SHECS: A Local Smart Hands-free Elderly Care Support System on Smart AR Glasses with AI Technology

TL;DR

This paper introduces SHECS, a lightweight, hands-free elderly care support system using smart AR glasses with facial recognition and text-to-speech, enabling quick, local resident info access without internet dependency.

Contribution

The paper presents a novel smart AR-based elderly care system that operates locally with high-accuracy facial recognition and voice synthesis, reducing network reliance and improving caregiver efficiency.

Findings

Facial recognition accuracy of 99.3%+ with low false accept rate

Caregiver acceptability rated at 3.6/5 or higher

System operates without continuous internet connection

Abstract

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.