Explainable shared control in assistive robotics

Shared control plays a pivotal role in designing assistive robots to complement human capabilities during everyday tasks. However, traditional shared control relies on users forming an accurate mental model of expected robot behaviour. Without this accurate mental image, users may encounter confusion or frustration whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. The Explainable Shared Control paradigm introduced in this thesis attempts to resolve such model misalignment by jointly considering assistance and transparency. There are two perspectives of transparency to Explainable Shared Control: the human's and the robot's. Augmented reality is presented as an integral component that addresses the human viewpoint by visually unveiling the robot's internal mechanisms. Whilst the robot perspective requires an awareness of human "intent", and so a clustering framework composed of a deep generative model is developed for human intention inference. Both transparency constructs are implemented atop a real assistive robotic wheelchair and tested with human users. An augmented reality headset is incorporated into the robotic wheelchair and different interface options are evaluated across two user studies to explore their influence on mental model accuracy. Experimental results indicate that this setup facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment. As for human intention inference, the clustering framework is applied to a dataset collected from users operating the robotic wheelchair. Findings from this experiment demonstrate that the learnt clusters are interpretable and meaningful representations of human intent. This thesis serves as a first step in the interdisciplinary area of Explainable Shared Control. The contributions to shared control, augmented reality and representation learning contained within this thesis are likely to help future research advance the proposed paradigm, and thus bolster the prevalence of assistive robots.Open Acces

Semi-automatic falls risk estimation of elderly adults using single wrist worn accelerometer

PhD ThesisThe population of the oldest old (aged 85 years and over) is growing. It is estimated that 30% of the adults over the age of 65 years experience falls at least once a year. This figure rises to 50% per annum for adults over 80 years living either at home or in care home. Currently older people are the fastest growing segment of the population. In the UK alone, the proportion of people aged 85 years old has increased from 2% to 4% in the past six decades. This marked increase in growth of population aged over 85 years is expected to have substantial impact on overall falls rate and pose serious issues to meet care needs for social and health care departments. In the light of such negative consequences for the faller and the associated costs to society, simple and quantitative techniques for falls risk screening can contribute significantly. This study describes a semi-automated technique to estimate falls risk of community dwelling elderly adults (aged 85 and over). This study presents the detailed analysis of tri-axial accelerometer movement data recorded from the right wrist of individuals undertaking the Timed Up and Go (TUG) test. The semi-automated assessment is evaluated here on 394 subjects’ data collected in their home environment. The study compares logistic regression models developed using accelerometer derived features against the traditional TUG measure ‘time taken to complete the test’. Gender based models were built separately across two groups of participants- with and without walking aid. The accelerometer derived feature model yielded a mean sensitivity of 63.95%, specificity of 63.51% and accuracy of 66.24% based on leave one-out cross validation compared to manually timed TUG (mean sensitivity of 52.64%, specificity of 45.41% and accuracy of 55.22%). Results show that accelerometer derived models offer improvement over traditional falls assessment. This automated method enables identification of older people at risk of falls residing both at home and in care homes and to monitor intervention effectiveness of falls management

Fall risk in older adults with hip osteoarthritis : decreasing risk through education and aquatic exercise

Purpose: The primary purpose of this project was to determine the effect of aquatic exercise and aquatic exercise combined with an education group program on decreasing both psychosocial and physical fall risk factors in community-dwelling older adults with hip osteoarthritis (OA). Secondary purposes were to 1) describe fall risk, history and nature of falls and near-falls in older adults with hip OA, 2) determine the association of the timed up and go test (TUG) to history of falls and near-falls, 4) explore the relationship of both psychosocial and physical factors to history of falls and near-falls, and 5) evaluate the role of falls-efficacy in predicting balance performance. Methods: Participants were recruited from the community and screened for presence of hip osteoarthritis and fall risk. Baseline fall history and a battery of measures for balance, muscle strength, functional ability and falls-efficacy were administered. Participants were then randomly assigned to one of three groups: Aquatic Exercise, Aquatic Exercise and Education or a Control Group. The interventions were twice per week for 11 weeks. Fall risk factors were measured after 11 weeks. Study 1 described history of falls and near-falls and evaluated the association of the TUG screening test with fall and near-fall history. Study 2 summarized the relationships of physical and psychosocial fall risk factors and identified the primary predictors of fall risk, based on associations with fall history. Study 3 evaluated the randomized controlled clinical trial comparing the impact of the interventions (aquatic exercise and education) on fall risk outcomes. Results: Older adults with hip OA reported a high frequency of falls and near-falls. The TUG, using a cut-off score of 10 sec., was associated with frequent near-fall history. There was a strong association of frequent near-falls to history of actual falls, with the association increasing 7-fold if lower falls-efficacy was present. Falls-efficacy was also an independent predictor of balance impairment. Screening for history of near-falls and falls-efficacy may be important in predicting risk of future falls. The combination of Aquatic Exercise and Education improved falls-efficacy and functional mobility compared to Aquatic Exercise only or no intervention. Aquatic Exercise on its own was not effective in decreasing fall risk factors or improving falls-efficacy. Significance of Findings: The accumulation of both physical and psychosocial risk factors in older adults with hip OA increases their vulnerability to falls and injury. Fall prevention programs for this population should be designed to include both exercise and education to address falls-efficacy and physical fall risk factors