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Running biomechanics have traditionally been analysed in laboratory settings, but this may not reflect natural running gait. Wearable technology has the potential to enable precise monitoring of running gait beyond the laboratory. This study aimed to evaluate the analytical validity and intra-session reliability of temporal running gait outcomes measured by the ViMove2 wearable system in healthy adults. Seventy-four healthy adults (43 males, 31 females, aged 18–55 years) wore the inertial device, ViMove2 on the tibia. Participants ran on a treadmill for one minute at various speeds (8, 10, 12, 14km/hr), completed in a standardised shoe (Saucony Guide Runner). Running gait was measured with the ViMove2 wearable and 3D motion capture (Vicon). Temporal running gait outcomes included ground contact time (GCT) and cadence (steps/min). GCT and cadence from the ViMove2 had face validity with expected changes in outcome with different running speeds, but ViMove2 tended to over-estimate GCT, and under-estimate cadence compared to the reference, especially at slower speeds. GCT demonstrated moderate to good agreement to the reference at speeds >10km/hr, but poor agreement at 8km/hr and within female runners. Cadence had moderate to excellent agreement across speeds compared to the reference. GCT and cadence had excellent reliability across speeds, but at 8km/hr GCT had good agreement between trials. Overall, temporal gait outcomes of GCT and cadence can be measured with the ViMove2, but accuracy and reliability are impacted at slow running speeds and within female runners. Future work is needed to clarify sex or speed-dependent corrections to algorithms / outcomes to aid interpretation and application. [ABSTRACT FROM AUTHOR]

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Background: Eye-tracking technology could be used to study human factors during teamwork. Objectives: This work aimed to compare the visual attention (VA) of a team member acting as both a team leader and managing the airway, compared to a team member performing the focused task of managing the airway in the presence of a dedicated team leader. This work also aimed to report differences in team performance, behavioural skills, and workload between the two groups using validated tools. Methods: We conducted a simulation-based, pilot randomised controlled study. The participants included were volunteer paediatric trainees, nurse practitioners, and neonatal nurses. Three teams consisting of four team members were formed. Each team participated in two identical neonatal resuscitation simulation scenarios in a random order, once with and once without a team leader. Using a commercially available eye-tracking device, we analysed VA regarding attention to (1) a manikin, (2) a colleague, and (3) a monitor. Only the trainee who was the airway operator would wear eye-tracking glasses in both simulations. Results: In total, 6 simulation scenarios and 24 individual role allocations were analysed. Participants in a no-team-leader capacity had a greater number of total fixations on manikin and monitors, though this was not significant. There were no significant differences in team performance, behavioural skills, and individual workload. Physical demand was reported as significantly higher by participants in the group without a team leader. During debriefing, all the teams expressed their preference for having a dedicated team leader. Conclusion: In our pilot study using low-cost technology, we could not demonstrate the difference in VA with the presence of a team leader. [ABSTRACT FROM AUTHOR]

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use. [ABSTRACT FROM AUTHOR]

The present study was undertaken to understand the prevalence of child sexual abuse and its association with the wellbeing of a sample of LGBTQ + individuals in India. A total of 206 respondents took part in the study. The WHO-5 wellbeing scale developed by the World Health Organization was used to assess the wellbeing of the respondents. The prevalence of child sexual abuse among the respondents was high (46.1 per cent). In general, the level of wellbeing of the respondents was found to be below average. Among those who had experienced child sexual abuse, the majority of the respondents were cisgender women. Cisgender women respondents were also more likely to have a much lower mean rank of wellbeing compared to the other groups and similarly, respondents who identified as lesbians were also found to have a lower level of wellbeing compared to respondents who identified themselves as bisexual, pansexual, and gay. Other associated factors have been discussed in the full paper. The role of family in supporting and helping the individuals heal, has also been discussed in detail. There is a need for families to be more empathetic toward the victims. [ABSTRACT FROM AUTHOR]

• Free-living inertial-based gait assessment to inform fall risk is limited. • Wearable video glasses could help better inform free-living gait assessment. • AI-based computer video methods could enable automated and ethical video analysis. • Computer vision can provide descriptive information on free-living environments. Contemporary research to better understand free-living fall risk assessment in Parkinson's disease (PD) often relies on the use of wearable inertial-based measurement units (IMUs) to quantify useful temporal and spatial gait characteristics (e.g., step time, step length). Although use of IMUs is useful to understand some intrinsic PD fall-risk factors, their use alone is limited as they do not provide information on extrinsic factors (e.g., obstacles). Here, we update on the use of ergonomic wearable video-based eye-tracking glasses coupled with AI-based computer vision methodologies to provide information efficiently and ethically in free-living home-based environments to better understand IMU-based data in a small group of people with PD. The use of video and AI within PD research can be seen as an evolutionary step to improve methods to understand fall risk more comprehensively. [ABSTRACT FROM AUTHOR]