Casey C. Bennett

A Robot of My Own: Participatory Design of Socially Assistive Robots for Independently Living Older Adults Diagnosed with Depression

This paper presents an ongoing project using participatory design methods to develop design concepts for socially assistive robots SARs with older adults diagnosed with depression and co-occurring physical illness. We frame SARs development in the context of preventive patient-centered healthcare, which empowers patients as the primary drivers of health and aims to delay the onset of disease rather than focusing on treatment. After describing how SARs can be of benefit in this form of healthcare, we detail our participatory design study with older adults and therapists aimed at developing preventive SARs applications for this population. We found therapists and older adults to be willing and able to participate in assistive robot design, though hands-on participation was a challenge. Our findings suggest that important areas of concern for older adults with depression are social interaction and companionship, as well as technologies that are easy to use and require minimal intervention.

Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment

The CDOI outcome measure -- a patient-reported outcome (PRO) instrument utilizing direct client feedback -- was implemented in a large, real-world behavioral healthcare setting in order to evaluate previous findings from smaller controlled studies. PROs provide an alternative window into treatment effectiveness based on client perception and facilitate detection of problems/symptoms for which there is no discernible measure (e.g. pain). The principal focus of the study was to evaluate the utility of the CDOI for predictive modeling of outcomes in a live clinical setting. Implementation factors were also addressed within the framework of the Theory of Planned Behavior by linking adoption rates to implementation practices and clinician perceptions. The results showed that the CDOI does contain significant capacity to predict outcome delta over time based on baseline and early change scores in a large, real-world clinical setting, as suggested in previous research. The implementation analysis revealed a number of critical factors affecting successful implementation and adoption of the CDOI outcome measure, though there was a notable disconnect between clinician intentions and actual behavior. Most importantly, the predictive capacity of the CDOI underscores the utility of direct client feedback measures such as PROs and their potential use as the basis for next generation clinical decision support tools and personalized treatment approaches.

Perceptions of affective expression in a minimalist robotic face

This study explores deriving minimal features for a robotic face to convey information (via facial expressions) that people can perceive/understand. Recent research in computer vision has shown that a small number of moving points/lines can be used to capture the majority of information (~95%) in human facial expressions. Here, we apply such findings to a minimalist robot face; recognition rates were similar to more complex robots. The project aims to answer a number of fundamental questions about robotic face design, as well as to develop inexpensive/replicable robotic faces for experimental purposes.

Expert Systems in Mental Health Care: AI Applications in Decision-Making and Consultation

Artificial intelligence (AI) based tools hold potential to extend the current capabilities of clinicians, to deal with complex problems and ever-expanding information streams that stretch the limits of human ability. In contrast to previous generations of AI and expert systems, these approaches are increasingly dynamical and less computationalist – less about “rules” and more about leveraging the dynamic interplay of action and observation over time. The (treatment) choices we make change what we observe (clinically, or otherwise), which changes future choices, which affects future observations, and so forth. As humans (clinicians or otherwise), we leverage this fact every day to act “intelligently” in our environment. To best assist us, our clinical computing tools should approximate the same process. Such an approach ties to future developments across the broader healthcare space, e.g., cognitive computing, smart homes, and robotics.Artificial intelligence (AI) based tools hold potential to extend the current capabilities of clinicians, to deal with complex problems and ever-expanding information streams that stretch the limits of human ability. In contrast to previous generations of AI and expert systems, these approaches are increasingly dynamical and less computationalist – less about “rules” and more about leveraging the dynamic interplay of action and observation over time. The (treatment) choices we make change what we observe (clinically, or otherwise), which changes future choices, which affects future observations, and so forth. As humans (clinicians or otherwise), we leverage this fact every day to act “intelligently” in our environment. To best assist us, our clinical computing tools should approximate the same process. Such an approach ties to future developments across the broader healthcare space, e.g., cognitive computing, smart homes, and robotics.

Context congruency and robotic facial expressions: Do effects on human perceptions vary across culture?

We performed an experimental study (n=48) of the effects of context congruency on human perceptions of robotic facial expressions across cultures (Western and East Asian individuals). We found that context congruency had a significant effect on human perceptions, and that this effect varied by the emotional valence of the context and facial expression. Moreover, these effects occurred regardless of the cultural background of the participants. In short, there were predictable patterns in the effects of congruent/incongruent environmental context on perceptions of robot affect across Western and East Asian individuals. We argue that these findings fit with a dynamical systems view of social cognition as an emergent phenomenon. Taking advantage of such context effects may ease the constraints for developing culturally-specific affective cues in human-robot interaction, opening the possibility to create culture-neutral models of robots and affective interaction.

Investigation of Ancient DNA from Western Siberia and the Sargat Culture

Abstract Mitochondrial DNA from 14 archaeological samples at the Ural State University in Yekaterinburg, Russia, was extracted to test the feasibility of ancient DNA work on their collection. These samples come from a number of sites that fall into two groupings. Seven samples are from three sites, dating to the 8th–12th century AD, that belong to a northern group of what are thought to be Ugrians, who lived along the Ural Mountains in northwestern Siberia. The remaining seven samples are from two sites that belong to a southern group representing the Sargat culture, dating between roughly the 5th century BC and the 5th century AD, from southwestern Siberia near the Ural Mountains and the present-day Kazakhstan border. The samples are derived from several burial types, including kurgan burials. They also represent a number of different skeletal elements and a range of observed preservation. The northern sites repeatedly failed to amplify after multiple extraction and amplification attempts, but the samples from the southern sites were successfully extracted and amplified. The sequences obtained from the southern sites support the hypothesis that the Sargat culture was a potential zone of intermixture between native Ugrian and/or Siberian populations and steppe peoples from the south, possibly early Iranian or Indo-Iranian, which has been previously suggested by archaeological analysis.

Reanalysis of Eurasian Population History: Ancient DNA Evidence of Population Affinities

ABSTRACT Mitochondrial hypervariable region I genetic data from ancient populations at two sites in Asia—Linzi in Shandong (northern China) and Egyin Gol in Mongolia—were reanalyzed to detect population affinities. Data from 51 modern populations were used to generate distance measures (FSTs) to the two ancient populations. The tests first analyzed relationships at the regional level and then compiled the top regional matches for an overall comparison to the two probe populations. The reanalysis showed that the Egyin Gol and Linzi populations have clear distinctions in genetic affinity. The Egyin Gol population as a whole appears to bear close affinities with modern populations of northern East Asia. The Linzi population seems to have some genetic affinities with the West, as suggested by the original analysis, although the original attribution of “European-like” seems to be misleading. We suggest that the Linzi individuals are potentially related to early Iranians, who are thought to have been widespread in parts of Central Eurasia and the steppe regions in the first millennium b.c., although some significant admixture between a number of populations of varying origin cannot be ruled out. We also examine the effect of sequence length on this type of genetic data analysis and discuss the results of previous studies on the Linzi sample.

MiRAE: My Inner Voice

This video presents the interactions between MiRAE, an interactive robotic face, and visitors to an art exhibition at which it was displayed. The robot operated eight hours a day, six days a week, for three weeks in Spring 2014 and interacted with over 700 people across 300 interactions. The robot was fully autonomous and researchers were not present on site during the exhibit, so people interacted in a free-form manner, both individually and in groups. During the exhibit, video recordings were taken of peoples responses to the robot. This video depicts a series of resulting interactions, with MiRAEs interpretation of the events.