Presentation on a research paper published on the applicaton of video gaming technology in detecting falls.

1. Home Monitoring:
By Therese Tisseverasinghe
Video Gaming Technology to Detect Falls

2. https://youtu.be/NCixWY3eaOc
Video: Home sensors enable seniors to live independently

3. Methods of detecting falls
1. Wearable devices
2. Environmentally mounted sensors
• Depth imaging sensor
Hardware: Microsoft Kinect; Computer
Software: libfreenect library

4. Overview
1. Raw data to information
2. Information to decision

5. Step 1: Depth Image Processing
Algorithm

6. Step 1: Depth Image Processing
Algorithm

7. Step 2: Two-Staged Fall Detection Analytics
I. First stage:
(i) Vertical state
(ii) Event segmentation
II. Second stage:
(i) Event features
(ii) Fall confidence
(iii) Ground truth matching

8. Stage Ia: Vertical State Characterization
3 features to characterize the vertical
state (Vs)
Zmax
Zcent
Zpg

9. Stage Ib: On Ground Event Segmentation

10. Graph of Event Segmentation for a Fall

11. Stage IIa. On ground event segmentation
5 features extracted for each on ground event:
1.Minimum Vertical Velocity
2.Minimum Vertical Acceleration
3.Mean Vavg
4.Occlusion Adjusted Change in Zpg
5.Minimum Frame-to-Frame Vertical Velocity

12. Stage IIb. Ensemble for Fall Confidence
• Fall confidence computation
• An ensemble of binary decision trees
• Each leaf node represents a predicted value based on
training data

13. Stage IIc. Ground truth matching
• Matching “on ground events” to “ground truth” by
condition:

14. Challenges
1. Sensor’s field of view
2. Distant falls
3. Sunlight interference
4. False alarms

15. Part II. Course Themes

16. Big Data
• Value: Clinically relevant
• Volume: Continuous monitoring
• Velocity: High-speed processing
• Variety: Heterogenous and unstructured
• Veracity: Uncontrolled environments
• Variability: Non-deterministic models
-(Andreu-Perez, Poon, Merrifield, Wong, & Yang, 2015)

17. Given that this technology enables data
collection that is consistent with the 6 Vs:
• 3,339 days (9 years)
• 14 residences
• Over 80,000 hours
• 7.5 frames per second
• 3.44 TB of disc space
It is Big Data

18. Evidence-based Healthcare
“current best evidence should be used explicitly
and judiciously for diagnosis, management, and
other activities in healthcare settings.”
(Sedig, Parsons, Naimi, & Willoughby, 2015).

19. Given that this technology is based on the following current
health-related information about falls in U.S.:
• 1 in 3 older adults
• Medical cost of falls
• Hip fractures and head traumas
• Reduced mobility and independence
• Increased risk of early death
• Increased risk of complications
• Not receiving immediate assistance
It is evidence-based Healthcare

20. Analytics and Data Mining
The Analytics Pipeline adapted
from Kumar et al. (Hersh,2014)
1. Input data sources
2. Feature extraction
3. Statistical processing
4. Prediction output

21. Given that this technology fulfills the 4 steps of
analytics pipeline:
1. Input data sources: Depth image sensor
2. Feature extraction: Foreground; Vertical state; On ground
event segmentation; 5 features of on ground event
3. Statistical processing: Ensemble for fall confidence
(binary decision trees)
4. Prediction output (descriptive): Ground truth matching
It utilizes big data analytics

22. Data Presentation
“Infographics is an abbreviated
term for an information graphic.
Information is presented in a
logical manner, similar to
storytelling, using data
visualizations, text and
pictures.” Scott et al.

23. The authors utilized graphs (scatter plot and
line charts) in addition to a variety of images
to illustrate the technology.

24. Is it Human-Centered?
Stakeholders:
1. Client
2. Family/physician
3. Researchers/innovators

25. Client: How adapted is this technology to
humans?
• Low cost and widely available
• Unobtrusive system
• Automatic

26. Interactive visualization and
visual analytics
Characteristics of visualization tools: visual representations,
interaction, and distributed processing of tasks.
~(Ola, Buchel, & Sedig, 2015)

27. Questions?