Sophisticated ubiquitous sensing systems are being used to measure motor ability in clinical settings. Intended to augment clinical decision-making, the interpretability of the machine-learning measurements underneath becomes critical to their use. We explore how visualization can support the interpretability of machine-learning measures through the case of Assess MS, a system to support the clinical assessment of Multiple Sclerosis. A substantial design challenge is to make visible the algorithm's decision-making process in a way that allows clinicians to integrate the algorithm's result into their own decision process. To this end, we present a series of design iterations that probe the challenges in supporting interpretability in a real-world system. The key contribution of this article is to illustrate that simply making visible the algorithmic decision-making process is not helpful in supporting clinicians in their own decision-making process. It disregards that people and algorithms make decisions in different ways. Instead, we propose that visualisation can provide context to algorithmic decision-making, rendering observable a range of internal workings of the algorithm from data quality issues to the web of relationships generated in the machine-learning process.

A growing number of domains, including affect recognition and movement analysis, require a single, real number ground truth label capturing some property of a video clip. We term this the provision of continuum labels. Unfortunately, there is often an uncacceptable trade-off between label consistency and the efficiency of the labelling process with current tools. We present a novel interaction technique, setwise comparison, which leverages the intrinsic human capability for consistent relative judgements and the TrueSkill algorithm to solve this problem. We describe SorTable, a system demonstrating this technique. We conducted a real-world study where clinicians labelled videos of patients with multiple sclerosis for the ASSESS MS computer vision system. In assessing the efficiency-consistency trade-off of setwise versus pairwise comparison, we demonstrated that not only is setwise comparison more efficient, but it also elicits more consistent labels. We further consider how our findings relate to the interactive machine learning literature.

The use of depth-sensing computer vision to capture bodily movement is increasingly being exploited in healthcare. Yet, there are few descriptions of how real-world practices influence the design of such applications. To this end, we present the development and empirical evaluation of ASSESS MS, a system to support the clinical assessment of Multiple Sclerosis using Kinect. A key issue for developing machine-learning based systems is the need for standardized data on which statistical inferences can be made. We demonstrate that there are many aspects of clinical practice that are at odds with the need to capture standardized data for a computer vision system. We offer three design guidelines so address these: 1) Standardization is a multi-disciplinary issue and needs to be addressed early in the development process; 2) Tools that provide a view into what the camera “sees” can support the achievement of standardized data capture in real environments; 3) Tools to support standardized data capture should maintain the agency of human interaction. More broadly we show that when considering every day contexts, the traditional focus on measurement accuracy is only a small part of the effort needed to make a technology “work” in practice.

MOTIVATION: Kinetochores are multiprotein complexes which mediate chromosome attachment to microtubules (MTs) of the mitotic spindle. They regulate MT dynamics during chromosome segregation. Our goal is to identify groups of kinetochore proteins with similar effects on MT dynamics, revealing pathways through which kinetochore proteins transform chemical and mechanical input signals into cues of MT regulation. RESULTS: We have developed a hierarchical, agglomerative clustering algorithm that groups Saccharomyces cerevisiae strains based on MT-mediated chromosome dynamics measured by high-resolution live cell microscopy. Clustering is based on parameters of autoregressive moving average (ARMA) models of the probed dynamics. We have found that the regulation of wildtype MT dynamics varies with cell cycle and temperature, but not with the chromosome an MT is attached to. By clustering the dynamics of mutants, we discovered that the three genes IPL1, DAM1 and KIP3 co-regulate MT dynamics. Our study establishes the clustering of chromosome and MT dynamics by ARMA descriptors as a sensitive framework for the systematic identification of kinetochore protein subcomplexes and pathways for the regulation of MT dynamics. AVAILABILITY: The clustering code, written in Matlab, can be downloaded from http://lccb.scripps.edu. ('download' hyperlink at bottom of website). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.