International Journal of Biomechatronics and Biomedical Robotics (4 papers in press)
Computational Modelling of Musculoskeletal to predict the Human Response with Exoskeleton Suit.
by Govinda Anantha Padmanabha, Shravan Tata Ramalingasetty, Boobalan Vetrivel, Indrajit Mukherjee, Omkar .S.N, Sivakumar R
Abstract: Wearable Exoskeleton is an assistive device for humans to carry heavy loads over long distance. The main aim of the study is to analyse the effect of an exoskeleton on human body during stand to sit motion, the study is accomplished by the use of a biomechanical analysis softwarerncalled LifeMOD
Keywords: Muscle activation; LifeMod®; ADAMS®; Lagrangian Dynamics; Motion capture.
Design and development of an Electrostatic based micropump
by Vinay Varghese
Abstract: Growing need for variety of micro-electro-mechanical systems necessitates advances in manufacturing technologies for high volume production at low cost. In the bio-medical field, drug delivery is one of the areas that attract the most attention for MEMS because of its potential to make drug delivery less invasive, more precise and less painful. A typical micro pump is a MEMS device, which provides the actuation source to transfer the fluid, in this case the drug, from the drug reservoir to the body (tissue or blood vessel) with precision, accuracy and reliability. Micro pumps are therefore an essential component in the drug delivery systems. In this study, a mechanical micro pump (with moving mechanical parts) will be modelled using MEMS module of COMSOL simulation tool. The micro pump will be based on electrostatic actuation which in-turn is based on the Coulomb attraction force between oppositely charged plates. The objective of this study is to determine the performance characteristics of electrostatic diaphragm driver for a range of applied voltages.
Keywords: MEMS; micropumps; drug delivery; electrostatic actuation; coulomb attraction.
Intelligent Detection of Melanoma Growth Stage Based on the Analysis of the Thermal Response of Skin
by Fatemeh Khosravi, Mohammad-Reza Sayyed Noorani, Maryam Shoaran
Abstract: The steady temperature of the skin resulted from the thermal equilibrium of the under skin blood circulation, the local metabolism of the body, and the environment temperature, is important in medical diagnosis. Studies have indicated that in response to a thermal stimulation skin tumors produce a different temperature time history in comparison with healthy tissues. In this paper we exploit this fact to design an intelligent melanoma detection system that estimates the development stage of a skin tumor. This system is based on a feed-forward artificial neural network that receives the signal of the thermal response measured from the skin surface during the heat recovery after removing a cold stimulus. In order to obtain data, using FEM-based simulations we extract the thermal signals of 30 stages of tumor growth. We model the thermal behavior of the skin tissue with the Pennes bioheat transfer equation. Then, these signals are processed quantitatively and their convenient features are extracted. Finally, using the data the network classifier is trained to accurately predict the growth stage of the tumor. The achieved accuracy of 96% shows that the thermal response as the distinguishing criterion is an appropriate choice for the early diagnosis of the melanoma type of skin cancer.
Keywords: Intelligent Systems; Artificial Neural Networks; Melanoma Early Diagnosis; Pennes’ Bio- Heat Transfer Equation.
Assessment of the surface electromyographic activity of ankle muscles in males and females
by Francesco Di Nardo, Alessandro Mengarelli, Elvira Maranesi, Laura Burattini, Sandro Fioretti
Abstract: Surface electromyographic (sEMG) signal is commonly used as main input information to control robotic prosthetic systems. sEMG signals vary from person to person; gender is a factor influencing this variation. Thus, the aim of the study is to detect gender-related differences in sEMG activity of two main ankle-flexor muscles (tibialis anterior, TA and gastrocnemius lateralis, GL) during walking at comfortable speed and cadence. Statistical analysis of sEMG signals, performed in seven male (M-group) and seven female (F-group) adults, showed clear gender-related differences in muscle behavior. The assessment of the different activation modalities, indeed, allowed to detect that F-group adopts a walking modality with a higher number of activations during gait cycle, compared to M-group. This suggests a female propensity for a more complex muscle recruitment, during walking. This novel information suggests considering a separate approach for males and females, in providing electromyographic signals as input information to control robotic systems.
Keywords: surface EMG; statistical gait analysis; gender; ankle motion; shank muscles; tibialis anterior; gastrocnemius lateralis; walking; gait cycle; modalities of muscle activation; myoelectric activity.