KINECAL: A Dataset for Falls-Risk Assessment and Balance Impairment Analysis.

The field of human action recognition has made great strides in recent years, much helped by the availability of a wide variety of datasets that use Kinect to record human movement. Conversely, progress towards the use of Kinect in clinical practice has been hampered by the lack of appropriate data. In particular, datasets that contain clinically significant movements and appropriate metadata. This paper proposes a dataset to address this issue, namely KINECAL. It contains the recordings of 90 individuals carrying out 11 movements, commonly used in the clinical assessment of balance. The dataset contains relevant metadata, including clinical labelling, falls history labelling and postural sway metrics. KINECAL should be of interest to researchers interested in the clinical use of motion capture and motion analysis.

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

A new process to measure postural sway using a Kinect depth camera during a Sensory Organisation Test.

Posturography provides quantitative, objective measurements of human balance and postural control for research and clinical use. However, it usually requires access to specialist equipment to measure ground reaction forces, which are not widely available in practice, due to their size or cost. In this study, we propose an alternative approach to posturography. It uses the skeletal output of an inexpensive Kinect depth camera to localise the Centre of Mass (CoM) of an upright individual. We demonstrate a pipeline which is able to measure postural sway directly from CoM trajectories, obtained from tracking the relative position of three key joints. In addition, we present the results of a pilot study that compares this method of measuring postural sway to the output of a NeuroCom SMART Balance Master. 15 healthy individuals (age: 42.3 ± 20.4 yrs, height: 172 ± 11 cm, weight: 75.1 ± 14.2 kg, male = 11), completed 25 Sensory Organisation Test (SOT) on a NeuroCom SMART Balance Master. Simultaneously, the sessions were recorded using custom software developed for this study (CoM path recorder). Postural sway was calculated from the output of both methods and the level of agreement determined, using Bland-Altman plots. Good agreement was found for eyes open tasks with a firm support, the agreement decreased as the SOT tasks became more challenging. The reasons for this discrepancy may lie in the different approaches that each method takes to calculate CoM. This discrepancy warrants further study with a larger cohort, including fall-prone individuals, cross-referenced with a marker-based system. However, this pilot study lays the foundation for the development of a portable device, which could be used to assess postural control, more cost-effectively than existing equipment.