The document discusses the role of Internet-of-Things (IoT) technologies in healthcare. It describes a multi-tier IoT system architecture consisting of biosensors, personal devices, and servers. Wireless communication standards are used to transmit health data from wearable devices to medical centers. Case studies from TigerPlace and Washington State University demonstrate how IoT can be used to detect physical and mental impairments through sensors and analyze activities of daily living.

1. The Role of Internet-of-Things (IoT) in Healthcare
Gonçalo Frazão | 78136
Sebastião Barros | 78478
Luís Rita | 78680
António Calçada | 79630
IST [MEBiom] | 4th Year | 2nd Semester | Health Systems
The Internet of Things (IoT) has been called the next Industrial Revolution — it will
impact the way all businesses, governments, and consumers interact with the
physical world.

2. IoT-based Multi-tier System
Architecture
BioSensorTier 1: WWBAN
Tier 2:
Tier 3:
Personal Server Personal Digital Assistance
PAN
Internet Server
Hospital/health centers;
Emergency care providers;
Insurance Companies;
Health data repositories. Tier 1
Tier 2
Tier 3
Step 1
Step 2

3. Wireless Communication Standards
for WHM
Steps of transmission of the collected data:
1. Communication between biosensors and the
central node (Tier 1 - Tier 2);
2. Sending the data from the wearable system to
a medical center (Tier 2 - Tier 3).

4. “Repository of electronically maintained information about an individual’s
lifetime health status and health care, stored such that it can serve the
multiple legitimate users of the record.”
ü Integrated access to patient data anywhere
ü Flexible and adaptable
ü Single copy in single format
ü Supports images, vídeos and graphs
ü Legible and coherent information
x Initial investment
x Time to learn how to deal with the system
x Security issues
x Contingency plans in case of failure
Electronic Health Record

5. Electronic Health Record
Functional
components
Integrated view
of patient data
Clinical decision
support
Clinician order
entry
Access to knowledge
resources
Integrated communication
and reporting support

6. QuiO SmartInjector

7. Wearable Devices
Artificial Pancreas
Electrocardiogram monitoring
Oxygen saturation monitoring
MINIMED 670G System
Quardio Quardiocore
Oxitone 1000

8. Apps
→ Smartphone penetration in Portugal was 68.8 % in
September 2016;
→ Most wearable devices (Tier 1) can be connected to
similar apps that allow the user to store, treat,
transmit his data and update his HER;
→ To overcome the unfamiliarity of the elder with IT,
the patient component has a very simple user
interface;
Nursy app
Healthmate

9. IoT in Health Care
Clinical Assistance (CA)
Supervised & Unsupervised Learning (SL & USL)
Physical Impairment Mental Impairment
Detect
[CASE STUDIES]
[Urinary tract infections, pneumonia, heart failure…] [Dementia, MCI]

10. TigerPlace
• Senior housing site located in Columbia, USA;
• Composed of independent living apartments;
• Aging in place concept;
• Meals and hygiene are provided 24/7 by the health carers.

11. Perceived need and preferences for
smart home tech
Method
→ Each group session last for 1h;
→ Advantages? Disadvantages? Willingness to adopt it? Who should
have access to data?
→ Type of wording previously studied;
→ Recorded sessions for analysis purposes.
Demiris et al., 2008
RESULTS
Bed sensors
Motion sensors
Video sensorsGait monitor
Access to data
Proactive vs Active
1st – Patients;
2nd – Family/Heath carers.
TigerPlace

12. Detecting Physical Impairment
Method
1. Event took place → clinicians were
alerted and asked to rate it (1-5, severity
of situation);
2. Retrospective analysis: hospitalizations,
emergency room visits and falls happened
through the same period;
3. Machine learning: fuzzy k-nearest
neighbour, neural networks and SVM.
Clinical analysis: fuzzy pattern tree.
Skubic et al., 2015
[SL, USL & CA]
TigerPlace
4 (alert parameters) × 3 (different time periods)
× 2 (increasing/decreasing values) = 24

13. Detecting Mental Impairment
Method
1. Patients went through DOT, which involves several
activities;
2. Patients’ performance was evaluated based on
duration and quality criteria.
3. Correlation between mental disease of each patient
and the assertiveness they performed the activities,
was represented using a coefficient of
determination (= 0,62).
Dawadi et al., 2013
[SL]
Washington State University CASAS

14. Media meet IoT…
World Trends Portugal Trends

15. Questions?

16. References
[1] Dawadi, P., Cook, D., & Schmitter-Edgecombe, M. (2013). Automated cognitive health assessment using smart home monitoring of complex
tasks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43, 1302-1313;
[2] Demiris, G., Hensel, B. K., Skubic, M., & Rantz, M. (2008). Senior residents' perceived need of and preferences for "smart home" sensor
technologies. International Journal of Technology Assessment in Health Care, 24(1), 120-124. DOI: 10.1017/S0266462307080154;
[3] Skubic, M., Guevara, R., & Rantz, M. (2015). Automated health alerts using in-home sensor data for embedded health assessment. IEEE
Journal of Translational Engineering in Health and Medicine, 3, 1-11;
[4] Motoi, Kosuke; Ogawa, Mitsuhiro; Ueno, Hiroshi; Kuwae, Yutaka; Ikarashi, Akira; Yuji, Tadahiko; Higashi, Yuji; Tanaka, Shinobu; Fujimoto,
Toshiro; Asanoi, Hidetsugu; Yamakoshi, Ken-ichi. (2009). A fully automated health-care monitoring at home without attachment of any
biological sensors and its clinical evaluation. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE
Engineering in Medicine and Biology Society. Conference, 2009: 4323-4326.
[5] Americareusa, “Our Gallery”. Available at: http://www.americareusa.net/p/retirement_community/gallery_1335/columbia-mo-
65201/tigerplace-1335 [Consult. 2017/04/15];