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Zhang B.,Shanghai University | Jiang S.,Shanghai University | Wei D.,Shanghai University | Marschollek M.,Peter L Reichertz Institute For Medical Informatics | Zhang W.,Shanghai University
Proceedings - 2012 IEEE/ACIS 11th International Conference on Computer and Information Science, ICIS 2012 | Year: 2012

The ability to measure human movement accurately forms an essential part of a clinical assessment, thus allowing the efficacy of therapeutic interventions to be determined. The most commonly used clinical method for assessing human movement is goniometry, motion capture system, Electromagnetic tracking systems and so on. There are some essential limitations in each method. Inertial sensors such as accelerometers, magnetometers and gyroscopes have the potential to be used for assessing human movement in various environments. Numerous studies have reported using systems based on accelerometers or gyroscopes. These two types of sensors may also be combined to study human motions with good accuracy. The present study employed an inertial system which utilized such fusion technology which is reported to provide motion data with better accuracy. This paper reviews walking analysis using wireless inertial sensors currently, analyzes the features of this methods comparing with other methods, and indicates the key problem and the future direction and application domains. © 2012 IEEE.


Marschollek M.,Peter L Reichertz Institute For Medical Informatics
PLoS ONE | Year: 2013

Background: Physical activity is inversely correlated to morbidity and mortality risk. Large cohort studies use wearable accelerometer devices to measure physical activity objectively, providing data potentially relevant to identify different activity patterns and to correlate these to health-related outcome measures. A method to compute relevant characteristics of such data not only with regard to duration and intensity, but also to regularity of activity events, is necessary. The aims of this paper are to propose a new method - the ATLAS index (Activity Types from Long-term Accelerometric Sensor data) - to derive generic measures for distinguishing different characteristic activity phenotypes from accelerometer data, to propose a comprehensive graphical representation, and to conduct a proof-of-concept with long-term measurements from different devices and cohorts. Methods: The ATLAS index consists of the three dimensions regularity (reg), duration (dur) and intensity (int) of relevant activity events identified in long-term accelerometer data. It can be regarded as a 3D vector and represented in a 3D cube graph. 12 exemplary data sets of three different cohort studies with 99,467 minutes of data were chosen for concept validation. Results: Five archetypical activity types are proposed along with their dimensional characteristics (insufficiently active: low reg, int and dur; busy bee: low dur and int, high reg; cardio-active: medium reg, int and dur, endurance athlete: high reg, int and dur; and weekend warrior: high int and dur, low reg). The data sets are displayed in one common graph, indicating characteristic differences in activity patterns. Conclusion: The ATLAS index incorporates the relevant regularity dimension apart from the widely-used measures of duration and intensity. Along with the 3D representation, it allows to compare different activity types in cohort study populations, both visually and computationally using vector distance measures. Further research is necessary to validate the ATLAS index in order to find normative values and group centroids. © 2013 Marschollek.


Marschollek M.,Peter L Reichertz Institute For Medical Informatics | Rehwald A.,Peter L Reichertz Institute For Medical Informatics | Wolf K.-H.,TU Braunschweig | Gietzelt M.,TU Braunschweig | And 3 more authors.
BMC Medical Informatics and Decision Making | Year: 2011

Background: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. Methods. In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Results: Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Conclusions: Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach. © 2011 Marschollek et al; licensee BioMed Central Ltd.


Marschollek M.,Peter L Reichertz Institute For Medical Informatics
Human Movement Science | Year: 2016

Objectives: Wearable actimetry devices are used increasingly in cohort and cross-sectional studies to assess physical activity (PA) behaviour objectively. Thus far, the medical relevance of distinct PA groups, as identified by using new methods of sensor data analysis, remains unclear. The objective of this research paper is to evaluate whether such PA groups differ in commonly accepted health risk parameters. Methods: PA sensor data and corresponding outcome data of the NHANES 2005-06 study were obtained. Data pre-processing included elimination of potential outliers, data splitting and the computation of PA parameters, including a novel regularity measure. PA groups were identified using the x-Means clustering algorithm, and groups were evaluated for differences in CRP, BMI and HDL. Results: Data sets of 7334 NHANES participants were analysed, and four distinct PA groups were identified. Statistically significant group differences were found for CRP and BMI (p<. 0.001), but not for HDL (p=. 0.67). Conclusions: PA groups derived from objective accelerometer mass data differ in exemplary health-related outcome parameters. The novel PA regularity measure is of particular interest and may become part of future PA assessments, especially when regarding low-intensity, short-lived PA events. Further research in pattern recognition methods and analytic algorithms for PA data from current multi-sensing devices is necessary. © 2015 Elsevier B.V.


Haux R.,Peter L Reichertz Institute For Medical Informatics
Studies in Health Technology and Informatics | Year: 2010

This text was originally published in Australia in 1996. Since then, the world has changed significantly. The emergence of the Internet and World Wide Web with its enormous possibilities had just begun, standardisation was in its infancy, broadband was unheard of, we had just started thinking about the Y2K bug, supply chain management was more theory than practice, Google wasn't even founded, nor would anybody have had dreams or nightmares about Google Health or Microsoft HealthVault to store your personal health information and make it accessible when needed. To say it with the words of Thomas Friedman [1], since 1996, the world has been flattened in the sense that many people have been empowered significantly and now have a far more equal opportunity to achieve, create, collaborate and compete with each other than used to be the case, in healthcare as well as in any other business. Thus, this second edition has been extensively reviewed, updated and a number of new topics have been included in order to meet contemporary issues and challenges. The text has a strong focus on health viewed from a computing perspective. It was compiled primarily for health professionals who now require knowledge about how these new technologies of information and communication may be used to enhance their practice. It aims to provide an overview of the health informatics discipline. The contents reflect what we consider are the basics for continuing education purposes and for inclusion into any curriculum which prepares a student for practice in any of the health professional disciplines. It is suitable for use as a basic text in both undergraduate and post graduate curricula. Each chapter can be expanded upon as required. Guidelines for health informatics education are provided in the last few chapters of this text. This text is not all inclusive or exhaustive; most of the chapters could be expanded individually into a book on its own. This text deliberately avoids a focus on any one of the health professions. Health care has become more and more integrated between the various sectors ranging from primary care to hospitals, as well as becoming more interdisciplinary between the various health professions. Also there is a trend to empowering the patient to play a more active part in decision making. All this requires clinical information to be available across sectors and across professions and necessitates integrated clinical (computer) systems such as 'professional' or 'clinician' workstations that support the focus on the patient as the centre of care rather than a discipline or departmental focus. Clinical data from multiple sources are integrated and support multiple types of clinical decision making. This also has implications for the language or terminology used and may well influence changes in how individuals practice their profession at the point of care. The book is divided into six sections, an overview of the discipline, basic health informatics concepts, the application of health informatics supporting clinical practice, health care service delivery management, clinical research and health informatics education. We first present the history of computing in health followed by an overview of the discipline and outline some of the basic principles underlying this health discipline, including the need to balance the technology with our underlying commitment to patient care. In section two we discuss the basic concepts which need to be grasped about computing and explain how these apply to the health professions to best meet the needs as detailed in section 1. The next four sections demonstrate how these new technologies can assist our daily work, in clinical practice, management, education and research enabling us to realize our global e-health vision. We thank the Spanish language editorial team, Carola Hullin Lucay Cossio, Erika Caballero Muñoz, Lorena Camus, Alejandro Gigoux Múller, Antonio Jose Ibarra Fernandez, and Maria Pilar Marin Villasante who managed the translation process prior to this book's publication by Mediterraneo, Santiago, Chile. © 2010 The authors and IOS Press. All rights reserved.


Marschollek M.,Peter L Reichertz Institute For Medical Informatics
BMC Medical Informatics and Decision Making | Year: 2012

Background: Demographic change with its consequences of an aging society and an increase in the demand for care in the home environment has triggered intensive research activities in sensor devices and smart home technologies. While many advanced technologies are already available, there is still a lack of decision support systems (DSS) for the interpretation of data generated in home environments. The aim of the research for this paper is to present the state-of-the-art in DSS for these data, to define characteristic properties of such systems, and to define the requirements for successful home care DSS implementations. Methods: A literature review was performed along with the analysis of cross-references. Characteristic properties are proposed and requirements are derived from the available body of literature. Results: 79 papers were identified and analyzed, of which 20 describe implementations of decision components. Most authors mention server-based decision support components, but only few papers provide details about the system architecture or the knowledge base. A list of requirements derived from the analysis is presented. Among the primary drawbacks of current systems are the missing integration of DSS in current health information system architectures including interfaces, the missing agreement among developers with regard to the formalization and customization of medical knowledge and a lack of intelligent algorithms to interpret data from multiple sources including clinical application systems. Conclusions: Future research needs to address these issues in order to provide useful information and not only large amounts of data for both the patient and the caregiver. Furthermore, there is a need for outcome studies allowing for identifying successful implementation concepts. © 2012 Marschollek.; licensee BioMed Central Ltd.


Marschollek M.,Peter L Reichertz Institute For Medical Informatics
Journal of medical systems | Year: 2016

The increasing use of wearable actimetry devices in cohort studies can provide a deep and objective insight in physical activity (PA) patterns. For reliable and reproducible pattern recognition, and to minimize the influence of specific device characteristics, there is a need for a generic method to identify relevant PA events in sensor data sets on the basis of comprehensive features such as PA duration and intensity. The objectives of this paper are to present a method to identify universal event detection thresholds for such parameters, and to attempt to find stable meta-clusters of PA behaviour. PA events of 5, 10, 20 and 30 min with low, medium and high intensity thresholds found in literature and intensity deciles were computed for a random sample (N = 100) of the NHANES 2005-06 accelerometer data set (N = 7457). On the basis of all combinations of the above, activity events were detected, and parameters mean duration, mean intensity and event regularity were computed. Results were clustered using x-Means clustering and visualized for 5-, 10-, 20-, and 30-min events. Stable clustering results are obtained with intensity thresholds up to the 8th decile and for event durations up to 10 min. Two stable meta-clusters were detected: 'irregularly active' (intensity at 52nd percentile) and 'regularly active' (intensity at 42nd percentile). Distinct generic thresholds could be identified and are proposed. They may prove useful for further investigations of similar actimetry data sets, minimising the influence of specific device characteristics. The results also confirm that distinct PA event patterns - including event regularity - can be identified using wearable sensor devices, especially when regarding low-intensity, short-term activities which do not correspond to current PA recommendations. Further research is necessary to evaluate actual associations between sensor-based PA parameters and health outcome. The author identified generic intensity and duration thresholds for analysing objective PA data from wearable devices. This may contribute to further analyses of PA patterns along with their relations with health outcome parameters.


Schulze M.,Peter L Reichertz Institute For Medical Informatics
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

Patients suffering from end-stage knee osteoarthritis are often treated with total knee arthroplasty, improving their functional mobility. A number of patients, however, report continued difficulty with stair ascent and descent or sportive activity after surgery and are not completely satisfied with the outcome. State-of-the-art analyses to evaluate the outcome and mobility after knee replacement are conducted under supervised settings in specialized gait labs and thus can only reflect a short period of time. A number of external factors may lead to artificial gait patterns in patients. Moreover, clinically relevant situations are difficult to simulate in a stationary gait lab. In contrast to this, inertial sensors may be used additionally for unobtrusive gait monitoring. However, recent notable approaches found in literature concerning knee function analysis have so far not been applied in a clinical context and have therefore not yet been validated in a clinical setting. The aim of this paper is to present a system for unsupervised long-term monitoring of human gait with a focus on knee joint function, which is applicable in patients' everyday lives and to report on the validation of this system gathered during walking with reference to state-of-the-art gait lab data using a vision system (VICON Motion System). The system KINEMATICWEAR - developed in close collaboration of computer scientists and physicians performing knee arthroplasty - consists of two sensor nodes with combined tri-axial accelerometer, gyroscope and magnetometer to be worn under normal trousers. Reliability of the system is shown in the results. An overall correlation of 0.99 (with an overall RMSE of 2.72) compared to the state-of-the-art reference system indicates a sound quality and a high degree of correspondence. KINEMATICWEAR enables ambulatory, unconstrained measurements of knee function outside a supervised lab inspection.


PubMed | Peter L Reichertz Institute For Medical Informatics
Type: Journal Article | Journal: Journal of medical systems | Year: 2015

The increasing use of wearable actimetry devices in cohort studies can provide a deep and objective insight in physical activity (PA) patterns. For reliable and reproducible pattern recognition, and to minimize the influence of specific device characteristics, there is a need for a generic method to identify relevant PA events in sensor data sets on the basis of comprehensive features such as PA duration and intensity. The objectives of this paper are to present a method to identify universal event detection thresholds for such parameters, and to attempt to find stable meta-clusters of PA behaviour. PA events of 5, 10, 20 and 30 min with low, medium and high intensity thresholds found in literature and intensity deciles were computed for a random sample (N=100) of the NHANES 2005-06 accelerometer data set (N=7457). On the basis of all combinations of the above, activity events were detected, and parameters mean duration, mean intensity and event regularity were computed. Results were clustered using x-Means clustering and visualized for 5-, 10-, 20-, and 30-min events. Stable clustering results are obtained with intensity thresholds up to the 8th decile and for event durations up to 10 min. Two stable meta-clusters were detected: irregularly active (intensity at 52nd percentile) and regularly active (intensity at 42nd percentile). Distinct generic thresholds could be identified and are proposed. They may prove useful for further investigations of similar actimetry data sets, minimising the influence of specific device characteristics. The results also confirm that distinct PA event patterns - including event regularity - can be identified using wearable sensor devices, especially when regarding low-intensity, short-term activities which do not correspond to current PA recommendations. Further research is necessary to evaluate actual associations between sensor-based PA parameters and health outcome. The author identified generic intensity and duration thresholds for analysing objective PA data from wearable devices. This may contribute to further analyses of PA patterns along with their relations with health outcome parameters.


PubMed | Peter L Reichertz Institute For Medical Informatics
Type: | Journal: Human movement science | Year: 2015

Wearable actimetry devices are used increasingly in cohort and cross-sectional studies to assess physical activity (PA) behaviour objectively. Thus far, the medical relevance of distinct PA groups, as identified by using new methods of sensor data analysis, remains unclear. The objective of this research paper is to evaluate whether such PA groups differ in commonly accepted health risk parameters.PA sensor data and corresponding outcome data of the NHANES 2005-06 study were obtained. Data pre-processing included elimination of potential outliers, data splitting and the computation of PA parameters, including a novel regularity measure. PA groups were identified using the x-Means clustering algorithm, and groups were evaluated for differences in CRP, BMI and HDL.Data sets of 7334 NHANES participants were analysed, and four distinct PA groups were identified. Statistically significant group differences were found for CRP and BMI (p<0.001), but not for HDL (p=0.67).PA groups derived from objective accelerometer mass data differ in exemplary health-related outcome parameters. The novel PA regularity measure is of particular interest and may become part of future PA assessments, especially when regarding low-intensity, short-lived PA events. Further research in pattern recognition methods and analytic algorithms for PA data from current multi-sensing devices is necessary.

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