The company invented a product that can monitor vulnerable people. It is important that they know exactly where their products are located. If a situation occurred where a vulnerable person needs immediate help, first responders can use the knowledge of the location to get to the patient as fast as possible. This thesis investigates if a modular localization solution can be designed for this product. The product will always be located in an indoor environment, which is a difficult place to establish the location. A modular solution is interpreted as a system that has parts that are replaceable. Some parts can be switched out for different parts with the same functionality, with the system still working properly. In order to design this system according to the requirements, the design process was divided in hardware and software. Both were designed in a modular way, yet eventually implemented in one specific way. This was done to get a tangible prototype that could be evaluated and tested. Improvements to the system were also designed, however could not be implemented in time. Nonetheless, the resulting system works well under conditions of intended use. There were some conditions in which the system performed less than required, however this should be fixed once the improvements are implemented. The results indicate that the localization system that is needed for the company's product is possible to be implemented. The next step is to research whether parts of the modular system can be replaced by an improvement. Then the system can be tested in more distinct working conditions, which could not be done during this project. If the system passes these tests, it is ready to be integrated with the other subsystems of the product, such that the company can bring their product to the market.

This thesis describes the design and implementation of a subsystem within a larger medical monitoring system. This subsystem will make bidirectional audio communication possible between a patient and their caregivers or family members. The patient in question can be premature babies that have to stay inside of incubators and elderly people that need to be monitored for their health. The designed subsystem is tasked with creating an audio stream that goes bidirectionally between a speaker with a handsfree functionality and the front end via a server. On the server the audio from the patient should be stored on a hard drive. Whether the audio communication should be stopped or started and whether the audio should be stored will be controlled by the front-end user. Initially the goal was to implement the solution on a Pine singleboard computer (SBC), but due to unforeseen delays of the delivery of the necessary Pine SBC, it was decided that a proof of concept would be developed. The future work in this thesis will discuss the portability of the solution to the Pine SBC.

This report is part of a bigger project to design a fall detection system for elderly and describes the hardware of the system. The importance of this research is that elderly can live longer in their home by alerting the correct instances in case of an accident. The hardware consists of a 1x2 meter sensor grid below the surface of the floor. The sensor grid uses the piezoresistivity of linqstat to measure the pressure and the location of the pressured point on the floor. An ESP8266 microprocessor combined with 74HC595 shift registers and a 74HC4067 demultiplexer is used to gather the data and send it to a server. The design is modular allowing easy installation in differently sized or shaped rooms. Each individual sensor grid has an update frequency of 14Hz and uses wifi to connect to a common server. Each module has an average power consumption of 0,74W and a peak power of 1,6W.

This report describes the design and implementation of an interface subsystem for a fall detection system using a pressure based floor sensor. The goal of the fall detection system is to detect and alarm when an elderly person has fallen. The subsystem covers communication between hardware and software, using the WiFi protocol MQTT. The communication is fast enough so that it does not limit other subsystem performance and allows for high expandability to accommodate the hardware’s modular design. The interface module receives a probability of fall occurrence from the separate processing module, from which an average is taken over a 30 second time window to avoid false positives. Alarming is done through the use of text-to-speech voice API to make automated phone calls that retrieve user input to confirm or deny that help is on the way. A habit tracker proof of concept is provided that could improve the accuracy of fall detection by checking if the client is showing anomalies during a given day. A user interface is implemented using the Kivy environment. This environment has its own language which allows for separation of functionality and layout, which keeps the interface elements separate and leads to ease of use. Overall, this results in a strong alternative for fall detection that could be used to improve the time an elderly person can live at home safely without the need to move to a nursing home.

In the past decades, the workload and the pressure on medical personnel has been growing to an unprecedented peak. This is partly due to the ageing population and the increasing capabilities to be independent at an older age, increasing the age people enter nursing homes. This paper focuses on a novel way to detect incidents that could occur in the daily life of the elderly. Unlike most systems already proposed by others, there will be no use of wearable positioning sensors and the system is implemented on an Single Board Computer (SBC). This thesis report is one of a set of two reports discussing the final implementation of the system.

This report describes the implementation of a custom streaming solution from an IP camera to a web browser. The system aims to both provide live video and Video on Demand. This will be used to monitor premature babies in incubators.

This report describes the design and implementation of a fall detection algorithm for a fall detection system using a pressure based floor sensor. The goal of the system is to detect falls and alarm the relevant personnel when an elderly person has fallen. The fall detection algorithm has a strong connection with the interface subsystem, which uses the algorithm as a function. The interface subsystem supplies matrices containing the raw sensor values of the pressure floor. The algorithm has been divided into multiple sub-algorithms. First, pre-processing: data linearization was applied on the raw sensor values and the sensor matrix was processed such that an image formed that looked like the real world scenario. Second, image processing techniques were applied to detect contours. Contours were being tracked through time, and being grouped. The characteristics of the contours and groups were used to classify falls. Tests have been done to validate the behaviour of the algorithm, from which an average false negative ratio of 30% was achieved in a time window of 30 seconds. The created prototype proves that image processing is a viable tool for detecting falls with the use of a pressure-based floor sensor. Overall, this results in a strong alternative for fall detection that could be used to improve the time an elderly person can live at home safely without the need to move to a nursing home

This thesis report, one of a set of two reports, describes a novel way to detect incidents that could occur in the daily life of the elderly. Unlike most systems already implemented in this field, this system does not use any wearable (positioning) sensors and works off an Single Board Computer (SBC).Independent of both of these systems is a system for reassurance to alleviate distress.

Problem: A biomarker that accurately predicts recovery of ischemic stroke for patients with poor baseline Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) is lacking. Biomarkers that predict recovery while providing functional insight into the underlying neural process are highly desired for optimal clinical care. Objectives: This explorative study aims to determine potential biomarkers from Somatosensory Evoked Potentials (SEP) using ElectroEncephaloGraphy (EEG). Brain asymmetry metrics are derived from high-density EEG recordings of five longitudinal stroke patients during the first six months of stroke recovery. In addition, EEG sources and their interactions, constrained by anatomical information from (d)MRI, are explored for a potential biomarker. Results: Subjects with low baseline FMA-UE show a trend of increased recovery as the Laterality Index (LI) of the infarcted hemisphere increases, although it is not significant within this small group. The LI of the non-infarcted hemisphere shows a significant trend of high LI values at baseline that decrease to lower values during recovery. This effect was tested on a different dataset of 17 longitudinal stroke patients (without dMRI data). The results in this second group showed strong variability between subjects and measurements. The non-infarcted hemisphere is able to significantly predict the FMA-UE for both datasets. On the other hand, estimated effects for the LI of the infarcted hemisphere did not show significant values. Biomarkers were not derived from EEG sources and their interactions. Conclusion: In this study, we have demonstrated the potential of the LI as a biomarker for stroke recovery. Small sample size and absence of controls make hard clinical conclusions impossible. However, our findings show that the LI might predict recovery of the FMA-UE, even at low baseline FMA-UE. The LI of the non-infarcted hemisphere is able to significantly predict the FMA-UE of the affected hand, a surprising discovery that might be attributed to background cortical activity or ipsilateral SEP components. Derivations from EEG sources and their interactions might lead to more sensitive metrics and novel insights into stroke rehabilitation. Significance: If the discovered trends apply to a larger sample size, asymmetry metrics from SEP’s have prognostic value in stroke recovery.

Various source localization algorithms exist to perform localization with High Density (HD)-ElectroEncephaloGraphy (EEG). However, validation of these EEG source localization algorithms is lacking. The current gold standard for source localization in the brain is functional Magnetic Resonance Imaging (fMRI) by calculating the difference in hemodynamic response to different stimuli. This study aims to validate HD-EEG source localization with fMRI using an MR compatible haptic robot. Participants performed several tasks with the robot to activate cortex patches and allow localization of source activity under various circumstances. These hypothesized patches are the somatosensory, motor and visual cortex. No comparison is made with fMRI due to time constraints. As no comparison could be made, we aim to validate the experimental methodology. Activation of the somatosensory cortex is clearly visible. The visual cortex is often localized, but lacks power in some settings. Activity during the torque task can be localized, but not conclusively to the motor cortex. In all, the experiment was a success, as it was able to induce verifiable different brain states. Hypothesized task contrasts contained different activity distributions. Improvements can be made by generating a more detailed leadfield and by applying a linear manipulator

This report details research, design, and implementation of a distance measuring subsystem for a pre existing patient monitoring platform. This platform is used to monitor vitals and the general situation regarding the elderly, people in care facilities, and babies in NICU’s (Neonatal Intensive Care Units). Every monitoring unit contains a GPS, a touch screen, and a camera. The product is also connected to a Bluetooth speaker for audio in- and output. Everything is designed in such a way that no technical knowledge is required to interact with it. The distance measurement subsystem needs to perform a distance measurement based on optical technology. Research revealed that an accurate and low-cost system can be implemented which is based on images. In the end a single camera feature-based method was implemented. A stereo vision solution was also looked into.

Non-contact infrared thermal imaging provides a non-invasive, safe and accurate means for screening subjects. COVID-19 (a disease caused by the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2)) has changed how health care workers (HCW) interact with patients. Despite the usage of personal protective equipment (PPE), social distancing protocols, and unique facilities to screen individuals, the increase of infection rates among HCW is high. This has enormous consequences as HCW cannot provide direct patient care, thereby decreasing the workforce’s availability during the pandemic and has fuelled the urgent need for a non-invasive IR system to screen for potential cases of infections. Existing remote screening systems are susceptible to shortcomings caused by location and technique used for measurement, distance-ambient temperature associated inaccuracies and inefficient integration of hardware and software modules, invalidating their utility in the current situation. During this project, a non-invasive screening prototype was developed that mitigates these shortcomings by using an adaptive measurement method with automatic environmental compensation for screening elevated skin temperature and respiratory rate. The prototype is tested and validated in a controlled environment.

This project aims to explore the possibility of monitoring the vitals of the neonate remotely and without placing any sensors or electrodes on the subjects body using Thermal Imaging.

Every year, nearly five million people undergo knee, making it one of the most common orthopaedic procedures. Sport-related injuries and age-related diseases are the major causes that lead to a knee operation. To recover mobility, stability and muscle strength, the person must go through a post-surgical rehabilitation program. The price of these physiotherapy sessions increases the already high medical cost of the treatment. Additionally, it is a long and repetitive process that might make them lose motivation and discourage them before finishing the whole program. It is also inaccessible for some people, who live far away from a hospital or rehabilitation centre or in areas where the public healthcare system is saturated. As a result, many people abandon their rehabilitation programs and never recover the pre-injury state. In this project, we propose the use of a game controlled using Inertial Measurement Units to improve the rehabilitation process. The game would reduce the costs of the rehabilitation, make it accessible for everybody and increase the motivation of the patients.

This report describes the implementation of a distributed system for an initially standalone application that is to be used in the medical sector. This new system aims to provide scalability and reliability.