Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications.

Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs.

A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.

Background: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors.

Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

Background: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device to measure speech that participants encounter as an indicator of social interaction.

Prompting arm activity after stroke: A clinical proof of concept study of wrist-worn accelerometers with a vibrating alert function.

Background: Frequent practice of functional movements after stroke may optimise motor recovery; however, it is challenging for patients to remember to integrate an impaired limb into daily activities. We report the activity responses of stroke patients receiving a vibrating alert delivered by a tri-axial accelerometer wristband to prompt movement of the impaired arm if hourly activity levels fell.

Methods: Adults with upper limb impairment ≤28 days post-stroke wore the device for four weeks.

Associations between prolonged sedentary time and breaks in sedentary time with cardiometabolic risk in 10-14-year-old children: The HAPPY study.

This study examines the association between prolonged sedentary time and breaks in sedentary time with cardiometabolic risk in 10-14-year-old children. This cross-sectional design study analysed accelerometry-determined sedentary behaviour and physical activity collected over 7 days from 111 (66 girls) UK schoolchildren. Objective outcome measures included waist circumference, fasting lipids, fasting glucose, blood pressure, and cardiorespiratory fitness.

Wristband Accelerometers to motiVate arm Exercise after Stroke (WAVES): study protocol for a pilot randomized controlled trial.

Background: Loss of upper limb function affects up to 85 % of acute stroke patients. Recovery of upper limb function requires regular intensive practise of specific upper limb tasks. To enhance intensity of practice interventions are being developed to encourage patients to undertake self-directed exercise practice.

A study of physical activity comparing people with Charcot-Marie-Tooth disease to normal control subjects.

Purpose: Charcot Marie Tooth disease (CMT) describes a group of hereditary neuropathies that present with distal weakness, wasting and sensory loss. Small studies indicate that people with CMT have reduced daily activity levels. This raises concerns as physical inactivity increases the risk of a range of co- morbidities, an important consideration in the long-term management of this disease.

Unsupervised home monitoring of Parkinson's disease motor symptoms using body-worn accelerometers.

Introduction: Current PD assessment methods have inherent limitations. There is need for an objective method to assist clinical decisions and to facilitate evaluation of treatments. Accelerometers, and analysis using artificial neural networks (ANN), have shown potential as a method of motor symptom evaluation.

Automated video-based assessment of surgical skills for training and evaluation in medical schools.

Purpose: Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead.

Non-alcoholic fatty liver disease is associated with higher levels of measured sedentary behaviour and lower levels of physical activity than matched healthy controls.

Background And Aims: Physical activity is a key determinant of metabolic control and is recommended for people with non-alcoholic fatty liver disease (NAFLD), usually alongside weight loss and dietary change. To date, no studies have reported the relationship between measured sedentary behaviour and physical activity, liver fat and metabolic control in people with NAFLD, limiting the potential to target sedentary behaviour in clinical practice. This study determined the level of sedentary behaviour and physical activity in people with NAFLD, and investigated links between physical activity, liver fat and glucose control.