Multi-sensor driver drowsiness monitoring

A system for driver drowsiness monitoring is proposed, using multi-sensor data acquisition and investigating two decision-making algorithms, namely a fuzzy inference system (FIS) and an artificial neural network (ANN), to predict the drowsiness level of the driver. Drowsiness indicator signals are selected allowing non-intrusive measurements. The experimental set-up of a driver-drowsiness-monitoring system is designed on the basis of the soughtafter indicator signals. These selected signals are the eye closure via pupil area measurement, gaze vector and head motion acquired by a monocular computer vision system, steering wheel angle, vehicle speed, and force applied to the steering wheel by the driver. It is believed that, by fusing these signals, driver drowsiness can be detected and drowsiness level can be predicted. For validation of this hypothesis, 30 subjects, in normal and sleep-deprived conditions, are involved in a standard highway simulation for 1.5 h, giving a data set of 30 pairs. For designing a feature space to be used in decision making, several metrics are derived using histograms and entropies of the signals. An FIS and an ANN are used for decision making on the drowsiness level. To construct the rule base of the FIS, two different methods are employed and compared in terms of performance: first, linguistic rules from experimental studies in literature and, second, mathematically extracted rules by fuzzy subtractive clustering. The drowsiness levels belonging to each session are determined by the participants before and after the experiment, and videos of their faces are assessed to obtain the ground truth output for training the systems. The FIS is able to predict correctly 98 per cent of determined drowsiness states (training set) and 89 per cent of previously unknown test set states, while the ANN has a correct classification rate of 90 per cent for the test data. No significant difference is observed between the FIS and the ANN; however, the FIS might be considered better since the rule base can be improved on the basis of new observations

In vitro localization of intracranial haematoma using electrical impedance tomography semi-array

Electrical Impedance Tomography is a non-invasive and portable method that has good potential as an ‎alternative to the conventional modalities for early detection of intracranial haematomas in high risk patients. ‎Early diagnosis can reduce treatment delays and most significantly can impact patient outcomes. Two eight-‎electrode layouts, a standard ring full array (FA) and a semi-array (SA), were investigated for their ability to ‎detect, localise and quantify simulated intracranial haematomas in vitro on ovine models for the purpose of ‎early diagnosis. SA layout speeds up electrode application and avoids the need to move and lift the patient's ‎head. Haematomas were simulated using gel samples with the same conductivity as blood. Both layouts, FA ‎and SA, could detect the presence of haematomas at any location within the skull. The mean of the relative ‎radial position error with respect to the brain radius was 7% for FA and 6% for SA, for haematomas close to the ‎electrodes, and 11% for SA for haematomas far from the electrodes at the back of the head. Size estimation ‎was not as good; the worst size estimation error for FA being around 30% while the best for SA was 50% for ‎simulated haematomas close to the electrodes.

Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries

This paper describes the development of a registration framework for image-guided solutions to the automation of certain routine neurosurgical procedures. The registration process aligns the pose of the patient in the preoperative space to that of the intra-operative space. CT images are used in the pre-operative (planning) stage, whilst white light (TV camera) images are used to capture the intra-operative pose. Craniofacial landmarks, rather than artificial markers, are used as the registration basis for the alignment. To further synergy between the user and the image-guided system, automated methods for extraction of these landmarks have been developed. The results obtained from the application of a Polynomial Neural Network (PNN) classifier based on Gabor features for the detection and localisation of the selected craniofacial landmarks, namely the ear tragus and eye corners in the white light modality are presented. The robustness of the classifier to variations in intensity and noise is analysed. The results show that such a classifier gives good performance for the extraction of craniofacial landmarks

Robust contact force controller for slip prevention in a robotic gripper

Grasping a soft or fragile object requires the use of minimum contact force to prevent damage or deformation. Without precise knowledge of object parameters, real-time feedback control must be used with a suitable slip sensor to regulate the contact force and prevent slip. Furthermore, the controller must be designed to have good performance characteristics to rapidly modulate the fingertip contact force in response to a slip event. In this paper, a fuzzy sliding mode controller combined with a disturbance observer is proposed for contact force control and slip prevention. The controller is based on a system model that is suitable for a wide class of robotic gripper configurations. The robustness of the controller is evaluated through both simulation and experiment. The control scheme was found to be effective and robust to parameter uncertainty. When tested on a real system, however, chattering phenomena, well known to sliding mode research, was induced by the unmodelled suboptimal components of the system (filtering, backlash, and time delays), and the controller performance was reduced

Haematoma detection using EIT in a sheep model

Performance evaluation of a portable digital electrical impedance tomography system to detect haematomas using a sheep model is presented. Two different experiments have been performed using 8-electrode full array configuration. Artificial haematomas were introduced in the first experiment by injecting blood-like conductivity solution via the brainstem, and in the second by placing blood-like conductivity gel at a certain position on top of the parietal lobes of the brain on the left and right sides. For the first experiment, the Electrical Impedance Tomography (EIT) images were reconstructed sequentially for different injection volumes and the quantity index (QI) was calculated as a function of the injected solution volume. The results show a linear relationship of QI to the injected volume. For the second experiment, the images were successfully reconstructed and haematoma was clearly detected and localised using our developed system. The promising results of sheep experiments prove that our developed EIT system is able to detect and quantify small haematomas in head

Augmentative and alternative communication (AAC) advances: A review of configurations for individuals with a speech disability

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain–computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications

X-ray-based machine vision system for distal locking of intramedullary nails

In surgical procedures for femoral shaft fracture treatment, current techniques for locking the distal end of intramedullary nails, using two screws, rely heavily on the use of two-dimensional X-ray images to guide three-dimensional bone drilling processes. Therefore, a large number of X-ray images are required, as the surgeon uses his/her skills and experience to locate the distal hole axes on the intramedullary nail. The long-term effects of X-ray radiation and their relation to different types of cancer still remain uncertain. Therefore, there is a need to develop a surgical technique that can limit the use of X-rays during the distal locking procedure. A robotic-assisted orthopaedic surgery system has been developed at Loughborough University to assist orthopaedic surgeons by reducing the irradiation involved in such operations. The system simplifies the current approach as it uses only two near-orthogonal X-ray images to determine the drilling trajectory of the distal locking holes, thereby considerably reducing irradiation to both the surgeon and patient. Furthermore, the system uses robust machine vision features to reduce the surgeon's interaction with the system, thus reducing the overall operating time. Laboratory test results have shown that the proposed system is very robust in the presence of variable noise and contrast in the X-ray images

Performance evaluation of a digital electrical impedance tomography system

Performance evaluation of a portable digital multi-frequency electrical impedance tomography system is presented. The instrumentation hardware and image reconstruction are assessed according to a systematic methodology using a practical phantom. The phantom is equipped with eight electrodes in a ring configuration and a sinusoidal current of constant amplitude is injected using an adjacent current injection protocol. Artificial anomalies are introduced as inhomogeneity targets and the boundary potential data is collected. The images are reconstructed from the boundary data using Comsol Multiphysics and Matlab. Signal to noise ratio (SNR) and accuracy of the measurements are calculated. The limits of detectability and distinguishability of contrasts are measured from the collected potential data set for single and double inhomogeneities. The conductivity of the targets is successfully reconstructed from the potential data measurements. The detectability value is found to be high when a single target is close to the electrodes, while the values are less for the target in the centre. Also, the value of distinguishability increases when the targets move further away from each other

A methodology for design and appraisal of surgical robotic systems

Surgical robotics is a growing discipline, continuously expanding with an influx of new ideas and research. However, it is important that the development of new devices take account of past mistakes and successes. A structured approach is necessary, as with proliferation of such research, there is a danger that these lessons will be obscured, resulting in the repetition of mistakes and wasted effort and energy. There are several research paths for surgical robotics, each with different risks and opportunities and different methodologies to reach a profitable outcome. The main emphasis of this paper is on a methodology for ‘applied research’ in surgical robotics. The methodology sets out a hierarchy of criteria consisting of three tiers, with the most important being the bottom tier and the least being the top tier. It is argued that a robotic system must adhere to these criteria in order to achieve acceptability. Recent commercial systems are reviewed against these criteria, and are found to conform up to at least the bottom and intermediate tiers, the most important first two tiers, and thus gain some acceptability. However, the lack of conformity to the criteria in the top tier, and the inability to conclusively prove increased clinical benefit, is shown to be hampering their potential in gaining wide establishment

Field trial of an automated ground-based infrared cloud classification system

Automated classification of cloud types using a ground-based infrared imager can provide invaluable high resolution and localised information for Air Traffic Controllers. Observations can be made consistently, continuously in real time and accurately during both day and night operation. Details of a field trial of an automated, ground-based infrared cloud classification system are presented. The system was designed at Campbell Scientific ltd in collaboration with Loughborough University, UK. The main objective of the trial was to assess the performance of an automated infrared camera system with a lightning detector in classifying several types of clouds, specifically Cumulonimbus and Towering Cumulus, during continuous day and night operation. Results from the classification system were compared with those obtained from Meteorological Aerodrome Reports (METAR) and with data generated by the UK Meteorological Office from their radar and sferics automated cloud reports system. In comparisons with METAR data, a Probability of Detection of up to 82% was achieved, together with a minimum Probability of False Detection of 18%