From Learning Gait Signatures of Many Individuals to Reconstructing Gait Dynamics of One Single Individual

Abstract

Based on the same databases, we computationally address two seemingly highly related, in fact drastically distinct, questions via computational data-driven algorithms: 1) how to precisely achieve the big task of differentiating gait signatures of many individuals and 2) how to reconstruct an individual's complex gait dynamics in full. Our brains can "effortlessly" resolve the first question, but will definitely fail in the second one because many fine temporal scale gait patterns surely escape our eyes. Based on accelerometers' 3D gait time series databases, we link the answers toward both questions via multiscale structural dependency within gait dynamics of our musculoskeletal system. Two types of dependency manifestations are explored. We first develop simple algorithmic computing called Principle System-State Analysis for the coarse dependency in implicit forms. Principle System-State Analysis is shown to be able to efficiently classifying among many subjects. We then develop a multiscale Local-1st-Global-2nd Coding Algorithm and a landmark computing algorithm. With both algorithms, we can precisely dissect rhythmic gait cycles, and then decompose each cycle into a series of cyclic gait phases. With proper color-coding and stacking, we reconstruct and represent an individual's gait dynamics via a 3D cylinder to collectively reveal universal deterministic and stochastic structural patterns on centisecond (10 milliseconds) scale across all rhythmic cycles. This 3D cylinder can serve as "passtensor" for authentication purposes related to clinical diagnoses and cybersecurity.

Mimicking Gait Dynamics: A Step Toward Precision Learning of Human Activities

“…That is, too many interacting relational patterns are likely resulted because computed foot-specific states are varying in their durations. This phenomenon was shown and concluded in an experiment in [10] via Lempel-Ziv complexity evaluations.…”

“…Discovering such a personal collection of biomechanical states is a computing task that has not been well established in literature yet. In our previous study [10], we reported findings of alternating cyclic states. Such data-driven cluster-based states' variations are too big to be biomechanically coherent.…”

“…Further, HC algorithm at the same time collects similar time points across different cycles. This fact was established in [10] via Lempel-Ziv complexity evaluations [12].…”

“…However, even up to today, gait dynamics related research primarily focuses on individual's gait recognition [4]- [6] and authentication [7], [8]. Our own previous work [10], which analyzed data from the MAREA database [9] and another database HuGaDB [11], also focused on recognitions and authentication.…”