1. A Smart Home Environment to Support Safety and Risk Monitoring for the Elderly
Tongai Chiridza, Janet Wesson & Dieter Vogts
Department of Computing Sciences
Nelson Mandela University
SOUTH AFRICA
03 OCT 2017, NMU

2. Background
The percentage of the aged [Elderly >= 60 years population] in SA is expected to double by 2050.
Health and cause-of-death statistics show a prevalence of chronic diseases, cognitive impairment, and poor balance among the elderly.
In the last population census in SA, about 38% of the elderly persons were using chronic medication by 60.
The risk of an “adverse event” increases with age.
Current monitoring systems are costly, obtrusive, and disintegrated.

3. Aim of Research
To design a model of an affordable smart home environment to support risk and safety monitoring for the elderly living independently.
The main research question is:
How should an affordable smart environment be designed to support risk and safety monitoring for the elderly in a home environment?
What are the safety and risk issues facing the elderly?
How can smart home technologies be incorporated in the home to suit the health care needs of the elderly?
What are the benefits derived from smart home environments?
How do usability and user experience aspects affect the design of a smart home environment for the elderly?

4. Adverse Events
Medical emergencies are common for the elderly with chronic ailments – can lead to unconsciousness.
Cardiovascular diseases and diabetes are the leading causes of emergency situations.
Emergencies could require admission to a medical facility or require certain drugs to be administered immediately.
Panic buttons and speed dialling have been used for emergency contact.
The elderly tend to forget where they put their phones and panic buttons.

5. Typical people involved in home-based care for the elderly

6. Focus group interview

7. Requirements

8. Smart Home Environment Components

9. Equipment

10. Design - IoT

11. Example Dashboard and Data

13. Motion Recordings
No motion detected between and 15:00: warning sent.
Abnormal activity detected between 23:00 and 02:00: warning sent.

14. Evaluation
The goal of the evaluation was to determine if the prototype can support risk and safety monitoring for the elderly.
The selected method was an ex post evaluation consisting of a lab experiment.
The evaluation consisted of a simulation of risk situations that can be experienced in the home.
Each result could have one of the following outcomes:
TP, TN, FP, FN
The classification of the results from a set of events yielded a confusion matrix.

15. Metrics Accuracy Error rate Sensitivity Specificity Acc= TP+TN P+N
ERR = 1-ACC
Sensitivity
SN= TP TP+FN
Specificity
SP= TN TN+FP

17. Discussion
An average accuracy of 94% was obtained from the results of the evaluation with an error rate of 6%.
The average sensitivity obtained was 96.92% and the average specificity was %.
These results show that the rate of not missing a TP is 96.2% and the rate of not missing a TN was 88.93%.
To a greater extent the prototype was able to accurately and consistently support safety and risk monitoring in the home in accordance with the requirements of the elderly living independently.
The prototype will be improved in future by incorporating the feedback regarding the discrepancies observed during the evaluations.

18. Future Work THANK YOU! Daily Activity Pattern Mining
Personalisation Module
More Sensors
Field studies should be conducted to compare the results obtained with the simulation results.
THANK YOU!

19. Questions