Every Step of the Way: Video-based Parkinsonian Turning Step Counting

As a prominent symptom of Parkinson's disease (PD), turning impairment is evaluated through parameters such as turning angle, duration, and particularly, the number of steps required to complete a turn, which directly reflects motor dysfunction.

Accurate step counting is challenging due to variability in real-world turning movements and atypical shuffling patterns in parkinsonian gait.

Existing methods are predominantly wearable-based, requiring users to wear and manage dedicated devices, which can be inconvenient for continuous daily use.

To address this, we propose a passive, video-based framework that estimates step count in a coarse-to-fine manner using diverse motion representations.

Specifically, an initial step count is estimated from foot movement signals derived from 3D human mesh recovery, providing high-level motion structures.

To incorporate fine-grained motion details, a motion encoder learns complementary gait dynamics from mesh and optical flow to refine the initial estimate.

In this process, coarse foot movement signals query the pixel-level motion cues via cross attention to capture subtle parkinsonian gait dynamics.

To handle varying video lengths, we partition each video into clips and integrate clip-wise motion embeddings via multiple instance learning (MIL) for step count residual prediction.

Extensive experiments show our method consistently outperforms existing step counting methods on real-world PD turning datasets.