Physiological Signals Monitoring Assistive Technology in Interaction with Machines to Address Healthy Aging

In this paper, development of age-friendly services and settings in interaction with machines that is among the WHO recommended strategies is addressed. In healthy aging, mental wellbeing plays an important role while over 20% of people in the age group of 60 years and above are affected by mental wellbeing issues worldwide. Mental wellbeing problems have an impact on physical health and vice versa and could cause severe illness. Life stressors are among the main contributors for mental wellbeing problems. People in the mentioned age group are more exposed to life stressors specifically during pandemic. Early stress detection and mood swings could potentially help better mental wellbeing that is currently mainly relying on self-reports which is very biased and subjective. Also, traditionally physiological measure of stress quantified by levels of cortisol requires laboratory settings. Therefore, the need for assistive technologies that addresses early detection and awareness of experienced stress, while providing suitable actions is addressed in this paper for the purpose of mental wellbeing issues caused by stress in everyday life without dependence on laboratory settings for the purpose of healthy aging

Digital Filtering and Signal Decomposition: A Priori and Adaptive Approaches in Body Area Sensing Systems

Elimination of undesired signals from a mixture of captured signals in body area sensing systems is studied in this paper. A series of filtering techniques including a priori and adaptive approaches are explored in detail and applied involving decomposition of signals along a new system’s axis to separate the desired signals from other sources in the original data. Within the context of a case study in body area systems, a scenario is designed and the introduced signal decomposition techniques are critically compared and evaluated. Applying the studied filtering and signal decomposition techniques demonstrates that the functional based approach outperforms the rest in reducing the effect of undesired changes in collected motion data. The results showed that the proposed technique reduces variations in the data for average of 94% outperforming the rest of the techniques in the case study although it will add computational complexity. Such technique helps wider adaptation of systems with less sensitivity; therefore, more portable body area sensing system

Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements.

Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer's Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries

Fusion Learning Colloquium 2022 - Proceedings

This is the proceedings of the 2022 Fusion Learning Colloquium held at Bournemouth University in the UK

Motion Capture Sensing Technologies and Techniques: A Sensor Agnostic Approach to Address Wearability Challenges

Body area sensing systems specifically designed for motion capture need to consider the wearer’s comfort and wearability criteria. In this paper, after studying body models and their approximation by link-segment models, the kinematics and inverse kinematics problems for determining motion are explored. Different sensor technologies and related motion capture systems are then discussed within the context of wearability and portability challenges of the systems. For such systems, the weight and size of the system need to be kept small and the system should not interfere with the user’s movements. The requirements will be considered in terms of portability: portable motion capture systems should be less sensitive in accurate positioning of sensors and have more battery lifetime or less power consumption for their wider adoption as an assisted rehabilitation platform. Therefore, a proposed signal processing technique is validated in a controlled setting to address the challenges. By reducing sampling frequency, the power consumption will be reduced but there would be more variability in data whereas by utilising an adaptive filtering approach the variation can be compensated for. It is shown how by using the technique it is possible to reduce the energy consumption; therefore, the potential to decrease the battery size leading to a less bulky on-body sensing system with more comfort to the wearer