![]() Alongside formal nursing and caregiving there were approximately 2 million people in the UK receiving informal adult care in 2010, nearly half of which were aged 70 years or older ( Foster et al., 2013). Furthermore, in the 3 years up to 2015 the average spending on agency nursing is estimated to have increased 213%. A 2015 UK report ( Christie and Co, 2015) shows the vacancy rates in the NHS and adult social care are 7 and 9%, respectively. There is also growing concern with the lack of carers and nurses who are available to cope with this increasing demand. Of all the ADLs, dressing showed the highest burden on caregiving staff and the lowest use of assistive technologies, see Figure 1. Of those beneficiaries, 27.3% reported difficulties with walking, 14.4% with transfers (chair/bed), 12% bathing, 8.2% dressing, 6.1% toileting, and 2.9% eating. The long-term care revolution ( LTCR, 2016) is one such agenda about reshaping long-term care in terms of structural and economic changes to deliver quality of life and promote independence for older people ( Tinker et al., 2013), which includes assistance with the activities of daily living (ADLs).Ī cross-sectional study of 14,500 Medicare beneficiaries, nationally representative of the USA ( Dudgeon et al., 2008), looked to quantify the difficulties people had with ADLs. To address growing health and social care needs, government agendas are promoting well-being and independence for older people and carers within communities to help people maintain their independence at home. With an aging population comes an increase in the incidence and prevalence of diseases and disabilities, which will have a profound societal and economic impact. The results show that the load cell could be used independently for this application with good accuracy but a combination of the lower cost sensors could also be used without a significant loss in precision, which will be a key element in the robot control architecture for safe HRI.īetween 20, the proportion of the world’s population of over 60 years is expected to double from about 11 to 22%, and the absolute number of people aged 60 years and older is expected to increase from 605 million to 2 billion over the same period ( WHO, 2016). When observing dressing errors (snagging), Baxter’s sensors and the IMU data demonstrated poor sensitivity but applying machine learning methods resulted in model with high predicative accuracy and low false negative rates (≤5%). Used independently, the IMU and Baxter sensors were insufficient to discriminate garment types with the IMU showing 40–72% accuracy, but when used in combination this pair of sensors achieved an accuracy similar to the more expensive load cell (98%). The 6-axis load cell successfully discriminated between clothing types with predictive model accuracies between 72 and 97%. We expand the analysis to include classification techniques such as decision tree and support vector machines using k-fold cross-validation. Data were analyzed by comparing the overlap of confidence intervals to determine sensitivity to dressing. We also report on suitability of these sensors for identifying dressing errors, e.g., fabric snagging. ![]() In this paper, a Baxter robot was used to dress a jacket onto a mannequin and human participants considering several combinations of user pose and clothing type (base layers), while recording dynamic data from the robot, a load cell, and an IMU. Using the correct force profile for robot control will be essential in this application of HRI requiring careful exploration of factors related to the user’s pose and the type of garments involved. One of the established activities of daily living where robots could play an assistive role is dressing. Successful deployment of robots working in close proximity with people requires consideration of both safety and human–robot interaction (HRI). 2Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Parc Tecnològic de Barcelona, Barcelona, SpainĪssistive robots have a great potential to address issues related to an aging population and an increased demand for caregiving.1Bristol Robotics Laboratory, University of the West of England, Bristol, UK.Praminda Caleb-Solly 1 and Sanja Dogramadzi 1 ![]()
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