TY - GEN
T1 - Getting on the Right Foot
T2 - 29th Annual Conference on Intelligent User Interfaces, IUI 2024
AU - Spann, James
AU - Chen, Sarah A.
AU - Ashizawa, Tetsuo
AU - Hoque, Ehsan
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/3/18
Y1 - 2024/3/18
N2 - Currently doctors rely on tools such as the Unified Parkinson's Disease Rating Scale (MDS-UDPRS) and the Scale for the Assessment and Rating of Ataxia (SARA) to make diagnoses for movement disorders based on clinical observations of a patient's motor movement. Observation-based assessments however are inherently subjective and can differ by person. Moreover, different movement disorders show overlapping symptoms, challenging neurologists to make a correct diagnosis based on eyesight alone. In this work, we create an intelligent interface to highlight movements and gestures that are indicative of a movement disorder to observing doctors. First, we analyzed the walking patterns of 43 participants with Parkinson's Disease (PD), 60 participants with ataxia, and 52 participants with no movement disorder to find ten metrics that can be used to distinguish PD from ataxia. Next, we designed an interface that provides context to the gestures that are relevant to a movement disorder diagnosis. Finally, we surveyed two neurologists (one who specializes in PD and the other who specializes in ataxia) on how useful this interface is for making a diagnosis. Our results not only showcase additional metrics that can be used to evaluate movement disorders quantitatively but also outline steps to be taken when designing an interface for these kinds of diagnostic tasks.
AB - Currently doctors rely on tools such as the Unified Parkinson's Disease Rating Scale (MDS-UDPRS) and the Scale for the Assessment and Rating of Ataxia (SARA) to make diagnoses for movement disorders based on clinical observations of a patient's motor movement. Observation-based assessments however are inherently subjective and can differ by person. Moreover, different movement disorders show overlapping symptoms, challenging neurologists to make a correct diagnosis based on eyesight alone. In this work, we create an intelligent interface to highlight movements and gestures that are indicative of a movement disorder to observing doctors. First, we analyzed the walking patterns of 43 participants with Parkinson's Disease (PD), 60 participants with ataxia, and 52 participants with no movement disorder to find ten metrics that can be used to distinguish PD from ataxia. Next, we designed an interface that provides context to the gestures that are relevant to a movement disorder diagnosis. Finally, we surveyed two neurologists (one who specializes in PD and the other who specializes in ataxia) on how useful this interface is for making a diagnosis. Our results not only showcase additional metrics that can be used to evaluate movement disorders quantitatively but also outline steps to be taken when designing an interface for these kinds of diagnostic tasks.
KW - Intelligent visualization
KW - Multi-modal Interfaces
KW - Quantitative methods
UR - http://www.scopus.com/inward/record.url?scp=85190950877&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190950877&partnerID=8YFLogxK
U2 - 10.1145/3640543.3645160
DO - 10.1145/3640543.3645160
M3 - Conference contribution
AN - SCOPUS:85190950877
T3 - ACM International Conference Proceeding Series
SP - 742
EP - 749
BT - Proceedings of 2024 29th Annual Conference on Intelligent User Interfaces, IUI 2024
PB - Association for Computing Machinery
Y2 - 18 March 2024 through 21 March 2024
ER -