RESUMO
This study develops a home-based frailty detection device that uses embedded systems and wireless sensing technology. This system helps monitor the impact of aging among the elderly through wireless automatic detection. The detection system consists of four devices. The first device, called eScale, simulates the traditional falling ruler test to measure reaction time. Another device, called eChair, measures the pressure exerted by a test subject through a pressure sensor. It is used to test three symptoms of frailty, namely slowness of movement, physical weakness, and body weight. The third device, called ePad, consists of a soft membrane switch placed on the ground to detect footsteps and is used to test balance. The fourth device, called eReach, measures displacement through ultrasound sensors. It is used to carry out the functional reach test. The sampling rate of each device is the main factor that determines system performance. When the test distance was set to 5 m for Home-Gateway, a 1-Hz sampling rate showed the best performance (98 %). Up to eight devices can be connected simultaneously to the gateway. The proposed system was compared with conventional approaches through testing with test subjects (n = 8). The results of the five tests were as follows: standing forward bend (r = 0.929), balance (r = 0.996), slowness of movement (r = 0.976), and physical weakness (r = 0.991), with p < 0.01. In the reaction time test, r = 0.871, with p < 0.1. All results suggest high correlations. Tests of aging symptoms were performed on 309 people aged over 65 years. Among males, degradation of over 20 % was found in the areas of physical weakness, slowness of movement, and functional reach. Among females, a degradation of 75 % was found in the balance test.
RESUMO
The purpose of this study is to integrate wireless sensor technologies and artificial neural networks to develop a system to manage personal frailty information automatically. The system consists of five parts: (1) an eScale to measure the subject's reaction time; (2) an eChair to detect slowness in movement, weakness and weight loss; (3) an ePad to measure the subject's balancing ability; (4) an eReach to measure body extension; and (5) a Home-based Information Gateway, which collects all the data and predicts the subject's frailty. Using a furniture-based measuring device to provide home-based measurement means that health checks are not confined to health institutions. We designed two experiments to obtain optimum frailty prediction model and test overall system performance: (1) We developed a three-step process to adjust different parameters to obtain an optimized neural identification network whose parameters include initialization, L.R. dec and L.R. inc. The post-process identification rate increased from 77.85% to 83.22%. (2) We used 149 cases to evaluate the sensitivity and specificity of our frailty prediction algorithm. The sensitivity and specificity of this system are 79.71% and 86.25% respectively. These results show that our system is a high specificity prediction tool that can be used to assess frailty.