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.

The combination of gestures, intonations, and textual content plays a key role in argument delivery.However, the current literature mostly considers textual content while assessing the quality of an argument, and is limited to datasets containing short sequences (18-48 words).In this paper, we study argument quality assessment in a multimodal context, and experiment on DBATES, a publicly available dataset of long debate videos.First, we propose a set of interpretable debate-centric features such as clarity, content variation, body movement cues, and pauses, inspired by theories of argumentation quality.Second, we design the Multimodal ARgument Quality assessor (MARQ) -a hierarchical neural network model that summarizes the multimodal signals on long sequences and enriches the multimodal embedding with debate-centric features.Our proposed MARQ model achieves an accuracy of 81.91% on the argument quality prediction task and outperforms established baseline models with an error rate reduction of 22.7%.Through ablation studies, we demonstrate the importance of multimodal cues in modeling argument quality.