Search

Maintaining and balancing an optimal level of workload is essential for completing the task productively. Fighter aircraft is one such example, where the pilot is loaded heavily both physically (due to G manoeuvering) and cognitively (handling multiple sensors, perceiving, processing and multi-tasking including communications and handling weapons) to fulfill the combat mission requirements. This cognitive demand needs to be analysed to understand the workload of fighter pilot. Objective of this study is to analyse dynamic workload of fighter pilots in a realistic high-fidelity flight simulator environment during different flying workload conditions. The various workload conditions are (a) normal visibility, (b) low visibility, (c) normal visibility with secondary task, and (d) low visibility with secondary task. Though, pilot’s flying performance score was good, the physiological measure like heart rate variability (HRV) features and subjective assessment (NASA-TLX) components are found to be statistically significant (p

Predicting and analysing individual muscle forces during walking can give a good perspective of anatomical, physiological, and neurological characteristics of human movement. It can help analyse neuromuscular impairments of skeletal system and provide an understanding on how lower limb assistive devices affect wearer’s body, as these assistive devices are vital to assist people with disabilities to carry their daily activities with ease. Estimating force from EMG allows us to assess the contribution of an individual muscle to the over-all force applied by a group of muscles. This finding helps in understanding muscle dynamics during walking, which can serve as input for assistive devices. Thus, electromyography signals (EMG) can be an excellent choice for force estimation in kinesiological studies. This study aims to predict individual muscle force from EMG during walking. The right gastrocnemius lateralis muscle of a 23-year-old-male subject with no neurological/muscular disorder was analysed at normal walking. Two approaches were used to predict forces from EMG using MATLAB. The forces obtained were compared with force predicted using OpenSim. The main parameters used for prediction were muscle length, muscle velocity, pennation angle, and isometric force, along with EMG.

The trend in aviation automation demands more mental workload of pilots, in addition to their routine manoeuvring task. For fighter aircraft pilots, the mental workload multifold due to read-back or hear-back with precise weapon handling in combat scenarios. Assessing the pilot cognitive workload is an important aspect, as it influences the pilot performance, and this is an error intolerance environment. This study aims to assess fighter aircraft Pilot’s Cognitive WorkLoad (PCWL) and attention when they exposed to a dynamic workload environment; by monitoring the neuronal activities. Here the spectral Electroencephalographic (EEG) features were extracted to assess the dynamic workload (normal, moderate, high, and very high workload), and attention monitored by National Aeronautics and Space Administration-Task Load Index (NASA-TLX) and it serves as a validation for cognitive findings. Also, brain source localization obtained from EEG inverse calculation technique, Standardized Low-Resolution brain Electromagnetic Tomography (sLORETA) performed to understand the coherence of workload and neuronal activity engagement concerning brain lobes. Statistical significance (p

EEG signal is one of the most complex signals having the lowest amplitude which makes it challenging for analysis in real-time. The different waveforms like alpha, beta, theta and delta were studied and selected features were related with the consciousness levels. The consciousness levels detection is useful for estimating the subjects’ performance in certain selected tasks which requires high alertness. This estimation was performed by analyzing signal properties of the EEG using features extracted through discrete wavelet transform with a moving window of 10 seconds with 90% overlap. The EEG signal is decomposed in to wavelets and the average energy and power of the coefficients related to the EEG bands is taken as the features. The data is collected from standard EEG machine from the volunteers as per the protocol. C3 and C4 locations (unipolar) of the standard 10-20 electrode system were selected. The central region of the brain is most optimal location for the consciousness levels detection. The estimation of the data using Discrete Wavelet Transform (DWT) energy, power features provided better accuracy when the central regions were chosen. An accuracy of 99% was achieved when the algorithm was implemented using a classifier based on linear kernel support vector machines (SVM).

Intravenous (IV) drug administration of fluids or medication and blood drawing procedures require the medical practitioner to access the veins in patients. This procedure is of prime importance in saving an injured soldiers life on the battlefield by intravenously administering haemostatic agents to prevent blood loss due to haemorrhage. This paper presents an image acquisition system that captures images of subcutaneous veinpatterns and processes these to conclude the location for intravenous needle-insertion. The system consists of a camera, receptive to near-infrared light, which captures an image of the subcutaneous veins illuminated by a suitable source of light. The contrast-enhanced image of the veins thus obtained is processed using an algorithm to conclude an ideal site for needle-insertion. Subcutaneous vein-images were captured using a modified web-camera that works well under the illumination of near-infrared light and a DSLR (Digital Single Lens Reflex) camera with an external filter to block visible light. An algorithm was developed to extract vein-segments from these images. The system will be used for automated delivery of haemostatic agent.

The wearable physiological monitoring system is a washable shirt, which uses an array of sensors connected to a central processing unit with firmware for continuously monitoring physiological signals. The data collected can be correlated to produce an overall picture of the wearer's health. In this paper, we discuss the wearable physiological monitoring system called 'Smart Vest'. The Smart Vest consists of a comfortable to wear vest with sensors integrated for monitoring physiological parameters, wearable data acquisition and processing hardware and remote monitoring station. The wearable data acquisition system is designed using microcontroller and interfaced with wireless communication and global positioning system (GPS) modules. The physiological signals monitored are electrocardiogram (ECG), photoplethysmogram (PPG), body temperature, blood pressure, galvanic skin response (GSR) and heart rate. The acquired physiological signals are sampled at 250samples/s, digitized at 12-bit resolution and transmitted wireless to a remote physiological monitoring station along with the geo-location of the wearer. The paper describes a prototype Smart Vest system used for remote monitoring of physiological parameters and the clinical validation of the data are also presented.