An efficient IoT based biomedical health monitoring and diagnosing system using myRIO

With the growing and aging population, patient auto monitoring systems are becoming more popular. Smart sensors linked with the internet of things (IoT) make patients' auto monitoring system possible. Nowadays myRIO with LabVIEW is more popular for easy data acquisition, instrument control, and automation. This paper proposed myRIO and IoT based health monitoring and diagnosing system (HMDS) to acquire heartbeat rate, pulse, blood pressure (BP), temperature and activities of the patient using various smart sensors with more accuracy. The acquired raw data from the various sensors had been sent to the myRIO using ESP 8266 Wi-Fi module. The received raw data by the myRIO would be processed to the equivalent medical parameters using LabVIEW and the same might be transferred to the remote monitoring system (RMS) using cloud via a gateway. The abnormalities in the obtained data would be monitored and the diagnosis was made. The experimental setup was developed using various wearable sensors, ESP 8266, myRIO with LabVIEW and cloud with the gateway

System Architecture of a Proactive Intelligent System to Monitor Health of Older Adults Living Alone

Worldwide improvements in the quality of life highlight immense need to have a remote health monitoring system that can provide critical biomedical data. This paper presents a low-cost health monitoring system, forming part of the Internet of Things (IoT), which aims at continuous, 24/7 monitoring of elderly people and disabled people. The system is implemented with a variety of sensors, for example, temperature, heart rate, and movement measurements, to observe a person’s status. Doctors may also prescribe this system with a specific number and type of sensor, depending on a patient’s condition. In a case study, three sensors measured the status of a person during the day. The measurements reflected the actions of the person as he/she relaxed or was active, in addition to monitoring his/her state of health. The observed data were recorded in a database that can be displayed by authorized caregivers. Results witnessed the efficacy of the proposed system. The proposed system finds enormous potential in giving remote healthcare facilities, especially to unaccompanied older adults

Unobtrusive monitoring system for adherence to glaucoma eye-drop treatment

Glaucoma is the second most common cause of blindness and the leading cause of irreversible vision loss worldwide. Glaucoma is an eye condition that mostly occurs due to high intraocular pressure and requires immediate treatment. If not treated, it can lead to blindness. The main method to treat glaucoma involves reducing the pressure inside the eye. The available treatments for treating glaucoma include medications, laser procedures and incisional surgery. Treatment of glaucoma is greatly dependent on the type that a patient is experiencing. Though, eye drops are often the first treatment option when dealing with glaucoma. Should eye drops not work, then alternative surgical treatments can be used. The problem with glaucoma patients is that it’s difficult to determine whether treatment is failing because the eye drop is not an effective treatment for them or because they don’t adhere to their treatment. For assisting the clinician, this project will seek to build a possible solution that will address adherence and compliance issues leading the clinician to take more effective decisions for the patient and thus better-quality clinical outcomes. Currently a number of solutions exist to judge and evaluate effective compliance. These range from manual observations to some form of electronic monitoring that seek to establish how well the patient has been keeping track of their medication. This project and thesis will seek to review glaucoma as a medical disease and its impact upon society as a whole. It will also present a solution that will incorporate a paper-thin electronic wrap that would be situated on the bottle with a view to making this as inexpensive as possible. The electronic device or system will be capable of revealing more details to a clinician such as how often the device is used and when it will be squeezed. The next step of this thesis is to design a system to identify correct compliance among these patients. Overall, the outcome of this project resulted in the creation of an electronic monitoring device in the form of a flexible PCB which, given its software, is capable of noting down basic compliance metrics. None of this, however, confirms if the drop actually entered the eye. Advancing on this work, a system was built to accurately determine if a droplet entered the eye utilising vision technologies

Metabolic and mechanical changes in ultra-endurance running races and the effects of a specific training on energy cost of running

The present thesis is divided into two parts. Part I: The objectives of the first part were to examine the factors affecting the ultra-endurance performance and in particular which aspects influence the cost of running (Cr). Consequently, we defined how the Cr and running mechanics changed during different types (i.e. level and uphill) of ultra-endurance races. Finally, we proposed a specific training protocol for improving the Cr in high-level ultra-marathoners. We assessed the Cr by measuring the oxygen consumption at one (or more) fixed speeds using a metabolic unit. Further, for the running mechanics measurement and the spring-mass model parameters computation we used video analysis. Other parameters such as maximal muscle power of the lower limbs (MMP), morphological properties of the gastrocnemius medialis and Achilles tendon stiffness were also measured. Our studies showed that the maximal oxygen uptake, the fraction of it maintained throughout the race and the Cr are the main physiological parameters affecting the ultra-endurance performance, both in level and uphill competitions. Moreover, low Cr values were related to high MMP, vertical stiffness (kvert), low foot print index (FPI), Achilles tendon stiffness and external work. These results indicate that MMP, kvert and FPI are important factors in determining ultra-endurance performance. Also, our studies reported that during ultra-endurance competitions athletes tend to change their running mechanics after a certain time (~4 hours) rather than after a certain distance covered. Then, by adding strength, explosive and power training to the usual endurance training it is possible to lower the cost of running leading to a better performance. From these conclusions we suggest new training protocol for the ultra-marathoners including strength, explosive and power training which maintain a correct and less expensive running technique during ultra-endurance events. Part II: The aim of the second part was to develop and validate a customized thermoplastic polyurethane insole shoe sensor for collecting data about the ground reaction forces (GRF), contact and aerial times. This prototype allowed us to collect vertical GRF and contact time by using piezoresistive force sensors (RFS). Our final model was composed by a rubber insole, five RFSs, an s-beam load cell, an acquisition device (NI myRIO) and a battery case. By using this device we can collect data on field, avoiding the restrictions imposed by the laboratory environmen

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Novel Validation Techniques for Autonomous Vehicles

The automotive industry is facing challenges in producing electrical, connected, and autonomous vehicles. Even if these challenges are, from a technical point of view, independent from each other, the market and regulatory bodies require them to be developed and integrated simultaneously. The development of autonomous vehicles implies the development of highly dependable systems. This is a multidisciplinary activity involving knowledge from robotics, computer science, electrical and mechanical engineering, psychology, social studies, and ethics. Nowadays, many Advanced Driver Assistance Systems (ADAS), like Emergency Braking System, Lane Keep Assistant, and Park Assist, are available. Newer luxury cars can drive by themselves on highways or park automatically, but the end goal is to develop completely autonomous driving vehicles, able to go by themselves, without needing human interventions in any situation. The more vehicles become autonomous, the greater the difficulty in keeping them reliable. It enhances the challenges in terms of development processes since their misbehaviors can lead to catastrophic consequences and, differently from the past, there is no more a human driver to mitigate the effects of erroneous behaviors. Primary threats to dependability come from three sources: misuse from the drivers, design systematic errors, and random hardware failures. These safety threats are addressed under various aspects, considering the particular type of item to be designed. In particular, for the sake of this work, we analyze those related to Functional Safety (FuSa), viewed as the ability of a system to react on time and in the proper way to the external environment. From the technological point of view, these behaviors are implemented by electrical and electronic items. Various standards to achieve FuSa have been released over the years. The first, released in 1998, was the IEC 61508. Its last version is the one released in 2010. This standard defines mainly: • a Functional Safety Management System (FSMS); • methods to determine a Safety Integrated Level (SIL); • methods to determine the probability of failures. To adapt the IEC61508 to the automotive industry’s peculiarity, a newer standard, the ISO26262, was released in 2011 then updated in 2018. This standard provides guidelines about FSMS, called in this case Safety Lifecycle, describing how to develop software and hardware components suitable for functional safety. It also provides a different way to compute the SIL, called in this case Automotive SIL (ASIL), allowing us to consider the average driver’s abilities to control the vehicle in case of failures. Moreover, it describes a way to determine the probability of random hardware failures through Failure Mode, Effects, and Diagnostic Analysis (FMEDA). This dissertation contains contributions to three topics: • random hardware failures mitigation; • improvementoftheISO26262HazardAnalysisandRiskAssessment(HARA); • real-time verification of the embedded software. As the main contribution of this dissertation, I address the safety threats due to random hardware failures (RHFs). For this purpose, I propose a novel simulation-based approach to aid the Failure Mode, Effects, and Diagnostic Analysis (FMEDA) required by the ISO26262 standard. Thanks to a SPICE-level model of the item, and the adoption of fault injection techniques, it is possible to simulate its behaviors obtaining useful information to classify the various failure modes. The proposed approach evolved from a mere simulation of the item, allowing only an item-level failure mode classification up to a vehicle-level analysis. The propagation of the failure modes’ effects on the whole vehicle enables us to assess the impacts on the vehicle’s drivability, improving the quality of the classifications. It can be advantageous where it is difficult to predict how the item-level misbehaviors propagate to the vehicle level, as in the case of a virtual differential gear or the mobility system of a robot. It has been chosen since it can be considered similar to the novel light vehicles, such as electric scooters, that are becoming more and more popular. Moreover, my research group has complete access to its design since it is realized by our university’s DIANA students’ team. When a SPICE-level simulation is too long to be performed, or it is not possible to develop a complete model of the item due to intellectual property protection rules, it is possible to aid this process through behavioral models of the item. A simulation of this kind has been performed on a mobile robotic system. Behavioral models of the electronic components were used, alongside mechanical simulations, to assess the software failure mitigation capabilities. Another contribution has been obtained by modifying the main one. The idea was to make it possible to aid also the Hazard Analysis and Risk Assessment (HARA). This assessment is performed during the concept phase, so before starting to design the item implementation. Its goal is to determine the hazards involved in the item functionality and their associated levels of risk. The end goal of this phase is a list of safety goals. For each one of these safety goals, an ASIL has to be determined. Since HARA relies only on designers expertise and knowledge, it lacks in objectivity and repeatability. Thanks to the simulation results, it is possible to predict the effects of the failures on the vehicle’s drivability, allowing us to improve the severity and controllability assessment, thus improving the objectivity. Moreover, since simulation conditions can be stored, it is possible, at any time, to recheck the results and to add new scenarios, improving the repeatability. The third group of contributions is about the real-time verification of embedded software. Through Hardware-In-the-Loop (HIL), a software integration verification has been performed to test a fundamental automotive component, mixed-criticality applications, and multi-agent robots. The first of these contributions is about real-time tests on Body Control Modules (BCM). These modules manage various electronic accessories in the vehicle’s body, like power windows and mirrors, air conditioning, immobilizer, central locking. The main characteristics of BCMs are the communications with other embedded computers via the car’s vehicle bus (Controller Area Network) and to have a high number (hundreds) of low-speed I/Os. As the second contribution, I propose a methodology to assess the error recovery system’s effects on mixed-criticality applications regarding deadline misses. The system runs two tasks: a critical airplane longitudinal control and a non-critical image compression algorithm. I start by presenting the approach on a benchmark application containing an instrumented bug into the lower criticality task; then, we improved it by injecting random errors inside the lower criticality task’s memory space through a debugger. In the latter case, thanks to the HIL, it is possible to pause the time domain simulation when the debugger operates and resume it once the injection is complete. In this way, it is possible to interact with the target without interfering with the simulation results, combining a full control of the target with an accurate time-domain assessment. The last contribution of this third group is about a methodology to verify, on multi-agent robots, the synchronization between two agents in charge to move the end effector of a delta robot: the correct position and speed of the end effector at any time is strongly affected by a loss of synchronization. The last two contributions may seem unrelated to the automotive industry, but interest in these applications is gaining. Mixed-criticality systems allow reducing the number of ECUs inside cars (for cost reduction), while the multi-agent approach is helpful to improve the cooperation of the connected cars with respect to other vehicles and the infrastructure. The fourth contribution, contained in the appendix, is about a machine learning application to improve the social acceptance of autonomous vehicles. The idea is to improve the comfort of the passengers by recognizing their emotions. I started with the idea to modify the vehicle’s driving style based on a real-time emotions recognition system but, due to the difficulties of performing such operations in an experimental setup, I move to analyze them offline. The emotions are determined on volunteers’ facial expressions recorded while viewing 3D representa- tions showing different calibrations. Thanks to the passengers’ emotional responses, it is possible to choose the better calibration from the comfort point of view