RIS-Aided Kinematic Analysis for Remote Rehabilitation

This paper is the first to examine the idea of using reconfigurable intelligent surfaces (RISs) as passive devices that measure the position and orientation of certain human body parts over time. In this paper, we investigate the possibility of utilizing the available geometric information provided by on-body RISs that reflect signals from an off-body transmitter to an off-body receiver for stroke rehabilitation. More specifically, we investigate the possibility of using on-body RISs to estimate the location information over time of upper limbs that may have been impaired due to stroke. This location information can help medical professionals to estimate the possibly time-varying pose and evaluate progress on the rehabilitation of the upper limbs. Our analysis indicates that while the upper limb orientation can be estimated when the receiver is in the near-field of a passive RIS, this orientation cannot be calculated in the far-field. We also present a lower bound on the achievable accuracy for estimating the upper limbs' location in the near-field propagation regime. The accuracy provided by the FIM-based analysis is on the order of 0.01 rad and 1 cm for orientation and position of the upper limbs, respectively. This accuracy can be better than that obtained from inertial measurement units (IMUs), and it does not degrade due to drift. The accuracy values presented are not specific to any algorithm. Instead, the accuracy values obtained through the FIM are a benchmark for any future limb location estimation algorithm. Finally, it is important to state that this work provides a rigorous mathematical framework to take advantage of the wireless signals that are already present to collect useful in-home health data. We acknowledge that RISs, in general, are still in their infancy, and their practical use in any setting depends on future advances in hardware.