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.

Design, modelling, simulation and integration of cyber physical systems: methods and applications

The main drivers for the development and evolution of Cyber Physical Systems (CPS) are the reduction of development costs and time along with the enhancement of the designed products. The aim of this survey paper is to provide an overview of different types of system and the associated transition process from mechatronics to CPS and cloud-based (IoT) systems. It will further consider the requirement that methodologies for CPS-design should be part of a multi-disciplinary development process within which designers should focus not only on the separate physical and computational components, but also on their integration and interaction. Challenges related to CPS-design are therefore considered in the paper from the perspectives of the physical processes, computation and integration respectively. Illustrative case studies are selected from different system levels starting with the description of the overlaying concept of Cyber Physical Production Systems (CPPSs). The analysis and evaluation of the specific properties of a sub-system using a condition monitoring system, important for the maintenance purposes, is then given for a wind turbine.

Effects of a 12-week aerobic exercise intervention on eating behaviour, food cravings and 7-day energy intake and energy expenditure in inactive men

This study examined effects of 12 weeks of moderate-intensity aerobic exercise on eating behaviour, food cravings and weekly energy intake and expenditure in inactive men. Eleven healthy men (mean ± SD: age, 26 ± 5 years; body mass index, 24.6 ± 3.8 kg/m2; maximum oxygen uptake, 43.1 ± 7.4 mL/kg/min) completed the 12-week supervised exercise programme. Body composition, health markers (e.g. blood pressure), eating behaviour, food cravings and weekly energy intake and expenditure were assessed before and after the exercise intervention. There were no intervention effects on weekly free-living energy intake (p=0.326, d=-0.12) and expenditure (p=0.799, d=0.04), or uncontrolled eating and emotional eating scores (p>0.05). However, there was a trend with a medium effect size (p=0.058, d=0.68) for cognitive restraint to be greater after the exercise intervention. Total food cravings (p=0.009, d=-1.19) and specific cravings of high-fat foods (p=0.023, d=-0.90), fast-food fats (p=0.009, d=-0.71) and carbohydrates/starches (p=0.009, d=-0.56) decreased from baseline to 12 weeks. Moreover, there was a trend with a large effect size for cravings of sweets (p=0.052, d=-0.86) to be lower after the exercise intervention. In summary, 12 weeks of moderate-intensity aerobic exercise reduced food cravings and increased cognitive restraint, however, these were not accompanied by changes in other eating behaviours and weekly energy intake and expenditure. The results indicate the importance of exercising for health improvements even when reductions in body mass are modest.

Enhancement of the total phenolic compounds and antioxidant activity of aqueous Citrus limon L. pomace extracts using microwave pre-treatment on the dry powder

The effect of microwave pre-treatment on the levels of total phenolic compounds, flavonoids, proanthocyanidins and individual major compounds as well as the total antioxidant activity of the dried lemon pomace was investigated. The results showed that microwave pre-treatment significantly affected all the examined parameters. The total phenolic content, total flavonoids, proanthocyanidins, as well as the total antioxidant activity significantly increased as the microwave radiation time and power increased (e.g., 2.5 folds for phenolics, 1.4 folds for flavonoids and 5.5 folds for proanthocyanidins), however irradiation more than 480 W for 5 min resulted in the decrease of these parameters. These findings indicate that microwave irradiation time and power may enhance higher levels of the phenolic compounds as well as the antioxidant capacity of the dried lemon pomace powder. However, higher and longer irradiation may lead to a degradation of phenolic compounds and lower the antioxidant capacity of the dried lemon pomace.

Challenges in imaging and predictive modeling of rhizosphere processes

Background Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes.

Towards a smart semi-active prosthetic leg: preliminary assessment and testing

This paper presents a development of a semi-active prosthetic knee, which can work in both active and passive modes based on the energy required during the gait cycle of various activities of daily livings (ADLs). The prosthetic limb is equipped with various sensors to measure the kinematic and kinetic parameters of both prosthetic limbs. This prosthetic knee is designed to be back-drivable in passive mode to provide a potential use in energy regeneration when there negative energy across the knee joint. Preliminary test has been performed on transfemoral amputee in passive mode to provide some insight to the amputee/prosthesis interaction and performance with the designed prosthetic knee.