IntelliChair: a non-intrusive sitting posture and sitting activity recognition system

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

Functional analysis of the 5′ flanking region of the human G6PC3 gene: regulation of promoter activity by glucose, pyruvate, AMP kinase and the pentose phosphate pathway

G6PC3 is a widely expressed isoform of glucose-6-phosphatase, found in many foetal and adult tissues. Mutations in this gene cause developmental abnormalities and severe neutropenia due to abolition of glucose recycling between the cytoplasm and endoplasmic reticulum. Low G6PC3 expression as a result of promoter polymorphisms or dysregulation could produce similar outcomes. Here we investigated the regulation of human G6PC3 promoter activity. HeLa and H4IIE cells were transiently transfected with G6PC3 promoter coupled to the firefly luciferase gene, and promoter activity was measured by dual luciferase assay. Activity was highest in a 453 bp segment of the G6PC3 promoter, from − 455 to − 3 relative to the transcriptional start site. This promoter was unresponsive to glucostatic hormones. Its activity increased significantly between 1 and 5.5 mM glucose, and was not elevated further by glucose concentrations up to 25 mM. Pyruvate increased its activity, but β-hydroxybutyrate and sodium acetate did not. Promoter activity was reduced by inhibitors of hexokinase, glyceraldehyde phosphate dehydrogenase and the oxidative branch of the pentose phosphate pathway, but not by a transketolase inhibitor. Deletion of two adjacent Enhancer-boxes (− 274 to − 279 and − 299 to − 304) reduced promoter activity and abolished the glucose effect, suggesting they could function as a glucose response element. Deletion of an additional downstream 140 bp (− 140 to − 306) restored activity, but not the glucose response, suggesting the presence of repressor elements in this region. 5-Aminoimidazole-4-carboxamide 1-β-d-ribofuranoside (AICAR) reduced promoter activity, showing dependence on AMP-kinase. Regulation of the G6PC3 promoter is thus radically different to that of the hepatic isoform, G6PC. It is sensitive to carbohydrate, but not to fatty acid metabolites, and at much lower physiological concentrations. Based on these findings, we speculate that reduced G6PC3 expression could occur during hypoglycemic episodes in vivo, which are common in utero and in the postnatal period. If such episodes lower G6PC3 expression they could place the foetus or infant at risk of impaired immune function and development, and this possibility requires further examination both in vitro and in vivo.

Age- and activity-related differences in the abundance of Myosin essential and regulatory light chains in human muscle

Traditional methods for phenotyping skeletal muscle (e.g., immunohistochemistry) are labor-intensive and ill-suited to multixplex analysis, i.e., assays must be performed in a series. Addressing these concerns represents a largely unmet research need but more comprehensive parallel analysis of myofibrillar proteins could advance knowledge regarding age- and activity-dependent changes in human muscle. We report a label-free, semi-automated and time efficient LC-MS proteomic workflow for phenotyping the myofibrillar proteome. Application of this workflow in old and young as well as trained and untrained human skeletal muscle yielded several novel observations that were subsequently verified by multiple reaction monitoring (MRM).We report novel data demonstrating that human ageing is associated with lesser myosin light chain 1 content and greater myosin light chain 3 content, consistent with an age-related reduction in type II muscle fibers. We also disambiguate conflicting data regarding myosin regulatory light chain, revealing that age-related changes in this protein more closely reflect physical activity status than ageing per se. This finding reinforces the need to control for physical activity levels when investigating the natural process of ageing. Taken together, our data confirm and extend knowledge regarding age- and activity-related phenotypes. In addition, the MRM transitions described here provide a methodological platform that can be fine-tuned to suite multiple research needs and thus advance myofibrillar phenotyping.