  As indoor applications grow in diversity, wireless sensing, vital in areas
like localization and activity recognition, is attracting renewed interest.
Indoor wireless sensing relies on signal processing, particularly channel state
information (CSI) based signal parameter estimation. Nonetheless, regarding
reflected signals induced by dynamic human targets, no satisfactory algorithm
yet exists for estimating the acceleration of dynamic path length change
(DPLC), which is crucial for various sensing tasks in this context. Hence, this
paper proposes DP-AcE, a CSI-based DPLC acceleration estimation algorithm. We
first model the relationship between the phase difference of adjacent CSI
measurements and the DPLC's acceleration. Unlike existing works assuming
constant velocity, DP-AcE considers both velocity and acceleration, yielding a
more accurate and objective representation. Using this relationship, an
algorithm combining scaling with Fourier transform is proposed to realize
acceleration estimation. We evaluate DP-AcE via the acceleration estimation and
acceleration-based fall detection with the collected CSI. Experimental results
reveal that, using distance as the metric, DP-AcE achieves a median
acceleration estimation percentage error of 4.38%. Furthermore, in multi-target
scenarios, the fall detection achieves an average true positive rate of 89.56%
and a false positive rate of 11.78%, demonstrating its importance in enhancing
indoor wireless sensing capabilities.
