Computer‐based teaching and evaluation of introductory statistics for health science students: Some lessons learned

In recent years, it has become possible to introduce health science students to statistical packages at an increasingly early stage in their undergraduate studies. This has enabled teaching to take place in a computer laboratory, using real data, and encouraging an exploratory and research‐oriented approach. This paper briefly describes a hypertext Computer Based Tutorial (CBT) concerned with descriptive statistics and introductory data analysis. The CBT has three primary objectives: the introduction of concepts, the facilitation of revision, and the acquisition of skills for project work. Objective testing is incorporated and used for both self‐assessment and formal examination. Evaluation was carried out with a large group of Health Science students, heterogeneous with regard to their IT skills and basic numeracy. The results of the evaluation contain valuable lessons

Automatic Assessment of the Type and Intensity of Agitated Hand Movements

With increasing numbers of people living with dementia, there is growing interest in the automatic monitoring of agitation. Current assessments rely on carer observations within a framework of behavioural scales. Automatic monitoring of agitation can supplement existing assessments, providing carers and clinicians with a greater understanding of the causes and extent of agitation. Despite agitation frequently manifesting in repetitive hand movements, the automatic assessment of repetitive hand movements remains a sparsely researched field. Monitoring hand movements is problematic due to the subtle differences between different types of hand movements and variations in how they can be carried out; the lack of training data creates additional challenges. This paper proposes a novel approach to assess the type and intensity of repetitive hand movements using skeletal model data derived from video. We introduce a video-based dataset of five repetitive hand movements symptomatic of agitation. Using skeletal keypoint locations extracted from video, we demonstrate a system to recognise repetitive hand movements using discriminative poses. By first learning characteristics of the movement, our system can accurately identify changes in the intensity of repetitive movements. Wide inter-subject variation in agitated behaviours suggests the benefit of personalising the recognition model with some end-user information. Our results suggest that data captured using a single conventional RGB video camera can be used to automatically monitor agitated hand movements of sedentary patients