How accurately does a simulation glove reflect function compared to rheumatoid arthritis sufferers?

Review of Wearable Sensor-Based Health Monitoring Glove Devices for Rheumatoid Arthritis

Early detection of Rheumatoid Arthritis (RA) and other neurological conditions is vital for effective treatment. Existing methods of detecting RA rely on observation, questionnaires, and physical measurement, each with their own weaknesses. Pharmaceutical medications and procedures aim to reduce the debilitating effect, preventing the progression of the illness and bringing the condition into remission. There is still a great deal of ambiguity around patient diagnosis, as the difficulty of measurement has reduced the importance that joint stiffness plays as an RA identifier. The research areas of medical rehabilitation and clinical assessment indicate high impact applications for wearable sensing devices. As a result, the overall aim of this research is to review current sensor technologies that could be used to measure an individual's RA severity. Other research teams within RA have previously developed objective measuring devices to assess the physical symptoms of hand steadiness through to joint stiffness. Unfamiliar physical effects of these sensory devices restricted their introduction into clinical practice. This paper provides an updated review among the sensor and glove types proposed in the literature to assist with the diagnosis and rehabilitation activities of RA. Consequently, the main goal of this paper is to review contact systems and to outline their potentialities and limitations. Considerable attention has been paid to gloved based devices as they have been extensively researched for medical practice in recent years. Such technologies are reviewed to determine whether they are suitable measuring tools.

Falls are unintentional: Studying simulations is a waste of faking time

Researchers tend to agree that falls are, by definition, unintentional and that sensor algorithms (the processes that allows a computer program to identify a fall among data from sensors) perform poorly when attempting to detect falls 'in the wild' (a phrase some scientists use to mean 'in reality'). Algorithm development has been reliant on simulation, i.e. asking actors to throw themselves intentionally to the ground. This is unusual (no one studies faked coughs or headaches) and uninformative (no one can intend the unintentional). Researchers would increase their chances of detecting 'real' falls in 'the real world' by studying the behaviour of fallers, however, vulnerable, before, during and after the event: the literature on the circumstances of falling is more informative than any number of faked approximations. A complimentary knowledge base (in falls, sensors and/or signals) enables multidisciplinary teams of clinicians, engineers and computer scientists to tackle fall detection and aim for fall prevention. Throughout this paper, I discuss differences between falls, 'intentional falling' and simulations, and the balance between simulation and reality in falls research, finally suggesting ways in which researchers can access examples of falls without resorting to fakery.