The placement position optimization of a biosensor array for wearable healthcare systems

Advancement in wearable healthcare technology allows a digital revolution in healthcare that is intended to extend life length and improve life quality. Until now, numerous efforts for wearable device-mediated healthcare systems have been focused on achieving accurate monitoring and robust operation by developing cutting-edge biosensors rather than by optimizing the placement position of well-established biosensors. Much therefore still remains unclear about the optimal position involved in biosensor placement. This leads to the need for systematic optimization of biosensor placement position. Here, the placement positions of 2 biosensor arrays (i.e., ECG-TC array and PPG-TC array) are optimized to achieve high-accuracy measurement and high-robustness operation in monitoring both body temperature and heart rate, which are commonly measured as core vital signs in wearable healthcare systems. The ECG-TC array is composed of an electrocardiography (ECG) sensor (for heart rate measurement) and a thermocouple (TC) sensor (for body temperature measurement) while the PPG-TC array consists of a photoplethysmography (PPG) sensor and a TC sensor. 34 major parts in the human body are scored in terms of monitoring accuracy (MA) and operational robustness (OR), which results in the MA index and OR index. Next, the body part having the maximum value, calculated as the product of two indices, is determined as the optimal placement position. The MA index is calculated from the signal magnitude of each biosensor and the OR index is assessed by quantifying the user accessibility, skin extensibility, and perspiration weight of each body part. Our study shows that the ECG-TC array needs to be placed on armpit, chest, and back thigh and the PPG-TC array on thumb, finger, and ear. This means the optimal placement position of biosensor-based wearable healthcare systems is affected by a combination of different biosensors. The findings of this study are expected to contribute to better understanding of biosensors and lead to further improvement in wearable healthcare devices.