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Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process.

Robots based on simplified or abstracted biomechanical concepts can be a useful tool for investigating how and why animals move the way they do. In this paper we present an extremely simple quadruped robot, which is able to walk with no form of software or controller. Instead, individual leg movements are triggered directly by switches on each leg which detect leg loading and unloading. As the robot progresses, pitching and rolling movements of its body result in a gait emerging with a consistent leg movement order, despite variations in stride and stance time. This gait has similarities to the gaits used by walking primates and grazing livestock, and is close to the gait which was recently theorised to derive from animal body geometry. As well as presenting the design and construction of the robot, we present experimental measurements of the robot’s gait kinematics and ground reaction forces determined using high speed video and a pressure mat, and compare these to gait parameters of animals taken from literature. Our results support the theory that body geometry is a key determinant of animal gait at low speeds, and also demonstrate that steady state locomotion can be achieved with little to no active control.

Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO2), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospital ward, the main challenges in the development of continuous and robust non-contact monitoring techniques are the identification of time periods and the segmentation of skin regions of interest (ROIs) from which vital signs can be estimated. We propose a deep learning framework to tackle these challenges. Approach: This paper presents two convolutional neural network (CNN) models. The first network was designed for detecting the presence of a patient and segmenting the patient’s skin area. The second network combined the output from the first network with optical flow for identifying time periods of clinical intervention so that these periods can be excluded from the estimation of vital signs. Both networks were trained using video recordings from a clinical study involving 15 pre-term infants conducted in the high dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford, UK. Main results: Our proposed methods achieved an accuracy of 98.8% for patient detection, a mean intersection-over-union (IOU) score of 88.6% for skin segmentation and an accuracy of 94.5% for clinical intervention detection using two-fold cross validation. Our deep learning models produced accurate results and were robust to different skin tones, changes in light conditions, pose variations and different clinical interventions by medical staff and family visitors. Significance: Our approach allows cardio-respiratory signals to be continuously derived from the patient’s skin during which the patient is present and no clinical intervention is undertaken.

Journal of biomedical optics 24(7), 070601 (2019). doi:10.1117/1.JBO.24.7.070601, Published by SPIE, Bellingham, Wash.

OBJECTIVES: To evaluate the reliability and validity of the Short Parkinson's Evaluation Scale (SPES)/SCales for Outcomes in Parkinson's disease (SCOPA)-a short scale developed to assess motor function in patients with Parkinson's disease (PD). METHODS: Eighty five patients with PD were assessed with the SPES/SCOPA, Unified Parkinson's Disease Rating Scale (UPDRS), Hoehn and Yahr (H&Y) scale, and Schwab and England (S&E) scale. Thirty four patients were examined twice by two different assessors who were blinded to each other's scores and test executions. Additionally, six items of the motor section of the SPES/SCOPA were assessed in nine patients and recorded on videotape to evaluate inter-rater and intra-rater reliability. RESULTS: The reproducibility of the sum scores in the clinical assessments was high for all subscales of the SPES/SCOPA. Inter-rater reliability coefficients for individual items ranged from 0.27-0.83 in the motor impairment section, from 0.58-0.82 in the activities of daily living section, and from 0.65-0.92 in the motor complications section. Inter-rater reliability of the motor items in the video assessments ranged from 0.70-0.87 and intra-rater reliability ranged from 0.81-0.95. The correlation between related subscales of the SPES/SCOPA and UPDRS were all higher than 0.85, and both scales revealed similar correlations with other measures of disease severity. The mean time to complete the scales differed significantly (p