireless Health, p. 13. ACM, 2011

.

21

Edgcomb

, A., and F. Vahid. MNFL: the monitoring and notification flow language for assistive monitoring. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 191-200. ACM, 2012.

Spatial

programming

more intuitive than

temporal. Under 8 mins to solve goal.

No compilation. Blocks

are always

executing, so users

get instant

feedback.

27

of 37

EasyNotify exampleSlide28

Estimating daily energy expenditure from video for assistive monitoring22

Actor

Fidelity1

r = 0.996

2r

= 1.0003r

= 0.9834r = 0.997

Combined

r

= 0.997

(Ideal is 1.0)

Average accuracy =

90.9%

(about same as body-worn device)

Copyright © 2014 Alex Edgcomb, UC Riverside.

22

Edgcomb

, A., and F. Vahid. Estimating Daily Energy Expenditure from Video for Assistive Monitoring, IEEE International Conference on Healthcare Informatics (ICHI), 2013

. (to appear)

Energy expenditure levels on Monday

We considered 12 features

Motion in video not correlated with energy expenditure (r = -0.01, p = 0.53)

Horizontal acceleration had highest correlation (r = 0.80,p < 0.01)

Power regression had best fit (R

2

-value = 0.76) compared to linear, logarithmic, and exponential regressions.

Feature F

28

of 37

Data set available:

http://www.cs.ucr.edu/~

aedgcomb/

videoBasedEnergyEstimate.html

Fidelity = correlation(video-based Calories, BodyBugg)

Accuracy = 1 - (|expected - observed|/expected)Slide29

Privacy perception and fall detection accuracy with privacy-enhanced video

23

Copyright © 2014 Alex Edgcomb, UC Riverside.

23

Edgcomb, A., and F. Vahid. Privacy Perception and Fall Detection Accuracy for In-Home Video Assistive Monitoring with Privacy Enhancements, ACM SIGHIT (Special Interest Group on Health Informatics) Record, 2012.

Common privacy enhancements not providing sufficient privacy

Privacy critical for adoption but makes events harder to detect

Did a fall occur? If so,

at about what second in the video?

Does this style provide sufficient privacy for grandpa? Yes / No.

29

of 37

376 participantsSlide30

Falls have a characteristic

shape that

is nearly identical for raw and privacy-enhanced

video

Copyright © 2014 Alex Edgcomb, UC Riverside.

30

of 37Slide31

Privacy-enhanced fall detection24 (1

of 2)

Observed shape

Characteristic

fall shape

Similarity0.84

Dynamic time warping

Non-fall

Non-fall

Fall

Observed shape

0.46

0.88

Binary tree

classification

25

Copyright © 2014 Alex Edgcomb, UC Riverside.

24

Edgcomb

, A. and F. Vahid. Automated Fall Detection on Privacy-Enhanced Video. IEEE Engineering in Medicine and Biology Society, 2012

.

25

Mueen

, A., E. Keogh and N. Young. Logical-shapelets: An Expressive Primitive for Time Series Classification. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

.

DTW established time series technique

Script to produce this image provided by Professor Keogh, 2012.

31

of 37Slide32

Privacy-enhanced fall detection

(2 of 2)

Privacy enhancementAverage sensitivityAverage specificity

Raw

0.91

0.92Blur1.00

0.67Silhouette0.91

0.75

Oval

0.91

0.92

Box

0.82

0.92

Copyright © 2014 Alex Edgcomb, UC Riverside.

-This method does not consider the time a person spends on the ground post-fall.

+More accurate fall detection than human observers.

Binary tree classifier trained on raw video only

Evaluated using leave-one-out method

23 videos, 1 min each

Each video labeled fall or not-fall

32

of 37Slide33

Automated in-home assistive monitoring with privacy-enhanced video

26

Copyright © 2014 Alex Edgcomb, UC Riverside.26Edgcomb, A. and F. Vahid. Automated In-Home Assistive Monitoring with Privacy-Enhanced

Video, IEEE International Conference on Healthcare

Informatics (ICHI), 2013. (to appear)

Already discussed

Energy

trends

Fall detection

33

of 37

Arisen in morning

Person

enters main living

area

Abnormally inactive

Person home but inactive for extended period

In room too long

Enter to left

Exit from left

In region too longSlide34

Energy estimation

fid./acc.

Fall detection sens./spec.In room too long sens./

spec.

Arisen in morning sens. /

spec.In region too long sens. / spec.

Abnormally inactive during day sens./spec.Raw0.997 /90.9%

0.91 /

0.92

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

Blur

0.994 /

80.5%

1.00 /

0.67

1.0 / 1.0

1.0 / 1.0

0.5

/ 1.0

1.0 / 1.0

Silhouette

0.998 /

85.0%

0.91 /

0.75

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

Oval

0.997 /

85.6%

0.91 /

0.92

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

Box

1.000 /

84.3%

0.82 /

0.92

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

1.0 / 1.0

Most goals were achieved equally well

even with privacy enhancements

Copyright © 2014 Alex Edgcomb, UC Riverside.

34

of 37

Data set available:

http://www.cs.ucr.edu

/~

aedgcomb/MNFLevents.html

Trained on raw

video only

MNFL goals trained on different person than tested

MNFL goalsSlide35

The background model tended to learn the blue

much more than the actorCopyright © 2014 Alex Edgcomb, UC Riverside.

35 of 37

Current frame

Background model

Foreground and MBRSlide36

Accurate and efficient algorithms that adapt to privacy-enhanced video

27

Copyright © 2014 Alex Edgcomb, UC Riverside.

27

Edgcomb, A. and F. Vahid. Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive Monitoring, ACM Transactions on Management Information Systems (TMIS): Special Issue on Informatics for Smart Health and Wellbeing, 2013.

Specific-color hunter

Edge-void filler

Fall detection

Energy estimation

36

of 37Slide37

Contributions

MBR and synchSM-based fall detection is more accurate and efficient than all previous work.MBR and head tracking are equally accurate,

except in very specific cases.Although monitoring goal accuracy degrades with privacy-enhanced video, adaptive algorithms can compensate without loosing computational efficiency.The common privacy enhancements of silhouette and blur provide insufficient privacy, whereas a bounding oval or box were sufficient.Copyright © 2014 Alex Edgcomb, UC Riverside.

37

of 37Slide38

Summary of graduate research

Monitoring and notification flow language for assistive monitoringEdgcomb, A., and F. Vahid. Feature extractors for integration of cameras and sensors during end-user programming of assistive monitoring systems. In Proceedings of the 2nd Conference on Wireless Health, p. 13. ACM, 2011

. (2 pages)Edgcomb, A., and F. Vahid. MNFL: the monitoring and notification flow language for assistive monitoring. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 191-200. ACM, 2012.Estimating daily energy expenditure from video for assistive monitoring

Edgcomb, A., and F. Vahid. Estimating Daily Energy Expenditure from Video for Assistive Monitoring, IEEE International Conference on Healthcare Informatics (ICHI), 2013. (to appear)

Participant privacy perceptions and fall detection accuracy with privacy enhancementsEdgcomb, A., and

F. Vahid. Privacy Perception and Fall Detection Accuracy for In-Home Video Assistive Monitoring with Privacy Enhancements, ACM SIGHIT (Special Interest Group on Health Informatics) Record, 2012.

Automated fall detection on videoEdgcomb, A. and F. Vahid. Automated Fall Detection on Privacy-Enhanced Video. IEEE Engineering in Medicine and Biology Society, 2012. (4 pages)Edgcomb, A. and F. Vahid. Accurate and Efficient Video-based Fall Detection using Moving-Region and State

Machines. (To be submitted)

Automated in-home assistive monitoring with privacy-enhanced video

Edgcomb

, A. and F. Vahid. Automated In-Home Assistive Monitoring with Privacy-Enhanced Video, IEEE International Conference on Healthcare Informatics (ICHI), 2013. (to appear)

Edgcomb

, A. and F. Vahid. Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive Monitoring, ACM Transactions on Management Information Systems (TMIS): Special Issue on Informatics for Smart Health and Wellbeing, 2013

.

Efficacy of digitally-enhanced education

Edgcomb

, A. and F. Vahid. Interactive Web Activities for Online STEM Learning Materials, American Society for Engineering Education Pacific Southwest Section Conference, 2013

.Edgcomb, A. and F. Vahid. Effectiveness of Online Textbooks vs. Interactive Web-Native Content, Proceedings of ASEE Annual Conference, 2014. (to appear)

Copyright © 2014 Alex Edgcomb, UC Riverside.

38