Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body is a time consuming and labour intensive postprocessing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework

The feasibility of using pattern recognition software to measure the influence of computer use on the consultation

BACKGROUND: A key feature of a good general practice consultation is that it is patient-centred. A number of verbal and non-verbal behaviours have been identified as important to establish a good relationship with the patient. However, the use of the computer detracts the doctor's attention away from the patient, compromising these essential elements of the consultation. Current methods to assess the consultation and the influence of the computer on them are time consuming and subjective. If it were possible to measure these quantitatively, it could provide the basis for the first truly objective way of studying the influence of the computer on the consultation. The aim was to assess whether pattern recognition software could be used to measure the influence and pattern of computer use in the consultation. If this proved possible it would provide, for the first time, an objective quantitative measure of computer use and a measure of the attention and responsiveness of the general practitioner towards the patient. METHODS: A feasibility study using pattern recognition software to analyse a consultation was conducted. A web camera, linked to a data-gathering node was used to film a simulated consultation in a standard office. Members of the research team enacted the role of the doctor and the patient, using pattern recognition software to try and capture patient-centred, non-verbal behaviour. As this was a feasibility study detailed results of the analysis are not presented. RESULTS: It was revealed that pattern recognition software could be used to analyse certain aspects of a simulated consultation. For example, trigger lines enabled the number of times the clinician's hand covered the keyboard to be counted and wrapping recorded the number of times the clinician nodded his head. It was also possible to measure time sequences and whether the movement was brief or lingering. CONCLUSION: Pattern recognition software enables movements associated with patient-centredness to be recorded. Pattern recognition software has the potential to provide an objective, quantitative measure of the influence of the computer on the consultation

The effect of posture, pressure and load distribution on (dis)comfort perceived by students seated on school chairs

The study of ergonomics and (dis)comfort is a major topic of interest to scientific literature since the introduction of ISO 11228. Over the past 30 years, researchers have attempted to understand the mechanisms underlying perceptions of (dis)comfort for a seated subject. Three main factors are recognized as paramount for the comfort performance of a seat: human body posture, pressure at interface, and load distribution on the contact area; this study investigates the relationships between them and examines their significance for (dis)comfort perception. Physiological factors are known to play a role in (dis)comfort perception, and we consider these as a consequence of the three main parameters above. An explanation is given for this hypothesis. Experimental tests were conducted using a school chair with a rigid seat pan and no armrests for reducing the number of factors of influence. Conclusions show the levels of correlation between acquired information and perceived (dis)comfort