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In affect-aware task adaptation, users' psychological states are recognized with diverse measurements and used to adapt computer-based tasks. User experience with such adaptation improves as the accuracy of psychological state recognition and task adaptation increases. However, it is unclear how user experience is influenced by algorithmic transparency: the degree to which users understand the computer's decision-making process. We thus created an affect-aware task adaptation system with 4 algorithmic transparency levels (none/low/medium/high) and conducted a study where 93 participants first experienced adaptation with no transparency for 16 minutes, then with one of the other 3 levels for 16 minutes. User experience questionnaires and physiological measurements (respiration, skin conductance, heart rate) were analyzed with mixed 2×3 analyses of variance (time × transparency group). Self-reported interest/enjoyment and competence were lower with low transparency than with medium/high transparency, but did not differ between medium and high transparency. The transparency level may also influence participants' respiratory responses to adaptation errors, but this finding is based on ad-hoc t-tests and should be considered preliminary. Overall, results show that the degree of algorithmic transparency does influence self-reported user experience. Since transparency information is relatively easy to provide, it may represent a worthwhile design element in affective computing. 

Physiological sensors are commonly applied for user state monitoring and consequent machine behavior adaptation in applications such as rehabilitation and intelligent cars. While more accurate user state monitoring is known to lead to better user experience, increased accuracy often requires more sensors or more complex sensors. The increased setup time and discomfort involved in the use of such sensors may itself worsen user experience. To examine this effect, we conducted a study where 72 participants interacted with a computer-based multitasking scenario whose difficulty was periodically adapted - ostensibly based on data from either a remote eye tracker or a lab-grade “wet” electroencephalography sensor. Deception was used to ensure consistent difficulty adaptation accuracies, and user experience was measured with the Intrinsic Motivation Inventory, NASA Task Load Index, and an ad-hoc scale. We found few user experience differences between the eye tracker and electroencephalography sensor - while one interaction effect was noted, it was small, and there were no other differences. This result is at first surprising and seems to indicate that comfort and setup time are not major factors for laboratory-based user experience evaluations of such technologies. However, the result is likely due to a suboptimal study protocol where each participant interacted with only one sensor. In future work, we will use an alternate protocol to further explore the effects of user comfort and setup time on user experience. 

Dyadic physiological responses are correlated with the quality of interpersonal processes – for example, the degree of “connectedness” in education and mental health counseling. Pattern recognition algorithms could be applied to such dyadic responses to identify the states of specific dyads, but such pattern recognition has primarily focused on classification. This paper instead uses regression algorithms to estimate three conversation aspects (valence, arousal, balance) from heart rate, skin conductance, respiration, and skin temperature. Data were collected from 35 dyads who engaged in 20 minutes of conversation, divided into 10 two-minute intervals. Each interval was rated with regard to conversation valence, arousal, and balance by an observer. When regression algorithms (support vector machines and Gaussian process regression) were trained on other data from the same dyad, they were able to estimate valence, arousal and balance with lower errors than a simple baseline estimator. However, when algorithms were trained on data from other dyads, errors were not lower than those of the baseline estimator. Overall, results indicate that, as long as training data from the same dyad are available, autonomic nervous system responses can be combined with regression algorithms to estimate multiple dyadic conversation aspects with some accuracy. This has applications in education and mental health counseling, though fundamental issues remain to be addressed before the technology is used in practice. 

Abstract BackgroundA robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome. MethodsAs a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session. ResultsAnalyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice. ConclusionsThough it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.