TY - GEN
T1 - Experimental Analysis of Cost-Effective Mobile Sensing Technologies for Activity Analytics in Elderly Care
AU - Newcombe, Lee
AU - Yang, Po
AU - Cater, Chris
AU - Hanneghan, Martin
AU - Qi, Jun
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/22
Y1 - 2019/1/22
N2 - Advancements in sensor technology has provided new ways for researchers to monitor the elderly in uncontrolled environments. Sensors have become smaller, cheaper and can now be worn on the body. Smart phones are also more common in the average household and can provide some analysis of behaviour. Because of this, researchers are able to monitor behaviours in a more natural setting, which can produce useful data. For those suffering with a mental illness, this is important as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However, issues with the sensors and the involvement of human factors are challenges that need to be addressed. These challenges must be taken into consideration in addition to the behavioural symptoms of Dementia that can appear in the elderly. The application of sensor technologies can aid in improving the quality of life of an elderly person with Dementia and monitor the progression of the disease through behavioural analysis. This paper will provide an experiment protocol that can be used to monitor those with mild cognitive impairment in a natural environment. We will also provide data and results from an initial experiment and discuss our plans for future experimentation.
AB - Advancements in sensor technology has provided new ways for researchers to monitor the elderly in uncontrolled environments. Sensors have become smaller, cheaper and can now be worn on the body. Smart phones are also more common in the average household and can provide some analysis of behaviour. Because of this, researchers are able to monitor behaviours in a more natural setting, which can produce useful data. For those suffering with a mental illness, this is important as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However, issues with the sensors and the involvement of human factors are challenges that need to be addressed. These challenges must be taken into consideration in addition to the behavioural symptoms of Dementia that can appear in the elderly. The application of sensor technologies can aid in improving the quality of life of an elderly person with Dementia and monitor the progression of the disease through behavioural analysis. This paper will provide an experiment protocol that can be used to monitor those with mild cognitive impairment in a natural environment. We will also provide data and results from an initial experiment and discuss our plans for future experimentation.
KW - Behaviour analytics
KW - Healthcare
KW - Internet of things
UR - http://www.scopus.com/inward/record.url?scp=85062505463&partnerID=8YFLogxK
U2 - 10.1109/HPCC/SmartCity/DSS.2018.00238
DO - 10.1109/HPCC/SmartCity/DSS.2018.00238
M3 - Conference Proceeding
AN - SCOPUS:85062505463
T3 - Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
SP - 1442
EP - 1448
BT - Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
Y2 - 28 June 2018 through 30 June 2018
ER -