On-body Sensing Systems: Motion Capture for Health Monitoring

On-body sensors capture quantitative data from variety of bio-signals on a subject’s body with applications in health, sports and entertainment. With the increase in health costs, a need has arisen to monitor a patient’s condition out of hospital in a costeffective way. In healthcare applications on-body sensing systems can provide feedback information about one’s health condition either to the user or to a medical centre. They can also be used for managing and monitoring chronic disease, elderly people, and rehabilitation patients. In rehabilitation applications, such systems can be used to capture patient movement and monitor progress or provide feedback to enhance patients’ motor learning and increase rehabilitation effectiveness. Human motion capture systems are expected to generate motion data through several techniques that dynamically represent the posture changes of a human body based on motion sensor technologies. In motion analysis, the human body is typically modelled as a system of rigid links connected by rotary joints. In this paper after describing body models and their approximation by link-segment models, we introduce kinematics and inverse kinematics problems for determining motion. Different sensor technologies and related motion capture systems are then discussed. It is shown how motion data is derived from position and orientation for the different motion capture technologies

Reducing power and increasing accuracy of on-body sensing in motion capture application.

Motion capture coupled with on-body sensing and biofeedback are key enabling technologies for assisted motor rehabilitation. However, wearability, power efficiency and measurement repeatability remain the principle challenges that need to be addressed before widespread adoption of such systems becomes possible. The weight and the size of the on-body sensing system needs to be kept small, and the system should not interfere with the user's movements or actions, but in general they are bulky due to their power consumption requirements. Furthermore, on-body sensors are very sensitive to positioning, which causes increased variability in the motion data. Isolating the characteristic patterns that represent the most important motion data affected by random positioning errors, while also reducing the power consumption, is the authors' main concern. An automated computational approach is considered to address the two problems. The use of functional principal component analysis is investigated for signal separation, whilst accounting for variability in the sensor position. To generate motion data, movements of human subjects and a robot arm are captured. As joint angles are considered in the analysis, the results are independent from the technology used to measure motion. The proposed post-processing technique can compensate for uncertainties due to sensor positional changes, whilst allowing greater energy efficiency of the sensors, thus enabling improved flexibility and usability of on-body sensing

Toward Flexibility in Sensor Placement for Motion Capture Systems: A Signal Processing Approach

Cannot archive published version. Post-Print only. http://www.sherpa.ac.uk/romeo/issn/1530-437X/ Author contacted 21/02/14

On-body Sensing and Signal Analysis for User Experience Recognition in Human-Machine Interaction

—In this paper, a new algorithm is proposed for recognition of user experience through emotion detection using physiological signals, for application in human-machine interaction. The algorithm recognizes user’s emotion quality and intensity in a two dimensional emotion space continuously. The continuous recognition of the user’s emotion during human-machine interaction will enable the machine to adapt its activity based on the user’s emotion in a real-time manner, thus improving user experience. The emotion model underlying the proposed algorithm is one of the most recent emotion models, which models emotion’s intensity and quality in a continuous two-dimensional space of valance and arousal axes. Using only two physiological signals, which are correlated to the valance and arousal axes of the emotion space, is among the contributions of this paper. Prediction of emotion through physiological signals has the advantage of elimination of social masking and making the prediction more reliable. The key advantage of the proposed algorithm over other algorithms presented to date is the use of the least number of modalities (only two physiological signals) to predict the quality and intensity of emotion continuously in time, and using the most recent widely accepted emotion model

Quality assessment of fish burgers from deep flounder (Pseudorhombus elevatus) and brushtooth lizardfish (Saurida undosquamis) during storage at -18ºC

Microbiological, chemical and sensory changes of fish burgers prepared from deep flounder (Pseudorhombus elevatus) and brushtooth lizardfish (Saurida undosquamis) were determined during storage at -18ºC for 5 months. Microbiological counts were including total plate count (TPC), total coliform (TC), Staphylococcus aureus, Psychotropic and Escherichia coli decreased throughout the frozen storage . Reduction of microbial load in brushtooth lizardfish was higher than that in deep flounder, except for Staphylococcus aureus counts that was almost equal in both groups. There was a significant increase in pH value in both groups (P<0.05) in first and second months of storage only. Moisture content increased in both groups at the end of 5th month, with increase of moisture in deep flounder fish burgers being higher than that in brushtooth lizardfish burgers. TVB-N values in both groups increased significantly (P<0.05 and P<0.008 for deep flounder and brushtooth lizardfish burgers, respectively) at the end of the second month, however, there was a decrease or no significant change afterward. TBA value of deep flounder fish burgers had a significant decrease (P<0.05) as storage time continued, however, it increased significantly in brushtooth lizardfish burgers at the end of second month (P<0.006) following by a decrease at the end of storage period. Peroxide value (PV) in both groups increased significantly (P<0.05 and P<0.002 in deep flounder and brushtooth lizardfish burgers, respectively) during storage time but a significant decrease was observed at the end of third and fourth months (P<0.005 and P<0.001 in deep flounder and brushtooth lizardfish burgers, respectively). Sensory parameters (color, texture, taste and general acceptability) for two groups decreased significantly (P<0.003 for all parameters in 2 groups) during storage with deep flounder fish burgers receiving higher scores than brushtooth lizardfish burgers at the beginning and end of the storage period

An approach for adaptive model performance validation within digital twinning

The validation of the operationality of models is considered a crucial step in the model development process. Recent developments in Digital Twinning (DT) enable the online availability of operational data from the physical asset required for operational validation. The benefits of DT in situations where operational validation has formed a basis for model adaptation has also been demonstrated. However, these benefits within DT have not been fully utilized due to the lack of an approach for benchmarking the required quantity, quality and diversity of validation data and performance metrics for online model validation and adaptation. Therefore, there is a need for a framework for benchmarking validation data and metrics requirements during model validation in different domains. An approach for benchmarking the required quantity, quality and variability of validation data and performance metric(s) for online model adaptation within DT is proposed. The approach is focused on addressing the problem of parameter(s) uncertainty of a predictive model within its uncertainty boundary. It involves generating virtual test models, a primary and another reference model for the performance evaluation of one compared to the another with the benchmarked validating data and metrics within DT. This process is repeated until the dataset and/or metric(s) are promising enough to validate primary model against the reference model. The proposed approach is demonstrated using BEASY - a simulator designed to predict protection provided by a cathodic protection system to an asset. In this case, a marine structure is the focus of the study, where the protection potentials to prevent corrosion are predicted over the life of the structure. The algorithm(s) for the approach are provided within a Scientific Software (MATLAB) and integrated to the simulator-based cathodic-protection model

Intelligent Joystick Sensing the User's Emotion and Providing Biofeedback

Development of an intelligent joystick is proposed which senses the user’s bio-signals and recognises the user’s emotion. It provides biofeedback to the user as well as the user’s emotional state information to the computer allowing human-computer interaction over sensitive environment. While the user is interacting with a computer via a joystick the bio-signals can be collected through the user’s fingers touching it. The collected bio-signals information is mapped on a two-dimensional space to find out the quality and intensity of emotion continuously and in a real-time manner. The intelligent joystick has application within several fields such as healthcare, sport and game industries. In such cases, the user can be influenced, or suffer from medical problems while under stress during interaction with the machines. The intelligent joystick will provide feedback to the user and alert alarm about unhealthy conditions through the embedded actuators and allow the machine to adapt with the users’ emotional state