Karthik Dinesh

TL;DR: Among individuals with movement disorders, the use of wearable sensors in clinic and at home was feasible and well-received, and these sensors can identify statistically significant differences in activity profiles between individuals with movements disorders and those without.

TL;DR: This article surveys the existing and emerging technologies that can enable this vision for the future of healthcare, particularly, in the clinical practice of healthcare and discusses the emerging directions, open issues, and challenges.

TL;DR: A dataset for facilitating audio-visual analysis of music performances that comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks is introduced.

TL;DR: The dataset as mentioned in this paper consists of 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks and provides the musical score in MIDI format, audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground truth annotation files including frame-level and note-level transcriptions.

TL;DR: The concept of deep phenotyping—the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools—for Parkinson’s disease is explored.