ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Even after rehabilitation, patients with lower-limb amputation may continue to exhibit suboptimal gait. A wireless telemetry system, featuring force sensors, accelerometers, control electronics and a Bluetooth transmission module was developed to measure plantar pressure information and remotely monitor patient mobility. Plantar pressure characterization studies were performed to determine the optimal sensor placement. Finally, the wireless telemetry system was integrated with a previously developed haptic feedback system in order to allow remote monitoring of patient mobility during haptic system validation trials.

Vital signs can indicate patient deterioration prior to adverse events such as cardiac arrest, emergency admission to the intensive care unit (ICU), or death. However, many adverse events occur in wards outside the ICU where the level of care and the frequency of patient monitoring are lower than in the ICU. This thesis describes models for detection of deterioration in acutely ill patients in two environments: a step-down unit in which patients recovering from an ICU stay are continuously monitored, and a general ward where patients are intermittently monitored following upper gastrointestinal cancer surgery. Existing data fusion models for classification of vital signs depend on a threshold which defines a “region of normality”. Bradypnoea (low breathing rate) and bradycardia (low heart rate) are relatively rare, and so these two types of abnormalities tend to be misclassified by existing methods. In this thesis, techniques for selecting a threshold are described, such that the classification of vital-sign data is improved. In particular, the proposed approach reduces the misclassification of bradycardia and bradypnoea events, and indicates the type of abnormality associated with the deterioration in a patient’s vital signs. Patients recovering from upper gastrointestinal (GI) surgery have a high risk of emergency admission to the ICU. At present in the UK, most intermediate and general wards outside the ICU depend on intermittent, manual monitoring using track-and-trigger systems. Both manual and automated patient monitoring systems are reported to have high false alert rates. The models described in this thesis take into account the low monitoring frequency in the upper GI ward, such that the false alert rate is reduced. In addition to accuracy, early detection of deterioration is a highly desirable feature in patient monitoring systems. The models proposed in this thesis generate alerts for patients earlier than the early warning systems which are currently in use in hospitals in the UK. The improvements to existing models proposed in this thesis could be applied to continuous and intermittently acquired vital-sign data from other clinical environments.

Wearable sensor systems composed of small and light sensing nodes have the potential to revolutionise healthcare. While uptake has increased over time in a variety of application areas, it has been slowed by problems such as lack of infrastructure and the functional capabilities of the systems themselves. An important application of wearable sensors is the detection of falls, particularly for elderly or otherwise vulnerable people. However, existing solutions do not provide the detection accuracy required for the technology to gain the trust of medical professionals. This thesis aims to improve the state of the art in automated human fall detection algorithms through the use of a machine learning based algorithm combined with novel data annotation and feature extraction methods. Most wearable fall detection algorithms are based on thresholds set by observational analysis for various fall types. However, such algorithms do not generalise well for unseen datasets. This has thus led to many fall detection systems with claims of high performance but with high rates of False Positive and False Negative when evaluated on unseen datasets. A more appropriate approach, as proposed in this thesis, is a machine learning based algorithm for fall detection. The work in this thesis uses a C4.5 Decision Tree algorithm and computes input features based on three fall stages: pre-impact, impact and post-impact. By computing features based on these three fall stages, the fall detection algorithm can learn patterns unique to falls. In total, thirteen features were selected across the three fall stages out of an original set of twenty-eight features. Further to the identification of fall stages and selection of appropriate features, an annotation technique named micro-annotation is proposed that resolves annotation-related ambiguities in the evaluation of fall detection algorithms. Further analysis on factors that can impact the performance of a machine learning based algorithm were investigated. The analysis defines a design space which serves as a guideline for a machine learning based fall detection algorithm. The factors investigated include sampling frequency, the number of subjects used for training, and sensor location. The optimal values were found to be10Hz, 10 training subjects, and a single sensor mounted on the chest. Protocols for falls and Activities of Daily Living (ADL) were designed such that the developed algorithms are able to cope under a variety of real world activities and events. A total of 50 subjects were recruited to participate in the data gathering exercise. Four common types of falls in the sagittal and coronal planes were simulated by the volunteers; and falls in the sagittal plane were additionally induced by applying a lateral force to blindfolded volunteers. The algorithm was evaluated based on leave one subject out cross validation in order to determine its ability to generalise to unseen subjects. The current state of the art in the literature shows fall detectors with an F-measure below 90%. The commercial Tynetec fall detector provided an F-measure of only 50% when evaluated here. Overall, the fall detection algorithm using the proposed micro-annotation technique and fall stage features provides an F-measure of 93% at 10Hz, exceeding the performance provided by the current state of the art.

Dialysis patients are operated to have AV Fistula which is a joint junction of an artery and vein in the arm, operated to increase the blood flow through the dialyzer machine. AV- fistula is a type of vascular access which is a path into the body to connect/disconnect devices, but in this case, it is mainly Dialyzer. To reduce the failure rate during maturation period of AV Fistula, doctors recommend squeezing ball exercise as a necessary precaution for AV Fistula failure. Doing Squeezable interaction for about 3-4 times a day is recommended based on patient’s health condition. Hence, the proposed architecture adopts this squeezable exercise by embedding with sensor and measuring the angle at which the sensor is bent. The framework also proposes a new care coordination system having the hardware layer which has key components such as raspberry Pi, sensor which help in recording the pressure values when user presses the ball and software layer which solely focuses on data sync among the applications used by the user. It has been recorded that 53 % of patients having AV-Fistula fail because of negligence and lack of care. The maturation period is so critical and important which made us to build a gamification platform to monitor the exercise and track the activity through android application to keep users motivated and disciplined. In further chapters of the study will focus on different clinical like procedure around AV-Fistula and technical information such as different technologies used and implemented in the proposed system along with sensor circuit. This project goal is to present a way of monitoring patients and to keep track of the compliance whether the patient is active doing exercise daily. This way we are trying to present a care monitoring system for patients to help prevent AV Fistula failure. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Bibliography: leaves 91-93.

Methods to improve self-care and clinical management of heart failure are required, especially in light of the anticipated increase in heart failure prevalence and associated high costs. Remote patient monitoring (RPM) has been shown to improve heart failure outcomes, but the feasibility and efficacy of mobile phone-based RPM systems are still unknown. The main objectives of this research were to investigate the optimal design of a mobile phone-based RPM system, and to determine the effects of the system on self-care, clinical management, and health outcomes. A mobile phone-based RPM system was first developed using a user-centric design process. It was then evaluated with a six-month randomized controlled trial consisting of 100 patients attending a heart function clinic. The quality of life improved only for the intervention group, but both intervention and control groups improved with respect to self-care, heart function, and heart failure prognosis. The clinic was determined to be a confounder. Patients who were enrolled into the clinic for less than six months showed significantly greater improvements (six months is required for patients to stabilize from medication optimization). Therefore, a subgroup analysis using data from the 63 patients who were enrolled into the clinic for over six months at time of recruitment was performed. The results from the subgroup analysis indicated that the RPM system improved self-care, heart function, and heart failure prognosis at statistically significant and clinically meaningful levels. These improvements were found to be a result of enhanced self-care knowledge and practices, as well as enhanced clinical management enabled by the system. No differences in mortality or hospital admissions were found between groups, but the trial was underpowered to detect changes in these outcome measures. In summary, mobile phone-based RPM was found to be a feasible and effective tool to help improve heart failure management and outcomes.

Methods to improve self-care and clinical management of heart failure are required, especially in light of the anticipated increase in heart failure prevalence and associated high costs. Remote patient monitoring (RPM) has been shown to improve heart failure outcomes, but the feasibility and efficacy of mobile phone-based RPM systems are still unknown. The main objectives of this research were to investigate the optimal design of a mobile phone-based RPM system, and to determine the effects of the system on self-care, clinical management, and health outcomes. A mobile phone-based RPM system was first developed using a user-centric design process. It was then evaluated with a six-month randomized controlled trial consisting of 100 patients attending a heart function clinic. The quality of life improved only for the intervention group, but both intervention and control groups improved with respect to self-care, heart function, and heart failure prognosis. The clinic was determined to be a confounder. Patients who were enrolled into the clinic for less than six months showed significantly greater improvements (six months is required for patients to stabilize from medication optimization). Therefore, a subgroup analysis using data from the 63 patients who were enrolled into the clinic for over six months at time of recruitment was performed. The results from the subgroup analysis indicated that the RPM system improved self-care, heart function, and heart failure prognosis at statistically significant and clinically meaningful levels. These improvements were found to be a result of enhanced self-care knowledge and practices, as well as enhanced clinical management enabled by the system. No differences in mortality or hospital admissions were found between groups, but the trial was underpowered to detect changes in these outcome measures. In summary, mobile phone-based RPM was found to be a feasible and effective tool to help improve heart failure management and outcomes.

Thesis (M.S.)--California Polytechnic State University, 2010. / Title from PDF title page; viewed on February 18, 2010. Major professor: Lily Laiho, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Engineering, with Specializations in Biomedical Engineering." "January 2010." Includes bibliographical references (p. 40-42).

published_or_final_version / Surgery / Master / Master of Philosophy

Thesis (Ph. D.)--University of Canterbury, 2008. / Typescript (photocopy). "11 April 2008." Includes bibliographical references (p. 245-260).