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In affective computing, classification algorithms are used to recognize users’ psychological states and adapt tasks to optimize user experience. However, classification is never perfect, and the relationship between adaptation accuracy and user experience remains understudied. It is also unclear whether the adaptation magnitude (‘size’ of action taken to influence user states) influences effects of adaptation accuracy. To evaluate impacts of adaptation accuracy (appropriate vs. inappropriate actions) and magnitude on user experience, we conducted a ‘Wizard of Oz’ study where 112 participants interacted with the Multi-Attribute Task Battery over three 11-minute intervals. An adaptation accuracy (50 % to 80 %) was preassigned for the first 11-minute interval, and accuracy increased by 10 % in each subsequent interval. Task difficulty changed every minute, and participant preferences for difficulty changes were assessed at the same time. Adaptation accuracy was artificially induced by fixing the percentage of times the difficulty changes matched participant preferences. Participants were also randomized to two magnitude conditions, with difficulty modified by 1 (low) or 3 (high) levels each minute. User experience metrics were assessed after each interval. Analysis with latent growth models offered support for linear increases in user experience across increasing levels of adaptation accuracy. For each 10 % gain in accuracy, results indicate a 1.3 (95 % CI [.35, 2.20]) point increase in NASA Task Load Index scores (range 6–60), a 0.40 (95 % CI [.18, 0.57]) increase in effort/importance (range 2–14), and 0.48 (95 % CI [.24, 0.72]) increase in perceived competence (range 2–14). Furthermore, the effect of accuracy on Task Load Index scores was modulated by adaptation magnitude. No effects were observed for interest/enjoyment or pressure/tension. By providing quantitative estimates of effects of adaptation accuracy on user experience, the study provides guidelines for researchers and developers of affect-aware technologies. Furthermore, our methods could be adapted for use in other affective computing scenarios. 

Trunk exoskeletons are wearable devices that support wearers during physically demanding tasks by reducing biomechanical loads and increasing stability. In this paper, we present a prototype sensorized passive trunk exoskeleton, which includes five motion processing units (3-axis accelerometers and gyroscopes with onboard digital processing), four one-axis flex sensors along the exoskeletal spinal column, and two one-axis force sensors for measuring the interaction force between the wearer and exoskeleton. A pilot evaluation of the exoskeleton was conducted with two wearers, who performed multiple everyday tasks (sitting on a chair and standing up, walking in a straight line, picking up a box with a straight back, picking up a box with a bent back, bending forward while standing, bending laterally while standing) while wearing the exoskeleton. Illustrative examples of the results are presented as graphs. Finally, potential applications of the sensorized exoskeleton as the basis for a semi-active exoskeleton design or for audio/haptic feedback to guide the wearer are discussed.