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The aim of this paper is to develop a wireless insole foot pressure acquisition device to measure and analyze foot planter pressure during various physical activities. Developed system consists of a pressure insole with four capacitive pressure sensors for each foot. Entire system is developed in house indigenously. Sensors are placed at four foot pressure points and interfaced with microcontroller and wireless acquisition board. Sensors are interfaced with dedicated electronics board made of Capacitance to Digital IC which converts change in capacitance due to foot pressure to an equivalent digital readout. Graphical User Interface is provided for different data curve plot, testing of pressure insole, quantifying force and pressure. A mobile and versatile pressure insole for analysis of foot pressure distribution and magnitude provides useful information to diagnose various foot disorders. Planter pressure measurement during standing, walking and other activity can demonstrate biomechanics of abnormal foot, can analyze diabetic offloading, sports medicine, pre and post treatment evaluation and yield measurement to track distance progression.

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Valvular heart diseases are a prevalent cause of cardiovascular morbidity and mortality worldwide, affecting a wide spectrum of the population. In-silico modeling of the cardiovascular system has recently gained recognition as a useful tool in cardiovascular research and clinical applications. Here, we present an in-silico cardiac computational model to analyze the effect and severity of valvular disease on general hemodynamic parameters. We propose a multimodal and multiscale cardiovascular model to simulate and understand the progression of valvular disease associated with the mitral valve. The developed model integrates cardiac electrophysiology with hemodynamic modeling, thus giving a broader and holistic understanding of the effect of disease progression on various parameters like ejection fraction, cardiac output, blood pressure, etc., to assess the severity of mitral valve disorders, naming Mitral Stenosis and Mitral Regurgitation. The model mimics an adult cardiovascular system, comprising a four-chambered heart with systemic, pulmonic circulation. The simulation of the model output comprises regulated pressure, volume, and flow for each heart chamber, valve dynamics, and Photoplethysmogram signal for normal physiological as well as pathological conditions due to mitral valve disorders. The generated physiological parameters are in agreement with published data. Additionally, we have related the simulated left atrium and ventricle dimensions, with the enlargement and hypertrophy in the cardiac chambers of patients with mitral valve disorders, using their Electrocardiogram available in Physionet PTBI dataset. The model also helps to create ‘what if’ scenarios and relevant analysis to study the effect in different hemodynamic parameters for stress or exercise like conditions.

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Aim of this paper is to develop an EMG based biofeedback system using a virtual reality platform which will help in gait rehabilitation. A low power multichannel EMG acquisition unit has been developed to acquire EMG of six different muscles of the lower limb. EMG from different channels are fused using Bayesian fusion technique and spurious data has been discarded. From the fused EMG data, we calculate different gait parameters like stride time, gait phase etc. Joint trajectory during a gait cycle is obtained, digitized and combined with the gait parameters acquired from EMG. Together they are fed to a VR human model. Just like a person walks, the same EMG and trajectory data being fed to the model, it walks too mimicking the gait of the user, with the same speed, thus providing biofeedback to the user.The system has massive application in gait rehabilitation for poststroke patients, people suffering from cerebral palsy and other neuro muscular gait defects, amputees etc.

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