Development of gravitationally aligned pendant IMU frailty identifier

This study focused on the practical development of a device for the early detection of frailty by leveraging the advanced Inertial Measurement Unit (IMU) technique. A five-time sit-to-stand test (FTSST) was conducted with people simply wearing a devised IMU pendant to evaluate the degree of frailty. As anticipated, the pendant sensor was recommended to be loosely strapped on older people's bodies for a comfortable outfit. Hence, the measurements from the IMU device would vary with the strap angle of the pedant. A calibration of gravitational alignment, which aligns the measurements to the gravity direction exactly with the FTSST motion signals consistently with an identical principal axis, would benefit the straightforward successive signal analysis and assessment. A prototype was thus developed with the designated calibration by using an open and systematic principle. This study analyzed the signals of accelerometer and gyroscope from IMU, and the subject's demographic profiles comprised a spectrum of features for identification analysis. Pearson correlation and principal component analysis for relevance ranking were used to reduce the feature dimensionality. Classifiers, including Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), were introduced to infer the frailty level of the subject. An innovative signal calibration and alignment process with solid mathematical support was imposed in the scheme. As deployed, the designated frailty identifier arrived at its generalized accuracy of 95.45 % with real clinic subjects in prediction. The alignment calibration process is the key for not only resolving the loose-attachment problem tolerantly but also enhancing the discriminability of the features. The prototype revealed the better integrity in developing a cheap, tiny, wearable device to identify the frailty.