Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

Physical activity and sedentary behaviour across the spectrum of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) patients are generally more sedentary and less physically active than healthy adults; putting them at increased risk of hospitalisation and death. For patients with mild-moderate COPD, physical activity appears to be reduced compared with apparently healthy adults but differences in time spent sedentary are less well established. Additionally, there is a need for a greater understanding of the correlates of behaviour in mild-moderate patients with much of the existing literature focusing on more severe or mixed stage patient samples and with many studies lacking objective behavioural monitoring, not adjusting for confounders and a paucity of data on correlates of sedentary time. Despite having mild-moderate airflow obstruction, these patients also report a range of symptom burdens with some individuals reporting severe symptoms. Subsequently, these patients represent a sub-set of individuals who may require lifestyle interventions. Therefore, factors associated with patients reporting more severe symptoms need to be identified to help understand how this phenomenon may manifest and be intervened upon. For patients with more advanced COPD who are admitted to hospital for an acute exacerbation behavioural intervention focussing on less intense movement may be a more suitable approach for reducing the risk of readmissions than more intense physical activity or exercise. To date no studies have specifically targeted reductions in sedentary behaviour in COPD. In addition, wearable self-monitoring technology may facilitate the provision of such interventions, removing important participation barriers such as travel and cost, but this has not been sufficiently examined in COPD. This thesis investigated: (i) objectively measured physical activity and sedentary time and the correlates of these behaviours for mild-moderate COPD patients and apparently healthy adults (Study One); (ii) factors associated with self-reported symptom severity and exacerbation history in mild-moderate COPD patients (Study Two) and (iii) the feasibility and acceptability of a home-based sedentary behaviour intervention using wearable self-monitoring technology for COPD patients following an acute exacerbation (Study Three). Methods: Study One: COPD patients were recruited from general practitioners and apparently healthy adults from community advertisements. Objectively measured moderate-to-vigorous physical activity (MVPA), light activity and sedentary time for 109 mild-moderate COPD patients and 135 apparently healthy adults were obtained by wrist-worn accelerometry. Patients with at least four valid days (≥10 waking hours) out of a possible seven were included in analysis. A range of demographic, social, symptom-based, general health and physical factors were examined in relation to physical activity and sedentary time using correlations and linear regressions controlling for confounders (age, gender, smoking status, employment status and accelerometer waking wear time). Study Two: In 107 patients recruited from general practitioners, symptoms were assessed using the COPD Assessment Test (CAT) and Modified Medical Research Council (mMRC) questionnaires. Twelve-month exacerbation history was self-reported. Exercise capacity was assessed via incremental shuttle walk test (ISWT) and self-reported usual walking speed. Physical activity and sedentary time were obtained from a wrist-worn accelerometer. Study Three: Patients were randomised in-hospital into a usual care (Control), Education or Education + Feedback group with the intervention lasting 14 days following discharge. The intervention groups received information about reducing prolonged sitting. The Education + Feedback group also received real-time feedback on their sitting time, number of stand-ups and step count at home through an inclinometer linked to a smart device app. The inclinometer also provided vibration prompts to encourage movement when the wearer had been sedentary for too long. Feasibility of recruitment (e.g. uptake and retention) and intervention delivery (e.g. fidelity) were assessed. Acceptability of the intervention technology (e.g. wear compliance, app usage and response to vibration prompts) was also examined. Results: Study One: COPD patients were more sedentary (592±90 versus 514±93 minutes per day, p20 or an mMRC score of ≥2 had lower VMCPM, were more sedentary and took part in less light activity than patients reporting a CAT score of 0-10 or mMRC of 0, respectively. Patients reporting ≥2 exacerbations took part in less MVPA than patients reporting zero exacerbations. Study Three: Study uptake was 31.5% providing a final sample of 33 COPD patients. Retention of patients at two-week follow-up was 51.5% (n=17). Reasons for drop-out were mostly related to being unable to cope with their COPD. Patients wore the inclinometer for 11.8±2.3 days (and charged it 8.4±3.9 times) with at least one vibration prompt occurring on 9.0±3.4 days over the 14 day study period. Overall, 325 vibration prompts occurred with patients responding 106 times (32.6%). 40.6% of responses occurred within 5 minutes of the prompt with patients spending 1.4±0.8 minutes standing and 0.4±0.3 minutes walking, taking 21.2±11.0 steps. Discussion: Study One: COPD patients were less active and more sedentary than apparently healthy adults; however, factors predicting behaviour were similar between groups. Correlates differed between sedentary time, light activity and MVPA for both groups. Interventions to boost physical activity levels and reduce sedentary time should be offered to patients with mild-moderate COPD, particularly those reporting more severe breathlessness. Study Two: Worse exercise capacity, low levels of physical activity and more time spent sedentary are some of the factors associated with patients of the same severity of airflow limitation reporting differing symptom severities. These patients may benefit from both lifestyle and exercise interventions. Study Three: Recruitment and retention rates suggest a trial targeting sedentary behaviour in hospitalised COPD patients is feasible. A revised intervention, building on the successful components of the present feasibility study is justified. Conclusion: The findings from this thesis have contributed a greater understanding of physical activity and sedentary behaviour in COPD and can inform the development of tailored physical activity and sedentary behaviour interventions for patients across the grades of COPD severity

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Methodology for detecting movements of interest in elderly people

RESUMEN: El aumento en la expectativa de vida, tanto en Colombia como a nivel mundial, requiere un mayor uso de tecnologías dentro del área de la salud que permita a los adultos mayores conservar su independencia y mejorar su calidad de vida. En esta tesis se analiza la problemática de caídas en adultos mayores independientes, cuyas consecuencias pueden minimizarse mediante un sistema portable de detección automática que envíe una alarma de forma oportuna. Como punto de partida se elaboró una base de datos con 38 participantes que realizaron 19 actividades de la vida diaria y simularon 15 tipos de caídas. Para ello se utilizó un dispositivo portable con un acelerómetro triaxial. Pruebas preliminares con algoritmos de extracción de características comúnmente usados en la literatura para discriminar entre caídas y actividades de la vida diaria presentaron una precisión de hasta 96%. Para ello se utilizó un clasificador de bajo costo computacional basado en umbral que pudiese funcionar en tiempo real en sistemas embebidos. Un análisis individual de actividades con cada uno de los algoritmos de extracción de características demostró que algunas de ellas son complementarias entre sí, este análisis se usó como punto de partida para desarrollar métricas no lineales que mejoraron la discriminación a un 99%. También se observó que muchos de los falsos positivos son debidos a actividades periódicas de alta aceleración, que pudieron ser detectados a partir de su periodo. Con el fin de garantizar que la metodología desarrollada fuese implementable en sistemas embebidos sin que ello signifique una alta carga computacional (y el consecuente consumo de batería), en este trabajo se propone un algoritmo basado en un filtro de Kalman, un pre procesamiento basado en un filtro Butterworth de cuarto orden, una métrica no lineal basada en dos características de extracción comúnmente usadas, y un clasificador basado en umbral. Este algoritmo fue implementado en un dispostivo embebido y validado mediante la simulación de las mismas actividades de la base de datos adquirida en este trabajo, además de una prueba piloto en condiciones reales con adultos mayores. Ambas pruebas presentaron una tasa de error inferior al 1%.ABSTRACT: The increase in life expectancy, both in Colombia and globally, requires higher use of healthcare technology to allow elderly adults maintain their independence and improve their quality of life. In this thesis, we analyze the problem of falls in independent elderly people. The consequences of a fall can be minimized by a portable automatic detection system, wich sends an alarm right after an event. We started by creating a dataset with 38 participants that conducted 19 activities of daily life and simulated 15 types of falls. They used a portable device with a triaxial accelerometer. Preliminary tests with feature extraction algorithms commonly used in the literature to discriminate between falls and activities of daily living presented up to 96% of accuracy. They were implemented with a low computational cost threshold-based classifier, which can operate in real-time on embedded systems. An individual activity analysis with each feature extraction algorithm demonstrated that some of them are complementary to each other. This analysis was used as a starting point to develop nonlinear discrimination metrics that improved the accuracy to 99%. We also noted that most false positives are due to high acceleration periodic activities, and we could detect them solely based on their period. In order to guarantee that the developed methodology can be implemented on embedded systems without affecting their computational capability (and the consequent battery consumption), we propose an algorithm based on a Kalman filter, with a pre-processing stage based on a 4-th order Butterworth filter, a non-linear feature based in two commonly used feature extraction characteristics, and a threshold-based classifier. This algorithm was implemented in an embedded device and validated by simulating the same activities of the dataset acquired in this work, along with a pilot test in real conditions with elderly adults. Both tests presented an error rate below 1%