In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.
Health care costs have increased over the last decades due to an ageing population. Therefore, research in personal health monitoring (PHM) has increased in response to this. PHM has advantages such as mobility (monitoring of health at work or at home), early detection of health problems enabling preventive health measures and a reduction of health care cost. Human motion analysis, using for example inertial measurement units and pedobarography, is an important subcategory of PHM. Pedobarography (PBG) is the study of pressure fields acting between the plantar surface of the foot and a supporting surface. Gait and posture analysis, prosthetics evaluation and monitoring of recovery from injury or disease are examples of PBG applications. Portable PBG can be performed using force sensing resistors built into the insole inside the shoe.
In accordance with this, the research goal for this licentiate thesis is to design, build and evaluate a wireless wearable measurement system based on pedobarography for monitoring of health. In order to fulfil the objectives of the research, literature studies were done and problems with existing in-shoe system solutions were identified. Thus, it was found that further opportunities existed for new designs of PBG systems which take these problems into account. Cross-sectional test case studies were used for validation. The research area is multidisciplinary and encompasses biomedical measurements, electronics and computer science.
The main research contributions include design and implementation of a PBG measurement system consisting of commercial off the shelf components, a novel method for selecting measurement samples for weight estimation of carried load during walk, and a novel method for analysing walking intensity using force-time integrals at the toe-off phase of the step. The research results suggest that the new PBG system, in combination with the two novel analysing methods, are suitable for use in wearable systems for monitoring of health. Personal health measurements are done to help decision making related to health. Thus, the future work will strive towards designing different decision support systems.
This study explores the role of science and technology parks (STPs) as facilitators in the creation of an innovation-advising organization network (IAON) within the health, social welfare, and medical technology sector (health-tech). The background to this study is that more and more products using Industry 4.0 technology, i.e., robotics, artificial intelligence, and Internet of Things products, are being developed for the health-tech sector, leading to safer and more secure and cost-effective healthcare and social welfare. The challenge, however, as identified by an STP, is that the innovation-advising organizations are not collaborating efficiently, which results in scattered advise to the small and medium-sized enterprise/startups (SME) and an insufficient understanding of each other’s work when advising the same SME. In this study, the STP, successfully, initiated and facilitated the work to develop an IAON. Similar initiatives are suggested. Because challenges and success factors were identified, further studies are also recommended.
This study explores science and technology parks’ (STP) role as facilitators when an innovation advising organization network (IAON) within the health, social welfare, and medical technology sector (health-tech) develops a quality-assured innovation advising process (QAIP). The background is that more and more products using Industry 4.0 technology are being developed for the health-tech sector. The challenge, however, as recognized by an STP, is that the IAON’s members use individual innovation advise processes, which results in inaccurate advice to the SME and insufficient collaboration between the advising organizations when advising the same SME. In this study, one STP initiated and facilitated the work to develop a QAIP, which was developed stepwise through a series of workshops in an iterative process. This study indicates that STPs may play an active part in supporting IAONs developing specialized innovation processes for certain business and technology areas, although further studies are suggested.
This study explores what role science and technology parks (STP) can play when aiming to increase knowledge regarding the implementation of products for the health, social welfare, and medical technology sector (health-tech), including Industry 4.0 technology-based products (products), on a national level in Sweden. Such products, have problems reaching the market due to increased complexity from a purchase perspective. In this study, an STP developed a guideline on implementation-advice based on user-needs, literature studies, and focus groups. The guideline was presented at a seminar, where representatives from all regions in Sweden attended. The STP initiated the project, developed the guideline, and disseminated it successfully at a national level. The guideline was evaluated six months after the seminar, indicating that it created value in practice. This indicates that STPs could take an active role supporting products in the health-tech sector. New seminars are planned, and further studies are suggested.
Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.
The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.
Eight ECG beat detection algorithms, from the PhysioNet's WFDB and Cardiovascular Signal toolboxes, were tested on twenty measurements, obtained by the Savvy patch ECG device, for their accuracy in beat detection. On each subject, one measurement is obtained while sitting and one while running. Each measurement lasted from thirty seconds to one minute. The measurements obtained while running were more challenging for all the algorithms, as most of them almost perfectly detected all the beats on the measurements obtained in sitting position. However, when applied on the measurements obtained while running, all the algorithms have performed with decreased accuracy. Considering overall percentage of the faulty detected peaks, the four best algorithms were jqrs, from the Cardiovascular Signal Toolbox, and ecgpuwave, gqrs, and wqrs, from the WFDB Toolbox, with percentages of faulty detected beats 1.7, 2.3, 2.9, and 3, respectively.
The atrial irregular rhythm, often reflected in atrial fibrillation, undulation or flutter, is recognized as one of the major causes of brain stroke and entails an increased risk of thromboembolic events because it increases the likelihood of blood clots formation. Its early detection is becoming an increasingly important preventive measure. The paper presents a simple methodology for the detection of atrial irregular rhythm by ECG body gadget that can perform long-term measurements, e.g. several weeks or more. Multichannel ECG, on the body surface, gives a more detailed insight into the atrial activity in comparison to standard 12-lead ECG. The information from MECG is compared with single-channel patch ECG. The obtained results suggest that the proposed methodology could be useful in treatments of atrial irregular rhythm. One can obtain a reliable information about the time and duration of fibrillation events, or determine arrhythmic focuses and conductive pathways in heart atria, or study the effects of antiarrhythmic drugs on existing arrhythmias and on an eventual development of new types of arrhythmias.