Feasibility of wireless horse monitoring using a kinetic energy harvester model

To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric energy harvester at the leg (or in the hoof) of the horse, allowing perpetual monitoring devices. This paper reports the average power that can be delivered to the node by energy harvesting for four different natural gaits of the horse: stand, walking, trot and canter, based on an existing model for a velocity-damped resonant generator (VDRG). To this end, 33 accelerometer datasets were collected over 4.5 h from six horses during different activities. Based on these measurements, a vibrational energy harvester model was calculated that can provide up to 64.04 mu W during the energetic canter gait, taking an energy conversion rate of 60% into account. Most energy is provided during canter in the forward direction of the horse. The downwards direction is less suitable for power harvesting. Additionally, different wireless technologies are considered to realize perpetual wireless data sensing. During horse training sessions, BLE allows continues data transmissions (one packet every 0.04 s during canter), whereas IEEE 802.15.4 and UWB technologies are better suited for continuous horse monitoring during less energetic states due to their lower sleep current

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Helium, Oxygen, Proton, and Electron (HOPE) Mass Spectrometer for the Radiation Belt Storm Probes Mission

The HOPE mass spectrometer of the Radiation Belt Storm Probes (RBSP) mission (renamed the Van Allen Probes) is designed to measure the in situ plasma ion and electron fluxes over 4π sr at each RBSP spacecraft within the terrestrial radiation belts. The scientific goal is to understand the underlying physical processes that govern the radiation belt structure and dynamics. Spectral measurements for both ions and electrons are acquired over 1 eV to 50 keV in 36 log-spaced steps at an energy resolution ΔE FWHM/E≈15 %. The dominant ion species (H+, He+, and O+) of the magnetosphere are identified using foil-based time-of-flight (TOF) mass spectrometry with channel electron multiplier (CEM) detectors. Angular measurements are derived using five polar pixels coplanar with the spacecraft spin axis, and up to 16 azimuthal bins are acquired for each polar pixel over time as the spacecraft spins. Ion and electron measurements are acquired on alternate spacecraft spins. HOPE incorporates several new methods to minimize and monitor the background induced by penetrating particles in the harsh environment of the radiation belts. The absolute efficiencies of detection are continuously monitored, enabling precise, quantitative measurements of electron and ion fluxes and ion species abundances throughout the mission. We describe the engineering approaches for plasma measurements in the radiation belts and present summaries of HOPE measurement strategy and performance

New Training Strategies and Evaluation Methods for Improving Health and Physical Performance

The aim of this Special Issue was to propose, on the basis of the evidence that the current literature provides, new training techniques and specific evaluation methods for the different populations practicing physical activity

Development of Multifunctional E-skin Sensors

Electronic skin (e-skin) is a hot topic due to its enormous potential for health monitoring, functional prosthesis, robotics, and human-machine-interfaces (HMI). For these applications, pressure and temperature sensors and energy harvesters are essential. Their performance may be tuned by their films micro-structuring, either through expensive and time-consuming photolithography techniques or low-cost yet low-tunability approaches. This PhD thesis aimed to introduce and explore a new micro-structuring technique to the field of e-skin – laser engraving – to produce multifunctional e-skin devices able to sense pressure and temperature while being self-powered. This technique was employed to produce moulds for soft lithography, in a low-cost, fast, and highly customizable way. Several parameters of the technique were studied to evaluate their impact in the performance of the devices, such as moulds materials, laser power and speed, and design variables. Amongst the piezoresistive sensors produced, sensors suitable for blood pressure wave detection at the wrist [sensitivity of – 3.2 kPa-1 below 119 Pa, limit of detection (LOD) of 15 Pa], general health monitoring (sensitivity of 4.5 kPa-1 below 10 kPa, relaxation time of 1.4 ms, micro-structured film thickness of only 133 µm), and robotics and functional prosthesis (sensitivity of – 6.4 × 10-3 kPa-1 between 1.2 kPa and 100 kPa, stable output over 27 500 cycles) were obtained. Temperature sensors with micro-cones were achieved with a temperature coefficient of resistance (TCR) of 2.3 %/°C. Energy harvesters based on micro-structured composites of polydimethylsiloxane (PDMS) and zinc tin oxide (ZnSnO3) nanowires (NWs; 120 V and 13 µA at > 100 N) or zinc oxide (ZnO) nanorods (NRs; 6 V at 2.3 N) were produced as well. The work described herein unveils the tremendous potential of the laser engraving technique to produce different e-skin devices with adjustable performance to suit distinct applications, with a high benefit/cost ratio

Artifact Noise Removal Techniques and Automatic Annotation on Seismocardiogram Using Two Tri-axial Accelerometers

Heart disease are ones of the most death causes in the world. Many studies investigated in evaluating the heart performance in order to detect cardiac diseases in the early stage. The aim of this study is to monitor the heart activities in long-term on active people to reduce the risk of heart disease. Specifically, this study investigates the motion noise removal techniques using two-accelerometer sensor system and various positions of the sensors on gentle movement and walking of subjects. The study also ends up with algorithms to detect cardiac phases and events on Seismocardiogram (SCG) based on acceleration sensors. A Wi-Fi based data acquisition system and a framework on Matlab are developed to collect and process data while the subjects are in motion. The tests include eight volunteers who have no record of heart disease. The walking and running data on the subjects are analyzed to find the minimal-noise bandwidth of the SCG signal. This bandwidth is used to design bandpass filters in the motion noise removal techniques and peak signal detection. There are three main techniques of combining data of the two sensors to mitigate the motion artifact: analog processing, digital processing and fusion processing. The analog processing comprises analog ADDER/SUBTRACTOR and bandpass filter to remove the motion before entering the data acquisition system. The digital processing processes all the data using combinations of total acceleration and z-axis only acceleration. The fusion processing automatically controls the amplification gain of the SUBTRACTOR to improve signal quality as long as a signal saturation is detected. The three techniques are tested on three placements of sensors including horizontal, vertical, and diagonal on gentle motion and walking. In general, the total acceleration and z-axis acceleration are best techniques to deal with gentle motion on all placements which improve average systolic signal-noise-ratio (SNR) around 2 times and average diastolic SNR around 3 times comparing to only one accelerometer. With walking motion, overall the ADDER and zaxis acceleration are best techniques on all placements of the sensors on the body which enhance about 7 times of average systolic SNR and about 11 times of average diastolic SNR comparing to only one accelerometer. The combination of two sensors also increases the average number of recognizable systole and diastole on walking corresponding to 71.3 % and 43.8 % comparing toiii only one sensor. Among the sensor placements, the performance of horizontal placement of the sensors is outstanding comparing with other positions on all motions. There are two detection stages to detect events in the SCG for automatic annotation. First, two algorithms including moving average threshold and interpolation are applied to locate the systolic and diastolic phases. Then, based on those identified phases, cardiac events are found in the searched intervals using two outstanding characteristics of the SCG. The two algorithms of phase detection are examined on the stationary data sets of digital processing and horizontal placement. The total acceleration of only one sensor is also calculated for comparison. With moving average threshold algorithm, the average error and missing rates of total acceleration and z-axis acceleration are 1.8 % and 2.1 % respectively which are lower than using one accelerometer (3.6 %). With interpolation algorithm, the average error and missing rates of total acceleration and z-axis acceleration are in the order of 2.3 % and 2.4 % which are still lower than one accelerometer. The average calculation time of the moving average algorithm is lower than the interpolation counterpart. The real-time mode of detection algorithms is also demonstrated on Matlab framework to prove the possibility of practical applications

Kinanthropometry IX

This is an edited collection of peer-reviewed papers presented at the Ninth International Conference of the Society for the Advancement of Kinanthropometry. Defined as the relationship between human body structure and function, kinanthropometry is an area of growing interest, and these proceedings will be of use to students, academics and professionals in the areas of ergonomics, sports science, nutrition, health, and other allied fields. The assembled works represent the latest research findings across kinanthropometry, moving the discipline forward and promoting good practice and the exchange of expertise

Kinanthropometry IX

This is an edited collection of peer-reviewed papers presented at the Ninth International Conference of the Society for the Advancement of Kinanthropometry. Defined as the relationship between human body structure and function, kinanthropometry is an area of growing interest, and these proceedings will be of use to students, academics and professionals in the areas of ergonomics, sports science, nutrition, health, and other allied fields. The assembled works represent the latest research findings across kinanthropometry, moving the discipline forward and promoting good practice and the exchange of expertise