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1. Human-Delivered Brushstroke Characterization Using an Instrumented Brush Focused on Torque

   https://doi.org/10.1109/WHC56415.2023.10224489  

   Landsman, Zack T.; Kao, Anika R.; Gerling, Gregory J. (July 2023, IEEE World Haptics Conference)

   Pleasant brush therapies may benefit those with autism, trauma, and anxiety. While studies monitor brushing velocity, hand-delivery of brush strokes introduces variability. Detailed measurements of human-delivered brushing physics may help under-stand such variability and subsequent impact on receivers’ perceived pleasantness. Herein, we instrument a brush with multi-axis force and displacement sensors to measure their physics as 12 participants pleasantly stroke a receiver’s forearm. Algorithmic procedures identify skin contact, and define four stages of arrival, stroke, departure, and airtime between strokes. Torque magnitude, rather than force, is evaluated as a metric to minimize inertial noise, as it registers brush bend and orientation. Overall, the results of the naturally delivered brushing experiments indicate force and velocity values in the range of 0.4 N and 3-10 cm/s, in alignment with prior work. However, we observe significant variance between brushers across velocity, force, torque, and brushstroke length. Upon further analysis, torque and force measures are correlated, yet torque provides distinct information from velocity. In evaluating the receiver’s response to individual differences between brushers of the preliminary case study, higher pleasantness is tied to lower mean torque, and lower instantaneous variance over the stroke duration. Torque magnitude appears to complement velocity’s influence on perceived pleasant-ness. 

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2. Inception of subsequent stepped leaders in negative cloud-to-ground lightning

   https://doi.org/10.1007/s00703-019-00702-8  

   Stolzenburg, Maribeth; Marshall, Thomas C.; Karunarathne, Sumedhe (August 2020, Meteorology and Atmospheric Physics)

   null (Ed.)

   Abstract Time-correlated high-speed video and electric field change data for 139 natural, negative cloud-to-ground (CG)-lightning flashes reveal 615 return strokes (RSs) and 29 upward-illumination (UI)-type strokes. Among 121 multi-stroke flashes, 56% visibly connected to more than one ground location for either a RS or UI-type stroke. The number of separate ground-stroke connection locations per CG flash averaged 1.74, with maximum 6. This study examines the 88 subsequent strokes that involved a subsequent stepped leader (SSL), either reaching ground or intercepting a former leader to ground, in 61 flashes. Two basic modes by which these SSLs begin are described and are termed dart - then - stepped leaders herein. One inception mode occurs when a dart leader deflects from the prior main channel and begins propagating as a stepped leader to ground. In these ‘divert’ mode cases, the relevant interstroke time from the prior RS in the channel to the SSL inception from that path is long, ranging from 105 to 204 ms in four visible cases. The alternative mode of SSL inception occurs when a dart leader reaches the end of a prior unsuccessful branch—of an earlier competing dart leader, stepped leader, or initial leader—then begins advancing as a stepped leader toward ground. In this more common ‘branch’ mode (85% of visible cases), there may be no portion of the subsequent RS channel that is shared with a prior RS channel. These two inception modes, and variations among them, can occur in different subsequent strokes of the same flash. 

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3. Derivation and Validation of an Algorithm to Detect Stroke Using Arm Accelerometry Data

   https://doi.org/10.1161/JAHA.122.028819  

   Messé, Steven R.; Kasner, Scott E.; Cucchiara, Brett L.; McGarvey, Michael L.; Cummings, Stephanie; Acker, Michael A.; Desai, Nimesh; Atluri, Pavan; Wang, Grace J.; Jackson, Benjamin M.; et al (February 2023, Journal of the American Heart Association)

   Background Early diagnosis is essential for effective stroke therapy. Strokes in hospitalized patients are associated with worse outcomes compared with strokes in the community. We derived and validated an algorithm to identify strokes by monitoring upper limb movements in hospitalized patients. Methods and Results A prospective case–control study in hospitalized patients evaluated bilateral arm accelerometry from patients with acute stroke with lateralized weakness and controls without stroke. We derived a stroke classifier algorithm from 123 controls and 77 acute stroke cases and then validated the performance in a separate cohort of 167 controls and 33 acute strokes, measuring false alarm rates in nonstroke controls and time to detection in stroke cases. Faster detection time was associated with more false alarms. With a median false alarm rate among nonstroke controls of 3.6 (interquartile range [IQR], 2.1–5.0) alarms per patient per day, the median time to detection was 15.0 (IQR, 8.0–73.5) minutes. A median false alarm rate of 1.1 (IQR. 0–2.2) per patient per day was associated with a median time to stroke detection of 29.0 (IQR, 11.0–58.0) minutes. There were no differences in algorithm performance for subgroups dichotomized by age, sex, race, handedness, nondominant hemisphere involvement, intensive care unit versus ward, or daytime versus nighttime. Conclusions Arm movement data can be used to detect asymmetry indicative of stroke in hospitalized patients with a low false alarm rate. Additional studies are needed to demonstrate clinical usefulness. 

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4. Sound out the impaired perfusion: Photoacoustic imaging in preclinical ischemic stroke

   https://doi.org/10.3389/fnins.2022.1055552  

   Menozzi, Luca; Yang, Wei; Feng, Wuwei; Yao, Junjie (December 2022, Frontiers in Neuroscience)

   Acoustically detecting the optical absorption contrast, photoacoustic imaging (PAI) is a highly versatile imaging modality that can provide anatomical, functional, molecular, and metabolic information of biological tissues. PAI is highly scalable and can probe the same biological process at various length scales ranging from single cells (microscopic) to the whole organ (macroscopic). Using hemoglobin as the endogenous contrast, PAI is capable of label-free imaging of blood vessels in the brain and mapping hemodynamic functions such as blood oxygenation and blood flow. These imaging merits make PAI a great tool for studying ischemic stroke, particularly for probing into hemodynamic changes and impaired cerebral blood perfusion as a consequence of stroke. In this narrative review, we aim to summarize the scientific progresses in the past decade by using PAI to monitor cerebral blood vessel impairment and restoration after ischemic stroke, mostly in the preclinical setting. We also outline and discuss the major technological barriers and challenges that need to be overcome so that PAI can play a more significant role in preclinical stroke research, and more importantly, accelerate its translation to be a useful clinical diagnosis and management tool for human strokes. 

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5. Self-Annotation Methods for Aligning Implicit and Explicit Human Feedback in Human-Robot Interaction

   https://doi.org/10.1145/3568162.3576986  

   Zhang, Qiping; Narcomey, Austin; Candon, Kate; Vázquez, Marynel (March 2023, HRI '23: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction)

   Recent research in robot learning suggests that implicit human feedback is a low-cost approach to improving robot behavior without the typical teaching burden on users. Because implicit feedback can be difficult to interpret, though, we study different methods to collect fine-grained labels from users about robot performance across multiple dimensions, which can then serve to map implicit human feedback to performance values. In particular, we focused on understanding the effects of annotation order and frequency on human perceptions of the self-annotation process and the usefulness of the labels for creating data-driven models to reason about implicit feedback. Our results demonstrate that different annotation methods can influence perceived memory burden, annotation difficulty, and overall annotation time. Based on our findings, we conclude with recommendations to create future implicit feedback datasets in Human-Robot Interaction. 

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