Human Bipedal Locomotion-Based Indoor Location Tracking Using Fiber-Optic Sensors

With the aim of promoting independent living and aging well, indoor location tracking has been a research focus in the area of ambient-assisted living (AAL) environment. In particular, there has been growing interest in object tracking using fiber-optic sensors due to the advantage of data efficiency, low computational cost, and nonintrusive. However, the current research on object tracking with fiber-optic sensors confronts several critical challenges, such as increasing the sensing efficiency and limitations of unnatural walking manner. To address these challenges, in this article, a compressive floor pressure (FP)-sensing approach is proposed based on human bipedal locomotion (HBL) characteristics. Specifically, to retain the natural characteristic of HBL, the spatiotemporal representation of HBL locations is expressed as a three-state vector by dividing the monitoring area into an eight-neighbor-grid structured environment. The spatial sparsity of HBL locations is then incorporated into compressive sensing (CS) mechanism for efficiency enhancement. More specifically, a triplet measurement vector and dynamic uniquely decipherable (TMV-DUD) encoding scheme is used to form the CS matrix. A prototype system of compressive FP sensing is developed for indoor location tracking within 36 grids using only five sensors. The prototype experimental results demonstrate the effectiveness of the proposed compressive tracking approach.