Alzheimer’s disease (AD) is the most common cause of dementia. Several haemodynamic risk factors for AD have been identified, including high systolic blood pressure (BP), brain hypoperfusion and arterial stiffness, as well as ageing. We propose a novel approach for assessing haemodynamic risk factors by analysing arterial pulse waves (PWs). The aim of this feasibility study was to determine whether features extracted from PWs might have utility for stratifying patients at risk of AD.

A numerical model of PW propagation was used to simulate arterial PWs at a range of potential measurement sites, for virtual subjects of each age decade from 25 to 75 years, with subjects at each age exhibiting normal variation in BP and arterial stiffness. Several PW features were extracted, and their relationships with AD risk factors were investigated.

PWs closer to the brain (at carotid and temporal arteries) were found to be suitable for detecting haemodynamic changes associated with AD. Several candidate PW features were identified for future clinical testing. These included features extracted from both BP and photoplethysmogram (PPG) PWs.

This study demonstrates the potential feasibility of using non-invasive PWs to assess haemodynamic risk factors for AD. Not only could these factors be assessed from the BP PW, which is usually measured by a skilled operator, but also from the PPG, which can be acquired by smart watches and phones. If the findings are replicated in clinical studies, then this may provide opportunity for patients to assess their own risk and make lifestyle changes accordingly.

Hospital patients recovering from major cardiac surgery are at risk of paroxysmal atrial fibrillation (AF), which can be life-threatening. Wearable sensors are routinely used for electrocardiogram (ECG) monitoring in patients at risk of AF, providing real-time AF detection. However, wearable sensors could have greater impact if used to identify the subtle changes in P-wave morphology which precede AF. This would allow prophylactic treatment to be administered, potentially preventing AF. ECG signals acquired by wearable sensors are susceptible to several artefacts, making it difficult to distinguish between physiological changes in P-wave morphology, and changes due to noise. The aim of this study was to design and assess the performance of a novel automated P-wave quality assessment tool to identify high quality P-waves, for AF prediction.

We designed a two-stage algorithm which uses P-wave template matching to assess quality. Its performance was assessed using the AFPDB, a database of wearable sensor ECG signals acquired from both healthy subjects and patients susceptible to AF. The algorithm’s quality assessments of 97,989 P-waves were compared to manual annotations. The algorithm identified high quality P-waves with high sensitivity (93%) and good specificity (82%).

This study indicates that the algorithm may have utility for identifying high quality P-waves in wearable sensor data. Measurements of P-wave morphology derived from high quality P-waves could be used to predict AF, improving patient outcomes and reducing healthcare costs. Further studies assessing the clinical utility of the presented tool are warranted for validation.

An individual's cardiovascular state is a crucial aspect of healthy life. However, it is not routinely assessed outside the clinical setting. Smart wearables use photoplethysmography (PPG) to monitor the arterial pulse wave (PW) and estimate heart rate. The PPG PW is strongly influenced by the ejection of blood from the heart, providing opportunity to monitor cardiac parameters using smart wearables. The aim of this study was to investigate the feasibility of monitoring cardiac contractility and left ventricular ejection time (LVET) from a peripheral PPG signal.

PPG PWs were simulated under a range of cardiovascular conditions using a numerical model of PW propagation. PWs were simulated at measurement sites suitable for non-invasive measurements, including the upper arm, wrist, and neck. Indices of cardiac contractility and LVET were extracted from the first and second derivatives of the PPG PWs, and compared to reference values extracted from the blood pressure PW at the aortic root.

There was strong agreement between the estimated and reference values of LVET, indicating that it may be feasible to assess LVET from PPG signals, including those acquired by smart watches. The correlations between the estimated and reference contractility parameters were less strong, indicating that further work is required to assess contractility robustly using smart wearables.

This study demonstrated the feasibility of assessing LVET using smart wearables, which would allow individuals to monitor their cardiovascular state on a daily basis. Further development of techniques to monitor contractility would be particularly for safety monitoring during drug trials.

Magnetic position and orientation detection systems typically feature a permanent magnet that moves relative to a magnetic sensor so that the mechanical motion can be calculated from the sensor output. As a result of their reliability, precision and low fabrication cost, such systems are widely used in modern industrial applications like automotive gear shift detection, for wheel speed sensing and many others. Magnetic system design refers to the task of finding an ideal layout for such a system, to detect the desired motion in the best possible way. The design process can be a mathematically challenging high dimensional optimization problem and must account for multiple constraints and requirements that include stray field influences, ferromagnetic surroundings, limited installation space, desired resolutions, compensation of fabrication tolerances and many more. State of the art techniques for magnetic system design are discussed including analytical solutions, numerical methods like FEM as well as a novel design of computer experiments approach based on additive Gaussian process models.

The rate at which an individual recovers from exercise is known to be an indicator of cardiovascular risk. It has been widely shown that the reduction in heart rate immediately after exercise is predictive of mortality. However, little research has been conducted into whether the time taken for the blood vessels to return to normal is also indicative of risk. In this study we present a novel approach to assess vascular recovery rate using the photoplethysmogram (PPG) signal, which is monitored by smart wearables.

The Vortal dataset (http://peterhcharlton.github.io/RRest/) was used for this study, containing PPG signals measured from 39 healthy subjects before (baseline) and after intense exercise. Pulse wave analysis techniques were used to extract over 30 cardiovascular parameters from the PPG pulse wave shape. The rate at which each parameter returned back to its baseline value after exercise was quantified, and the consistency of changes between subjects was assessed statistically.

Most parameters exhibited significant changes after exercise. Parameters derived from both the amplitudes and timings of pulse wave features showed consistent changes between subjects, indicating that both the heart rate and vascular elasticity changed following exercise.

This study demonstrated the feasibility of assessing vascular recovery rate after exercise from the PPG. Candidate parameters were identified for further studies, which should investigate whether they are associated with cardiovascular fitness, and whether they provide additional information beyond that of heart rate recovery. These parameters could be measured by both clinical and consumer devices, providing an extra dimension to cardiovascular risk assessment.

Hadrontherapy makes possible to deliver high doses of energy on cancerous tumours by using the large energy deposition in the Bragg-peak. However, uncertainties in the patient positioning and or in the anatomical parameters can cause distortions in the calculation of the dose distribution. In order to maximize the effectiveness of heavy particle treatments, an accurate monitoring system of the deposited dose dependent on the energy, the beam time and the spot size is necessary. The localized deposition of this energy leads to the generation of a thermoacoustic pulse that can be detected using acoustic technologies. This article presents different experimental and simulation studies of the acoustic localization of thermoacoustic pulses. With respect to the experimental measurements, the pulse is emulated by means of piezoelectric ceramic or thermoacoustically generated by an electrical source. Both sources are placed in water and within a "phantom" medium that recreates the mechanical properties of the human body.. The signals have been captured with a set of sensors around the samples with a data acquisition system. In addition, numerical simulations have been done where thermoacoustic pulses are emitted for the specific case of three proton beams of 20, 80 and 100 MeV, and the pressure signal is then determined at different positions in the space. Several methods for the localization of the source from the signals measured or simulated are used and compared. The results of the location of all these acoustic sources show that an accuracy of the order of a millimetre or less is possible, that is, the precision necessary for proton therapy monitoring is being reached.

In this work we present a prototype of a compact linear array with three elements that using the parametric acoustic effect is able to reproduce the acoustic signature of Ultra High Energy neutrino interaction in water. To mimic this signal is non-trivial since it is a very directive bipolar transient signal with cylindrical symmetry. We characterise the prototype by measuring the signal waveform, the attenuation, intensity variation and directivity. We also study different kind of signals to conclude the best application for the array. Finally, we propose other underwater acoustic applications for the prototype.

Energy harvesting engineering field constitute a promising area to provide electrical power for low-power electric applications obtained from other sources of energy available in the environment such as thermal, electromagnetic, vibrational and acoustic by using transducers. Vibrational sources stand out as a main alternative to be used for generating electric power in sensor nodes, microelectronic devices due to greater energy conversion efficiency and the use of simple structure. The cantilever is the main system implemented in studies of obtaining electric energy from vibrations using piezoelectric transducers. Most of piezoelectric transducers in the literature are not yet commercially available and / or are difficult to access for purchase and use it. This paper proposes the characterization of low-cost piezoelectric transducers, configured as sensors, for Energy Harvesting applications using three different sizes of circular piezoelectric diaphragms (diameters: 3.4cm, 2.,6cm and 1.5cm) PZTs. For all three different PZTs it was found that the maximum power transfer occurs for a resistive load of 82kΏ. The maximum power generated in the load for the three PZTs was 40uW, 14uW and 1.4W, RMS voltages 1.8V, 1.0V and 0.34V, acceleration of 1.3g and at vibration frequency approximate of 7Hz.

Recent advances in computer vision techniques allow to obtain information on the dynamic behavior of structures using commercial grade video recording devices. The advantage of such schemes is due to the non-invasive nature of video recording, and the ability to extract information at a high spatial density utilizing features on the structure. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm. Both are validated against conventional sensors for a lab-scale shear frame and compared. In cases of imperceptible motion, the recently proposed Phase-based Motion Magnification (PBMM) technique is employed to obtain modal information within frequency bands of interest and further used for modal analysis. The optical flow scheme combined with (PBMM) is further tested on a large-scale post-tensioned concrete beam and validated against conventional measurements, as a transition from lab- to outdoor field applications.

In this paper, we propose a vision-based recognition approach to control the posture of a 4 DOF robotic arm using static and dynamic human hand gesture. Different methods are investigated to intuitively control a robotic arm posture in real-time using depth data collected by a RGB-D sensor. First, the fingertips of the user's right hand are recognized and mapped in a Cartesian space to perform an inverse kinematic on the robot end effector's position. Meanwhile, a graphical interface assists the user in intuitively selecting the desired robotic arm posture from a set of possibilities, by displaying those possibilities based on the end effector position calculated using the FABRIK algorithm for inverse kinematics. Using his left hand, the user can select a specific posture from the samples by moving his hand. A second method uses the direction of a finger rather than a separate hand to select the posture based on a point of attraction to displace each joint. A weighted distance calculation for the set of joints is determined to evaluate the similarity of each new posture against the next available model posture. In this method, the user does not need to rely on displayed samples to select a new posture. The posture is automatically sent to the robotic arm if it converged toward a better match for the model posture compared to the last posture. The performance of these real-time natural human control approaches is compared and evaluated against classical master-slave solutions.