SHECS: A Local Smart Hands-free Elderly Care Support System on Smart AR Glasses with AI Technology
Some elderly care homes attempt to remedy the shortage of skilled caregivers and provide long-term care for the elderly residents, by enhancing the management of the care support system with the aid of smart devices such as mobile phones and tablets. Since mobile phones and tablets lack the flexibil...
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Zusammenfassung: | Some elderly care homes attempt to remedy the shortage of skilled caregivers
and provide long-term care for the elderly residents, by enhancing the
management of the care support system with the aid of smart devices such as
mobile phones and tablets. Since mobile phones and tablets lack the flexibility
required for laborious elderly care work, smart AR glasses have already been
considered. Although lightweight smart AR devices with a transparent display
are more convenient and responsive in an elderly care workplace, fetching data
from the server through the Internet results in network congestion not to
mention the limited display area. To devise portable smart AR devices that
operate smoothly, we first present a no keep alive Internet required smart
hands-free elderly care support system that employs smart glasses with facial
recognition and text-to-speech synthesis technologies. Our support system
utilizes automatic lightweight facial recognition to identify residents, and
information about each resident in question can be obtained hands free link
with a local database. Moreover, a resident information can be displayed on
just a portion of the AR smart glasses on the spot. Due to the limited size of
the display area, it cannot show all the necessary information. We exploit
synthesized voices in the system to read out the elderly care related
information. By using the support system, caregivers can gain an understanding
of each resident condition immediately, instead of having to devote
considerable time in advance in obtaining the complete information of all
elderly residents. Our lightweight facial recognition model achieved high
accuracy with fewer model parameters than current state-of-the-art methods. The
validation rate of our facial recognition system was 99.3% or higher with the
false accept rate of 0.001, and caregivers rated the acceptability at 3.6 (5
levels) or higher. |
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DOI: | 10.48550/arxiv.2110.13538 |