Sensor-based navigating mobile robots for people with disabilities

People with severe physical disabilities need help with everyday tasks, such as getting dressed, eating, brushing their teeth, scratching themselves, drinking, etc. They also need support to be able to work. They are usually helped by one or more persona

Examining the relationships among cognitive processing, physical function, and disability in older adults

Age-related declines in cognitive processing are well documented and may contribute to limitations performing daily living tasks as people age. The purpose of this dissertation was to examine the relationships among cognitive processing, physical function, and disability in older adults. Three studies were organized into three distinct manuscripts. In this dissertation, we use the term cognitive processing to refer to performance on measures of attention and processing speed. The objective of the first study was to examine the direct and indirect effects of cognitive processing on physical function and disability. The second study examined: (1) the predictive relationship of cognitive processing to changes in physical function and disability, and (2) the association of change in cognitive processing to change in physical function and disability. The purpose of the third was to explore the relationship of cognitive processing to self-reported disability measured as dependence and measured as difficulty. The combined results of all three experiments confirmed that cognitive processing is associated with both concurrent and future levels of physical function and disability in older adults. The relationship between cognitive processing and disability is primarily mediated by physical function, such that poor cognitive processing is associated with lower levels of physical function and indirectly with higher levels of disability. Poor baseline cognitive processing is also predictive of decreased balance and disability one year later. The relationship of cognitive processing with disability appears to be most robust when iv disability is defined as dependence. These results illustrate the complex relationship of cognitive processing to physical function and disability in older adults

The development and evaluation of a novel online tool for assessing dietary intake and physical activity levels for use in adult populations

The Synchronised Nutrition and Activity Program for Adults (SNAPA™) was developed to address the need for accurate, reliable, feasible, inexpensive and low burden methods for measuring diet and physical activity behaviours in free-living adult populations. Usability testing (n=5) identified a number of usability issues and the program was revised accordingly. Test-retest reliability (n=44) revealed no substantial systematic shifts in mean values. Outcome variables were percentage food energy from fat (%fat), number of fruit and vegetable portions (FV), and minutes of moderate to vigorous activity (MVPA). Single measure intra-class correlations (ICC) ranged from 0.62 to 0.72 and average measure ICC range from 0.76 to 0.84. The preliminary method comparison study (n=71) revealed correlations between SNAPA™ and multiple pass recall dietary interview-derived %fat and FV portions of 0.48 (bootstrapped 90% CI 0.31, 0.64) and 0.42 (bootstrapped 90% CI 0.22, 0.60) respectively. The correlation between SNAPA™ and accelerometry-derived MVPA was 0.39 (bootstrapped 90% CI 0.08, 0.64). The in-depth primary method comparison study (n=77) investigated the agreement between SNAPA™ and dietary observation and combined heart rate and accelerometry. The mean match and phantom rates between SNAPA™ and lunchtime dietary observation was 81.7% and 5.6%, respectively. Correlations between SNAPA™ and the reference method outcomes ranged between 0.39 and 0.56. Passing-Bablok (type II) regression analysis revealed both fixed and proportional bias for the estimation of energy intake; proportional bias for fat intake (g); a fixed bias for MVPA, and no substantial biases for %fat or FV portions. SNAPA™ was used to collect diet and physical activity data in a health promotion campaign, ‘Get a Better Life’ (n=1201), providing useful information on the feasibility of using the program in a real-world initiative. SNAPA™ is a promising tool for the surveillance of diet and physical behaviours at a group level in adult populations

Application and development of indirect measures of free-living energy expenditure

The aims of this thesis were to explore the accuracy in measuring free-living total daily energy expenditure (TDEE), by examining existing indirect measures of energy expenditure (EE) measurement and further, developing new techniques, for improved accuracy and application, in population-based studies. In a number of the studies, the research focus is the heart rate (HR) monitoring technique, for TDEE estimation as a result of its low cost and ease of implementation in large population-based studies. This thesis represents a progression from the application of the HR monitoring technique for estimating EE in response to training, or as a means to validate a physical activity recall instrument. However, what is highlighted are the limitations of the existing methodology for estimated TDEE in this way. Therefore, this thesis introduces a novel concept in the HR monitoring technique, incorporating group-based EE equations, and further, by including the effects of the previous minutes HR response on the estimation of EE from HR. Finally, this thesis validates these modifications, using a respiration chamber, purpose-built as a part of this dissertation. It should be noted, however, that in some instances, the thesis was constrained by opportunistic sampling, or the fact that in the case of Chapter 4, the study sample was part of a larger study designed for another purpose. Nevertheless, the outcomes of this research, in particular, the group-based HR-EE prediction equations, have important implications for large population-based epidemiological research concerning physical activity dose-response. Bibliography: p. 227-253

Interrupting prolonged sitting in overweight, and obese adults and glycaemic responses: a randomised crossover study in free-living conditions

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Science by Research.Aims: The aim of the present study was to investigate 24 h interstitial glycaemia responses to interrupting prolonged sitting in free-living conditions in inactive and sedentary overweight and obese adults. Methods: Twelve overweight and obese individuals (mean ± SD age 47.5 ± 9.9 y) completed two, four-day conditions in a randomised crossover design; Uninterrupted sitting (SIT): 10 h/day sitting, 7 h/day uninterrupted bouts sitting (7 x 60 min bouts), standing and walking restricted to 1.5 h/day, or interrupting sitting (INT SIT): 3 – 6 min of standing, walking, simple body-weight resistance; half squats, lunges, calf raises, knee lifts, and repeated sit-to-stand transitions every 30 min for 10 h/day. Incremental area under the curve (iAUC) was calculated using the trapezoid method. Results: There were no significant differences observed for iAUC glucose measures between SIT and INT SIT conditions. There was no difference in sedentary behaviour between conditions, but daily stepping time and total steps increased significantly in INT SIT compared with SIT. Conclusion: In overweight and obese participants, it may not be possible to manipulate increases or decreases in sedentary behaviourin free-living conditions. Therefore, it was not possible to compare effects of interrupted sitting versus uninterrupted sitting on glycaemia

A Preventive Medicine Framework for Wearable Abiotic Glucose Detection System

In this work, we present a novel abiotic glucose fuel cell with battery-less remote access. In the presence of a glucose analyte, we characterized the power generation and biosensing capabilities. This system is developed on a flexible substrate in bacterial nanocellulose with gold nanoparticles used as a conductive ink for piezoelectric deposition based printing. The abiotic glucose fuel cell is constructed using colloidal platinum on gold (Au-co-Pt) and a composite of silver oxide nanoparticles and carbon nanotubes as the anodic and cathodic materials. At a concentration of 20 mM glucose, the glucose fuel cell produced a maximum open circuit voltage of 0.57 V and supplied a maximum short circuit current density of 0.581 mA/cm2 with a peak power density of 0.087 mW/cm2 . The system was characterized by testing its performance using electrochemical techniques like linear sweep voltammetry, cyclic voltammetry, chronoamperometry in the presence of various glucose level at the physiological temperatures. An open circuit voltage (Voc) of 0.43 V, short circuit current density (Isc) of 0.405 mA/cm2 , and maximum power density (Pmax) of 0.055 mW/cm2 at 0.23 V were achieved in the presence of 5 mM physiologic glucose. The results indicate that glucose fuel cells can be employed for the development of a self-powered glucose sensor. The glucose monitoring device demonstrated sensitivity of 1.87 uA/mMcm2 and a linear dynamic range of 1 mM to 45 mM with a correlation coefficient of 0.989 when utilized as a self-powered glucose sensor. For wireless communication, the incoming voltage from the abiotic fuel cell was fed to a low power microcontroller that enables battery less communication using NFC technology. The voltage translates to the NFC module as the digital signals, which are displayed on a custom-built android application. The digital signals are converted to respective glucose concentration using a correlation algorithm that allows data to be processed and recorded for further analysis. The android application is designed to record the time, date stamp, and other independent features (e.g. age, height, weight) with the glucose measurement to allow the end-user to keep track of their glucose levels regularly. Analytics based on in-vitro testing were conducted to build a machine learning model that enables future glucose prediction for 15, 30 or 60 minutes

A pervasive body sensor network for monitoring post-operative recovery

Over the past decade, miniaturisation and cost reduction brought about by the semiconductor industry has led to computers smaller in size than a pin head, powerful enough to carry out the processing required, and affordable enough to be disposable. Similar technological advances in wireless communication, sensor design, and energy storage have resulted in the development of wireless “Body Sensor Network (BSN) platforms comprising of tiny integrated micro sensors with onboard processing and wireless data transfer capability, offering the prospect of pervasive and continuous home health monitoring. In surgery, the reduced trauma of minimally invasive interventions combined with initiatives to reduce length of hospital stay and a socioeconomic drive to reduce hospitalisation costs, have all resulted in a trend towards earlier discharge from hospital. There is now a real need for objective, pervasive, and continuous post-operative home recovery monitoring systems. Surgical recovery is a multi-faceted and dynamic process involving biological, physiological, functional, and psychological components. Functional recovery (physical independence, activities of daily living, and mobility) is recognised as a good global indicator of a patient’s post-operative course, but has traditionally been difficult to objectively quantify. This thesis outlines the development of a pervasive wireless BSN system to objectively monitor the functional recovery of post-operative patients at home. Biomechanical markers were identified as surrogate measures for activities of daily living and mobility impairment, and an ear-worn activity recognition (e-AR) sensor containing a three-axis accelerometer and a pulse oximeter was used to collect this data. A simulated home environment was created to test a Bayesian classifier framework with multivariate Gaussians to model activity classes. A real-time activity index was used to provide information on the intensity of activity being performed. Mobility impairment was simulated with bracing systems and a multiresolution wavelet analysis and margin-based feature selection framework was used to detect impaired mobility. The e-AR sensor was tested in a home environment before its clinical use in monitoring post-operative home recovery of real patients who have undergone surgery. Such a system may eventually form part of an objective pervasive home recovery monitoring system tailored to the needs of today’s post-operative patient.Open acces