Implementation of OpenCV in a Machine Vision System

In this thesis work, the general goal was to research and implement OpenCV into a machine vision as a part of a pilot study. The work was carried out by programming solutions for preselected real-world use-cases and problems using Python and C++ programming languages in different environments and setups as a part of a machine vision system. A central part of this thesis work was a technology review and selection between different versions of software while taking into account client needs and requirements. These selections were further proven by programming three use-cases in both of the pre-selected programming languages. The second goal of the programming was to detect any complications or bottlenecks in the implementations, these tests were all successfuly executed and proven. As a part of the detection for bottlenecks and performance issues, all the three use-cases were benchmarked, on basis of these benchmarks it was possible to make some preliminary assumptions on the performance of the scripts. The principles of the technology selection and programming along with the benchmarking results are presented as a part of this thesis within permitations of the NDA. As the last part of the work, a small calibration script was created in Python for calibrating the sensor system. The functionality of this script was tested in a real factory environment. With these tests, it was possible to verify the flawless functioning and suitability of the script in the selected usage and environments.Tässä insinöörityössä perehdytään OpenCV:n käyttöön osana konenäköjärjestelmää ja pilottihanketta. Työssä selvitettiin tosielämän käyttötapausten ja ongelmatilanteiden kautta OpenCV:n suorituskykyä sekä muuntautumiskykyä Python- ja C++ toteutuksien avulla erilaisissa ympäristöissä osana konenäköjärjestelmää. Työssä toteutettiin teknologiavertailu ja valinta eri käyttöympäristöjen sekä ohjelmistoversioiden välillä asiakkaiden vaatimukset ja tarpeet huomioiden. Nämä valinnat kyettiin perustelemaan toteuttamalla osana työtä toteuttamalla kolmeen eri käyttötapaukseen perustuvat ohjelmistoversiot kummallakin valituista kielistä. Ohjelmoinnin tarkoituksena oli myöskin pullokaulojen ja ongelmakohtien jäljittäminen sekä C++:n Pythonlaajennuksen testaus, näissä tehtävissä onnistuttiin kiitettävästi. Osana teknisiä valintaperusteita ja pullonkaulojen kartoitusta myös suoritettiin suorituskykyanalyysi eri ohjelmointikielillä tehtyjen toteutusten välillä joiden perusteella voitiin tehdä alustavia johtopäätöksiä suorituskyvyn suhteen. Testien ja ohjelmoinnin tulosten perusteella tehtyjä valintoja sekä suorituskykyanalyysin tuloksia esitellään tässä insinöörityössä salassapitosopimuksen sallimissa rajoissa. Työn viimeisenä osana toteutettiin sensorijärjestelmän kalibrointiin käytettävä pienimuotoinen ohjelmakoodi Pythonilla jonka toimivuutta testattiin tehdasympäristössä. Testien avulla kyettiin varmistamaan kalibrointikoodin ongelmaton toiminta sekä soveltuvuus valittuun käyttötarkoitukseen ja ympäristöihin

Daily Step Count and Incident Diabetes in Community-Dwelling 70-Year-Olds: A Prospective Cohort Study

Background: Older adults with diabetes take fewer steps per day than those without diabetes. The purpose of the present study was to investigate the association of daily step count with incident diabetes in community-dwelling 70-year-olds. Methods: This prospective cohort study included N = 3055 community-dwelling 70-year-olds (52% women) who participated in a health examination in Umeå, Sweden during 2012–2017, and who were free from diabetes at baseline. Daily step count was measured for 1 week using Actigraph GT3X+ accelerometers. Cases of diabetes were collected from the Swedish National Patient Register. The dose-response association was evaluated graphically using a flexible parametric model, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated using Cox regressions. Results: During a mean follow-up of 2.6 years, diabetes was diagnosed in 81 participants. There was an inverse nonlinear dose-response association between daily step count and incident diabetes, with a steep decline in risk of diabetes from a higher daily step count until around 6000 steps/day. From there, the risk decreased at a slower rate until it leveled off at around 8000 steps/day. A threshold of 4500 steps/day was found to best distinguish participants with the lowest risk of diabetes, where those taking ≥ 4500 steps/day, had 59% lower risk of diabetes, compared to those taking fewer steps (HR, 0.41, 95% CI, 0.25–0.66). Adjusting for visceral adipose tissue (VAT) attenuated the association (HR, 0.64, 95% CI, 0.38–1.06), which was marginally altered after further adjusting for sedentary time, education and other cardiometabolic risk factors and diseases (HR, 0.58, 95% CI, 0.32–1.05). Conclusions: A higher daily step count is associated with lower risk of incident diabetes in community-dwelling 70-year-olds. The greatest benefits occur at the lower end of the activity range, and much earlier than 10,000 steps/day. With the limitation of being an observational study, these findings suggest that promoting even a modest increase in daily step count may help to reduce the risk of diabetes in older adults. Because VAT appears to partly mediate the association, lifestyle interventions targeting diabetes should apart from promoting physical activity also aim to prevent and reduce central obesity

Wearable Technology Supported Home Rehabilitation Services in Rural Areas:– Emphasis on Monitoring Structures and Activities of Functional Capacity Handbook

The sustainability of modern healthcare systems is under threat. – the ageing of the population, the prevalence of chronic disease and a need to focus on wellness and preventative health management, in parallel with the treatment of disease, pose significant social and economic challenges. The current economic situation has made these issues more acute. Across Europe, healthcare expenditure is expected to rice to almost 16% of GDP by 2020. (OECD Health Statistics 2018). Coupled with a shortage of qualified personnel, European nations are facing increasing challenges in their ability to provide better-integrated and sustainable health and social services. The focus is currently shifting from treatment in a care center to prevention and health promotion outside the care institute. Improvements in technology offers one solution to innovate health care and meet demand at a low cost. New technology has the potential to decrease the need for hospitals and health stations (Lankila et al., 2016. In the future the use of new technologies – including health technologies, sensor technologies, digital media, mobile technology etc. - and digital services will dramatically increase interaction between healthcare personnel and customers (Deloitte Center for Health Solutions, 2015a; Deloitte Center for Health Solutions 2015b). Introduction of technology is expected to drive a change in healthcare delivery models and the relationship between patients and healthcare providers. Applications of wearable sensors are the most promising technology to aid health and social care providers deliver safe, more efficient and cost-effective care as well as improving people’s ability to self-manage their health and wellbeing, alert healthcare professionals to changes in their condition and support adherence to prescribed interventions. (Tedesco et al., 2017; Majumder et al., 2017). While it is true that wearable technology can change how healthcare is monitored and delivered, it is necessary to consider a few things when working towards the successful implementation of this new shift in health care. It raises challenges for the healthcare systems in how to implement these new technologies, and how the growing amount of information in clinical practice, integrates into the clinical workflows of healthcare providers. Future challenges for healthcare include how to use the developing technology in a way that will bring added value to healthcare professionals, healthcare organizations and patients without increasing the workload and cost of the healthcare services. For wearable technology developers, the challenge will be to develop solutions that can be easily integrated and used by healthcare professionals considering the existing constraints. This handbook summarizes key findings from clinical and laboratory-controlled demonstrator trials regarding wearables to assist rehabilitation professionals, who are planning the use of wearable sensors in rehabilitation processes. The handbook can also be used by those developing wearable sensor systems for clinical work and especially for use in hometype environments with specific emphasis on elderly patients, who are our major health care consumers

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all‐cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all‐cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free‐living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random Under‐ Sampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data‐driven and disease‐agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness

The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration

Background: Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. Objective: This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods: This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results: Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions: Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease-related health barriers and the users' confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management

Remote Rehabilitation: A solution to Overloaded & Scarce Health Care Systems

The population across Northern Europe is aging. Coupled with socio-economic challenges, health care systems are at risk of overloading and incurring unsustainable high costs. Rehabilitation services are used disproportionately by older people. One solution pertinent to rural areas is to change the model of rehabilitation to incorporate new technologies. This has the potential to free resources and reduce costs. However, implementation is challenging. In the Northern Periphery and Artic Programme (NPA), the Smart sensor Devices for rehabilitation and Connected health (SENDoc) project [1] is focused on introducing wearable sensor systems among elderly communities to support their rehabilitation. It is important to understand the context into which change is introduced. Therefore, an overview of the current state of health care systems in the four partner countries is presented, defining the concept of rehabilitation and how remote rehabilitation is currently delivered. Advantages (e.g. enhanced outcomes, less cost and enhanced patient engagement), and disadvantages of remote rehabilitation (e.g. complexity involved in the use of technology, design and safety issues) are discussed. It is concluded that the key advantage of remote rehabilitation is the potential to support change in patient behaviour, empowering active participation and living independently, with less need to travel for face-to-face sessions. Remote rehabilitation can make enhance quality of health care service delivery. However, all relevant stakeholders including medical staff and patients should be included in the design of the technology employed with a focus on simplicity, usability and robustness. Compliance with Security and the new GDPR regulation will be key to supporting remote rehabilitation. In addition, the diversity of available platforms and devices must also be supported to ensure interoperability. Finally, remote rehabilitation needs to be further validated in practice. Attempts to implement and sustain change should be cognisant of local and current organization of health care and of existing enablers and barriers

Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings