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Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes. [ABSTRACT FROM AUTHOR]

Children with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots. [ABSTRACT FROM AUTHOR]

Objective: Continuous glucose monitoring (CGM) has revealed that glycemic variability and low time in range are associated with albuminuria and retinopathy. We have investigated the relationship between glucose metrics derived from CGM and a highly sensitive measure of neuropathy using corneal confocal microscopy in participants with type 1 and type 2 diabetes., Methods: A total of 40 participants with diabetes and 28 healthy controls underwent quantification of corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), corneal nerve fiber length (CNFL) and inferior whorl length (IWL) and those with diabetes underwent CGM for four consecutive days., Results: CNBD was significantly lower in patients with high glycemic variability (GV) compared to low GV (median (range) (25.0 (19.0-37.5) vs 38.6 (29.2-46.9); P = 0.007); in patients who spent >4% compared to <4% time in level 1 hypoglycemia (54-69 mg/dL) (25.0 (22.9-37.5) vs 37.5 (29.2-46.9); P = 0.045) and in patients who spent >1% compared to <1% time in level 2 hypoglycemia (<54 mg/dL) (25.0 (19.8-41.7) vs 35.4 (28.1-44.8); P = 0.04). Duration in level 1 hypoglycemia correlated with CNBD (r = -0.342, P = 0.031). Duration in level 1 (181-250 mg/dL) and level 2 (>250 mg/dL) hyperglycemia did not correlate with CNFD (P > 0.05), CNBD (P > 0.05), CNFL (P > 0.05) or IWL (P > 0.05)., Conclusions: Greater GV and duration in hypoglycemia, rather than hyperglycemia, are associated with nerve fiber loss in diabetes.

Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alhaddad, Aly, Gad, Al-Ali, Sadasivuni, Cabibihan and Malik.)

The data is related to minimal force thresholds perception in robotic surgical grasping applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system that displays grasping tasks by a surgical grasper. The experiments included the display of two presentations at different force levels (i.e., grasping and indentation) in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). For each mode, the participants were asked to identify which of the two presentations was higher. Each experiment was repeated till the termination conditions were met. Sixty participants took part in these experiments. The experiments were randomized and the threshold forces were calculated based on an algorthim. The datasets contain the individual responses of each participant, the threshold forces calculations, and the number of iterations., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2022 The Author(s). Published by Elsevier Inc.)

The data is related to unwanted interactions between a person and a small robotic toy based on acceleration sensor embedded within the robotic toy. Three toys were considered namely, a stuffed panda, a stuffed robot, and an excavator. Each toy was embedded with an accelerometer to record the interactions. Five different unwanted interactions were performed by adult participants and children. The considered interactions were hit, shake, throw, pickup, drop, and idle for the no interaction case. The collected data contains the magnitude of the resultant acceleration from the interactions. The data was processed by extracting the instances of interactions. A secondary dataset was created from the original one by creating artificial sequences. This data article contains the processed data that can be used to explore different machine learning models and techniques in classifying such interactions. Online repository contains the files: https://doi.org/10.7910/DVN/FHOO0Q., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2020 The Author(s).)

In this article, a data generated from impacts of objects with different shapes, masses, and impact velocities on a developed dummy head. The mass considered was in the range of 0.3-0.5 kg while the shapes considered were cube, wedge, and cylinder. The impact velocities levels were in the range of 1-3 m/s. A total of 144 experiments were conducted and the corresponding videos and raw data were analyzed for impact velocity, peak head linear acceleration, 3 ms criterion, and the Head Injury Criterion (HIC). This dataset includes the raw acceleration data and a summary of the overall processed data. The data is available on Harvard Dataverse: https://doi.org/10.7910/DVN/AVC8GG.