TY - GEN
T1 - SAFLOOR
T2 - 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018
AU - Tangkongchitr, Pasit
AU - Buathang, Mingkamon
AU - Unsuwan, Thanavit
AU - Wongpatikaseree, Konlakorn
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The main reason for hospital admission of elderly people worldwide is falls. The longer one has to stay on the floor due to inability to get up after falling, the more severe the injury can become. Existing fall detection devices that need to be worn on the body is not very useful because the elderly simply forget or do not want to wear them. From this, we have created a fall detection system named 'SaFloor' - a soft mat with force sensors embedded inside. SaFloor can be placed in fall-prone areas, such as by the bedside, bathroom, at the bottom of stairs, etc. It can distinguish between a real fall and other impacts such as walking and dropped objects, and send out a notification to a family member or a care giver via Line message when a fall is detected. The experiment consisting of 14 participants with different weights and heights shows that SaFloor has a successful fall detection rate of 88%.
AB - The main reason for hospital admission of elderly people worldwide is falls. The longer one has to stay on the floor due to inability to get up after falling, the more severe the injury can become. Existing fall detection devices that need to be worn on the body is not very useful because the elderly simply forget or do not want to wear them. From this, we have created a fall detection system named 'SaFloor' - a soft mat with force sensors embedded inside. SaFloor can be placed in fall-prone areas, such as by the bedside, bathroom, at the bottom of stairs, etc. It can distinguish between a real fall and other impacts such as walking and dropped objects, and send out a notification to a family member or a care giver via Line message when a fall is detected. The experiment consisting of 14 participants with different weights and heights shows that SaFloor has a successful fall detection rate of 88%.
KW - Fall detection
KW - Force sensor
KW - Internet of things
UR - https://www.scopus.com/pages/publications/85065097273
U2 - 10.1109/iSAI-NLP.2018.8692857
DO - 10.1109/iSAI-NLP.2018.8692857
M3 - Conference contribution
AN - SCOPUS:85065097273
T3 - 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018 - Proceedings
BT - 2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 November 2018 through 17 November 2018
ER -