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Postural movement of a seated person, as determined by lateral aspect video analysis, can be used to estimate learning-relevant emotions. In this article the motion of a person interacting with a computer is automatically extracted from a video by detecting the position of motion-tracking markers on the person's body. The detection is done by detecting candidate areas for marker with a Convolutional Neural Network and the correct candidate areas are found by template matching. Several markers are detected in more than 99 % of the video frames while one is detected in only approximate to 80,2 % of the frames. The template matching can also detect the correct template in approximate to 80 of the frames. This means that almost always when the correct candidates are extracted, the template matching is successful. Suggestions for how the performance can be improved are given along with possible use of the marker positions for estimating sagittal plane motion.

Background: Spelling errors in documents lead to reduced trustworthiness, but the mechanism for weighing the psychological assessment (i.e., integrative versus dichotomous) has not been elucidated. We instructed participants to rate content of texts, revealing that their implicit trustworthiness judgments show marginal differences specifically caused by spelling errors. Methods: An online experiment with 100 English-speaking participants were asked to rate 27 short text excerpts (∼100 words) about multiple sclerosis in the format of unmoderated health forum posts. In a counterbalanced design, some excerpts had no typographic errors, some had two errors, and some had five errors. Each participant rated nine paragraphs with a counterbalanced mixture of zero, two or five errors. A linear mixed effects model (LME) was assessed with error number as a fixed effect and participants as a random effect. Results: Using an unnumbered scale with anchors of "completely untrustworthy" (left) and "completely trustworthy" (right) recorded as 0 to 100, two spelling errors resulted in a penalty to trustworthiness of 5.91 ± 1.70 (robust standard error) compared to the reference excerpts with zero errors, while the penalty for five errors was 13.5 ± 2.47; all three conditions were significantly different from each other (P < 0.001). Conclusion: Participants who rated information about multiple sclerosis in a context mimicking an online health forum implicitly assigned typographic errors nearly linearly additive trustworthiness penalties. This contravenes any dichotomous heuristic or local ceiling effect on trustworthiness penalties for these numbers of typographic errors. It supports an integrative model for psychological judgments of trustworthiness.

[This corrects the article DOI: 10.2196/15171.].

Introduction: Inertial sensors generate objective and sensitive metrics of movement disability that may indicate fall risk in many clinical conditions including multiple sclerosis (MS). The Timed-Up-And-Go (TUG) task is used to assess patient mobility because it incorporates clinically-relevant submovements during standing. Most sensor-based TUG research has focused on the placement of sensors at the spine, hip or ankles; an examination of thigh activity in TUG in multiple sclerosis is wanting. Methods: We used validated sensors (x-IMU by x-io) to derive transparent metrics for the sit-to-stand (SI-ST) transition and the stand-to-sit (ST-SI) transition of TUG, and compared effect sizes for metrics from inertial sensors on the thighs to effect sizes for metrics from a sensor placed at the L3 level of the lumbar spine. Twenty-three healthy volunteers were compared to 17 ambulatory persons with MS (PwMS, HAI = 2). Results: During the SI-ST transition, the metric with the largest effect size comparing healthy volunteers to PwMS was the Area Under the Curve of the thigh angular velocity in the pitch direction-representing both thigh and knee extension; the peak of the spine pitch angular velocity during SI-ST also had a large effect size, as did some temporal measures of duration of SI-ST, although less so. During the ST-SI transition the metric with the largest effect size in PwMS was the peak of the spine angular velocity curve in the roll direction. A regression was performed. Discussion: We propose for PwMS that the diminished peak angular velocity during SI-ST directly represents extensor weakness, while the increased roll during ST-SI represents diminished postural control. Conclusions: During the SI-ST transition of TUG, angular velocities can discriminate between healthy volunteers and ambulatory PwMS better than temporal features. Sensor placement on the thighs provides additional discrimination compared to sensor placement at the lumbar spine.