Early Detection and Monitoring of Sarcopenia in Older Individuals

Sarcopenia, defined by a gradual loss of muscle mass and function, affects many elderly people and is associated with reduced mobility, greater frailty, and a higher risk of falling. Using a vision attentive model and integrating the embedded Timed Up and Go Test (TUG-T), 3-meter Walk Test (3 mW−T), and fall risk analysis, this research proposes a unique method for assessing sarcopenia in elderly people. The attentive vision model uses computer vision techniques to examine TUG activities and gait speed in real-time, offering insightful information about the elderly’s the functional ability and muscular strength. Moreover, this approach provides a more comprehensive assessment of sarcopenia by integrating falling risk analysis. The proposed system achieved an overall accuracy of 86.6%, outperforming the individual components: TUG test (84.0%,p<0.05), gait speed (88.2%,p<0.05), and fallen risk assessment (93.0%,p<0.05). The results indicate that this novel strategy has enormous potential for aged healthcare, enabling targeted therapies and enhancing the overall quality of life for older people at risk of issues connected to sarcopenia.

Publications

Solar Remote LAB

Development of the Solar Remote Laboratory for the Department of Mechanical Engineering, OUSL initiated under the EUSL energy program funded by European Union in 2020. Solar remote laboratory intended to carry out experiments based on control and real environmental conditions to cater to different learning outcomes. The solar remote laboratory is a setup that could perform experiments and gather data for research purposes remotely controlled by the experiment setup.

Development Group

Monitoring the Impact of Stress on Facial Skin Using Affective Computing

Nowadays, individuals and communities are benefiting from early recognition of stress. The American Psychological Association states that 75% of individuals are suffering moderate to severe levels of stress. Traditional stress detection methods rely on physiological signals, which are contact-based and require sensors to be in close proximity to humans. As a result, developing a reliable method of detecting stress that does not rely on physiological signaling is still a challenge. Several researchers in the field have used facial expressions and physiological signals to indicate stress levels. Stress might manifest itself in the form of facial skin conditions in the severe stage. The American Institute of Stress states that stress causes damage to the facial skin in different ways. There is, however, a gap in the field for describing stress using a combination of valence, arousal, and physical changes caused to the face. Contribution: This research is evaluating the relationship between mind (stress) and skin (facial skin) using factors such as valence, arousal, and facial skin conditions using the affective computing approach. Results: We investigated the mind-skin connection between 37 emotional test subjects (mean age: 31 ± 4 yrs.), and observed that V-A evaluation was 80.83% accurately showed promising results. The stress test group had a direct association with their facial skin, according to our findings. Moreover, 85.5% of the stress test group were suffered from high to server-level of skin conditions. 

Publications

Tea Bud Leaf Identification by Using Machine Learning and Image Processing Techniques

This research paper concerns the machine learning approach for tea bud leaf identification. The tea bud identification is most important for the process of automated tea leaves grading machines. In the present situation, there are no methods to identify the tea bud leaf separately from the main tea leaf. Unfortunately developing of mechanism for identification process is impossible because the plucked tea leaves not in the same condition. Therefore, the identification is needs intelligent practice to detect the tea bud leaf. In this research, the machine learning object detection technique is developed and successfully used for identify tea bud leaf and the capability of this proposed technique is validated through experimental results obtained by performing experiments by using MATLAB software. According to results, the proposed methodology provides 55% of overall accuracy for identification.


Design and Development of Fuzzy Logic Controller for Magnetic Levitation System

This paper concerns the control behavior of a magnetic levitation system using a fuzzy logic controller (FLC). The magnetic levitation system is a feedback control system already acknowledged and accepted in advance control systems. This kind of system needs a suitable intelligent controller for positioning a metal sphere in airspace with the help of an electromagnetic force. The magnetic force produced by an electromagnet and magnetic force controlled by changing the supply current. The position of the metal sphere controlled by changing the electromagnetic force. FLC proposed in this paper is to control the magnetic levitation system, developed by using Takagi-Sugeno fuzzy controller. The final simulation results with different initial conditions concerning the ball's desire and actual responses are provided to validate the theory. The capability of this proposed technique is to validate through experimental results obtained by performing experiments by using a real-time magnetic levitation system. Based on the experimental results and response analysis, it shows the fuzzy controller can stabilize the system and efficiently follow the desired trajectory the same as the PID controller response.

Publications