Author:
Fokas Emily E.,Ahmed Zuha,Parnandi Avinash R.,Venkatesan Anita,Pandit Natasha G.,Nilsen Dawn M.,Schambra Heidi M.
Abstract
AbstractUpper-body dressing (UBD) is a key aspect of motor rehabilitation after stroke, but most individuals with stroke require long-term dressing assistance. Having a measurement approach that captures the quantity and quality of dressing movements during training could support more targeted strategies. As the basis of an approach, we modified our previously developed motion taxonomy, which categorizes elemental motions into classes of functional primitives (e.g.reaches, transports, stabilizations). Three expert coders examined videos of two healthy subjects performing dressing tasks, and expanded the taxonomy to account for the unique arm and trunk motions of UBD. An expert and a trained coder then applied the expanded taxonomy to dressing videos of five chronic stroke subjects. We examined the interrater reliability (IRR) for classifying primitives. Using the expanded taxonomy, IRR for identifying primitives in UBD was overall low (k = 0.52) but varied by primitive class: IRR was moderate forreach(k = 0.75),transport(k = 0.63), andidle(k = 0.68), lower forreposition(k = 0.58), and negligible forstabilization(k = -0.02). IRR increased with increasing UE-FMA score (ρ=1, p<0.0001), indicating that the reliability of primitive classification improved with less impaired movement. With additional modification, the expanded taxonomy could support the measurement of training doses and impaired motion during dressing activities.
Publisher
Cold Spring Harbor Laboratory
Reference10 articles.
1. The recovery of perceptual problems after stroke and the impact on daily life
2. A method for evaluation of physical performance;Scandinavian journal of rehabilitation medicine,1975
3. Interrater reliability: the kappa statistic;Biochemia medica,2012
4. Parnandi, A. , Kaku, A. , Venkatesan, A. , Pandit, N. , Fokas, E. , Yu, B. , … Schambra, H. (2023). Data-driven quantitation of movement abnormality after stroke. Bioengineering, 10(6).
5. Parnandi, A. , Kaku, A. , Venkatesan, A. , Pandit, N. , Wirtanen, A. , Rajamohan, H. , … Schambra, H. (2022). PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training. PLOS digital health, 1(6).