Health-Enabling and Ambient Assistive Technologies: Past, Present, Future.

BACKGROUND: During the last decades, health-enabling and ambient assistive technologies became of considerable relevance for new informatics-based forms of diagnosis, prevention, and therapy. OBJECTIVES: To describe the state of the art of health-enabling and ambient assistive technologies in 1992 and today, and its evolution over the last 25 years as well as to project where the field is expected to be in the next 25 years. In the context of this review, we define health-enabling and ambient assistive technologies as ambiently used sensor-based information and communication technologies, aiming at contributing to a person's health and health care as well as to her or his quality of life. METHODS: Systematic review of all original articles with research focus in all volumes of the IMIA Yearbook of Medical Informatics. Surveying authors independently on key projects and visions as well as on their lessons learned in the context of health-enabling and ambient assistive technologies and summarizing their answers. Surveying authors independently on their expectations for the future and summarizing their answers. RESULTS: IMIA Yearbook papers containing statements on health-enabling and ambient assistive technologies appear first in 2002. These papers form a minor part of published research articles in medical informatics. However, during recent years the number of articles published has increased significantly. Key projects were identified. There was a clear progress on the use of technologies. However proof of diagnostic relevance and therapeutic efficacy remains still limited. Reforming health care processes and focussing more on patient needs are required. CONCLUSIONS: Health-enabling and ambient assistive technologies remain an important field for future health care and for interdisciplinary research. More and more publications assume that a person's home and their interaction therein, are becoming important components in health care provision, assessment, and management.

A novel approach for discovering human behavior patterns using unsupervised methods.

BACKGROUND: As is well known, elderly people gradually lose the ability of self-care. The decline can be reflected in changes in their daily life behavior. A solution to assess their health status is to design sensor-enhanced living environments to observe their behavior, in which unobtrusive sensors are usually used. With respect to information extraction from the dataset collected by means of these kinds of sensors, unsupervised methods have to be relied on for practical application. Under the assumption that human lifestyle is associated with health status, this study intends to propose a novel approach to discover behavior patterns using unsupervised methods. METHODS: To evaluate the feasibility of this approach it was applied to datasets collected in the GAL-NATARS study. The study is part of the Lower Saxony research network Design of Environments for Aging (GAL) and conducted in subjects' home environments. The subjects recruited in GAL-NATARS study are older people (age ≥ 70 years), who are discharged from hospital to live alone again at their homes after treatment of a femoral fracture. RESULTS: The change of lifestyle regularity is measured. By analyzing the correlation between the extracted information and medical assessment results of four subjects, two of them exhibited impressive association and the other two showed less association. CONCLUSIONS: The approach may provide complementary information for health assessment; however, the dominant relationship between the change of behavior patterns and the health status has to be shown and datasets from more subjects must be collected in future studies. LIMITATIONS: Merely environmental data were used and no wearable sensor for activity detection or vital parameter measurement is taken into account. Therefore, this cannot comprehensively reflect reality.

Multimodal sensor-based fall detection within the domestic environment of elderly people.

BACKGROUND: Falls represent a major threat to the health of the elderly and are a growing burden on the healthcare systems. With the growth of the elderly population within most societies efficient fall detection becomes increasingly important; however, existing fall detection systems still fail to produce reliable results. OBJECTIVES: A study was carried out on sensor-based fall detection, analysis of falls with the help of fall protocols and the analysis of user acceptance of fall detection sensor technology through questionnaires. MATERIAL AND METHODS: A total of 28 senior citizens were recruited from a German community-dwelling population. The primary goal was a sensor-based detection of falls with accelerometers, video cameras and microphones. Details of the falls were analyzed with the help of medical geriatric assessments and standardized fall protocols. The study duration was 8 weeks and required a maximum of nine visits per subject. RESULTS: The study participants were 28 subjects with a mean age of 74.3 and a standard deviation (SD) of ± 6.3 years of which 12 were male and 16 female. A total of 1225.7 measurement days were recorded from all participants and the algorithms detected 2.66 falls per day. During the study period 15 falls occurred and 12 of these falls were correctly recognized by the fall detection system. CONCLUSION: Current fall detection technologies work well under laboratory conditions but it is still problematic to produce reliable results when these technologies are applied to real life conditions. Acceptance towards the sensors decreased after study participation although the system was generally perceived as useful or very useful.

Home-centered health-enabling technologies and regional health information systems. An integration approach based on international standards.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records". OBJECTIVES: In this paper, we present a prototype of a Home-Centered Health-Enabling Technology (HET-HC), which is able to capture, store, merge and process data from various sensor systems at people's home. In addition, we present an architecture designed to integrate HET-HC into an exemplary regional Health Information System (rHIS). METHODS: rHIS are traditionally document-based to fit to the needs in a clinical context. However, HET-HC are producing continuous data streams for which documents might be an inappropriate representation. Therefore, the HET-HC could register placeholder-documents at rHIS. These placeholder-documents are assembled upon user-authenticated request by the HET-HC and are always up-to-date. Moreover, it is not trivial to find a clinical coding system for continuous sensor data and to make the data machine-readable in order to enhance the interoperability of such systems. Therefore, we propose the use of SNOCAP-HET, which is a nomenclature to describe the context of sensor-based measurements in health-enabling technologies. RESULTS: We present an architectural approach to integrate HET-HC into rHIS. Our solution is the centralized registration of placeholder-documents with rHIS and the decentralized data storage at people's home. CONCLUSIONS: We concluded that the presented architecture of integrating HET-HC into rHIS might fit well to the traditional approach of document-based data storage. Data security and privacy issues are also duly considered.

Development and pilot study of a bed-exit alarm based on a body-worn accelerometer.

BACKGROUND: Falls are a major problem in hospitals and nursing homes. The consequences of falls can be severe, both for the individual and for the caring institution. OBJECTIVE: The aim of the work presented here is to reduce the number of falls on a geriatric ward by monitoring patients more closely. To achieve this goal, a bed-exit alarm that reliably detects an attempt to get up has been constructed. MATERIALS AND METHODS: A requirements analysis revealed the nurses' and physicians' needs and preferences. Based on the gathered information, an incremental design process generated different prototypes. These were tested for the reliability of their ability to detect attempts to get up in both laboratory settings and with geriatric patients. Based on the result of these tests, a scalable technical solution has been developed and proven its reliability in a 1-year, randomized controlled pilot clinical trial on a geriatric ward. RESULTS: The developed system is unobtrusive and easy to deploy. It has been tested in laboratory settings, usability tests and a 1-year randomized clinical trial with 98 patients. This paper focuses on the technical development of the system. We present different prototypes, the experiments and the pilot study used to evaluate their performance. Last but not least, we discuss the lessons learned so far. CONCLUSION: The developed bed-exit alarm is able to reliably detect patients' attempts to get up. The results of the clinical trial show that the system is able to reduce the number of falls on a geriatric ward. Next steps are the design of a specialized sensor node that is easier to use and can be applied on an even larger scale due to its reduced cost. A multicenter trial with a larger number of patients is required to confirm the results of this pilot study.

Measurement of accelerometry-based gait parameters in people with and without dementia in the field: a technical feasibility study.

BACKGROUND: Gait analyses are an important tool to diagnose diseases or to measure the rehabilitation process of patients. In this context, sensor-based systems, and especially accelerometers, gain in importance. They are able to improve objectiveness of gait analyses. In clinical settings, there is usually a supervisor who gives instructions to the patients, but this can have an influence on patients' gait. It is expected that this effect will be smaller in field studies. OBJECTIVE: Aim of this study was to capture and evaluate gait parameters measured by a single waist-mounted accelerometer during everyday life of subjects. METHODS: Due to missing ground-truth in unsupervised conditions, another external criterion had to be chosen. Subjects of two different groups were considered: patients with dementia (DEM) and active older people (ACT). These groups were chosen, because of the expected difference in gait. The idea was to quantify the expected difference of accelerometric-based gait parameters. Gait parameters were e.g. velocity, step frequency, compensation movements, and variance of the accelerometric signal. RESULTS: Ten subjects were measured in each group. The number of walking episodes captured was 1,187 (DEM) vs. 1,809 (ACT). The compensation and variance parameters showed an AUC value (Area Under the Curve) between 0.88 and 0.92. In contrast, velocity and step frequency performed poorly (AUC values of 0.51 and 0.55). It was possible to classify both groups using these parameters with an accuracy of 89.2%. CONCLUSION: The results showed a much higher amount of walking episodes in field studies compared to supervised clinical trials. The classification showed a high accuracy in distinguishing between both groups.

Assessment of risk factors for post-rewarming "rebound hyperthermia" in cardiac arrest patients undergoing therapeutic hypothermia.

INTRODUCTION: The outcomes associated with therapeutic hypothermia (TH) after cardiac arrest, while overwhelmingly positive, may be associated with adverse events. The incidence of post-rewarming rebound hyperthermia (RH) has been relatively unstudied and may worsen survival and neurologic outcome. The purpose of this study was to determine the incidence and risk factors associated with RH as well as its relationship to mortality, neurologic morbidity, and hospital length of stay (LOS). METHODS: A retrospective, observational study was performed of adult patients who underwent therapeutic hypothermia after an out-of-hospital cardiac arrest. Data describing 17 potential risk factors for RH were collected. The primary outcome was the incidence of RH while the secondary outcomes were mortality, discharge neurologic status, and LOS. RESULTS: 141 patients were included. All 17 risk factors for RH were analyzed and no potential risk factors were found to be significant at a univariate level. 40.4% of patients without RH experienced any cause of death during the initial hospitalization compared to 64.3% patients who experienced RH (OR: 2.66; 95% CI: 1.26-5.61; p=0.011). The presence of RH is not associated with an increase in LOS (10.67 days vs. 9.45 days; absolute risk increase=-1.21 days, 95% CI: -1.84 to 4.27; p=0.434). RH is associated with increased neurologic morbidity (p=0.011). CONCLUSIONS: While no potential risk factors for RH were identified, RH is a marker for increased mortality and worsened neurologic morbidity in cardiac arrest patients who have underwent TH.

GAL@Home: a feasibility study of sensor-based in-home fall detection.

BACKGROUND: A considerable proportion of falls occur within the domestic environment. Sensor-based identification of falls in seniors' homes could help them to remain autonomous and self-sufficient in their own homes. The objective of this study was to evaluate fall detection systems within the home environment using optical and accelerometric sensor systems. METHODS: Portable triaxial accelerometers and optical sensors were used to detect falls in subjects with known problems of mobility and a recent fall history. RESULTS: Three subjects were investigated with the system. Overall nine falls occurred during the study period. Four falls were recorded by the accelerometric system and one fall by the optical system. Subjects with increased risk of falling as measured with mobility and fall risk assessments tend to fall more frequently. CONCLUSION: The study shows that there is a considerably large difference between fall-detector evaluation studies in domestic environments and in laboratory trials.

Development and clinical validation of an unobtrusive ambulatory knee function monitoring system with inertial 9DoF sensors.

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.

Sensor-based fall risk assessment--an expert 'to go'.

BACKGROUND: Falls are a predominant problem in our aging society, often leading to severe somatic and psychological consequences, and having an incidence of about 30% in the group of persons aged 65 years or above. In order to identify persons at risk, many assessment tools and tests have been developed, but most of these have to be conducted in a supervised setting and are dependent on an expert rater. OBJECTIVES: The overall aim of our research work is to develop an objective and unobtrusive method to determine individual fall risk based on the use of motion sensor data. The aims of our work for this paper are to derive a fall risk model based on sensor data that may potentially be measured during typical activities of daily life (aim #1), and to evaluate the resulting model with data from a one-year follow-up study (aim #2). METHODS: A sample of n = 119 geriatric inpatients wore an accelerometer on the waist during a Timed 'Up & Go' test and a 20 m walk. Fifty patients were included in a one-year follow-up study, assessing fall events and scoring average physical activity at home in telephone interviews. The sensor data were processed to extract gait and dynamic balance parameters, from which four fall risk models--two classification trees and two logistic regression models--were computed: models CT#1 and SL#1 using accelerometer data only, models CT#2 and SL#2 including the physical activity score. The risk models were evaluated in a ten-times tenfold cross-validation procedure, calculating sensitivity (SENS), specificity (SPEC), positive and negative predictive values (PPV, NPV), classification accuracy, area under the curve (AUC) and the Brier score. RESULTS: Both classification trees show a fair to good performance (models CT#1/CT#2): SENS 74%/58%, SPEC 96%/82%, PPV 92%/ 74%, NPV 77%/82%, accuracy 80%/78%, AUC 0.83/0.87 and Brier scores 0.14/0.14. The logistic regression models (SL#1/SL#2) perform worse: SENS 42%/58%, SPEC 82%/ 78%, PPV 62%/65%, NPV 67%/72%, accuracy 65%/70%, AUC 0.65/0.72 and Brier scores 0.23/0.21. CONCLUSIONS: Our results suggest that accelerometer data may be used to predict falls in an unsupervised setting. Furthermore, the parameters used for prediction are measurable with an unobtrusive sensor device during normal activities of daily living. These promising results have to be validated in a larger, long-term prospective trial.

Decision support for teletraining of COPD patients.

BACKGROUND: Supervised physical training has been shown to promote rehabilitation of patients affected by chronic obstructive pulmonary disease (COPD). Currently, due to limited resources, not all COPD patients can be trained by an expert supervisor. OBJECTIVES: The objective of our research is to construct a decision support system (DSS) which observes and controls physical ergometer training sessions of COPD patients. METHODS: A systematic literature review and expert interviews were carried out to build up the knowledge base for the DSS. RESULTS: Nine production rules were established and standardized by Drools and Arden Syntax. The developed software autonomously controls training sessions on a bicycle ergometer on the basis of vital signs data. Thus it offers a new way for the rehabilitation of COPD patients. CONCLUSION: Evaluation with nine healthy subjects in a laboratory environment has confirmed its correct function, but the effects of its use for COPD patients' rehabilitation and their quality of life have to be investigated in a further study.

Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements.

BACKGROUND: Falls are among the predominant causes for morbidity and mortality in elderly persons and occur most often in geriatric clinics. Despite several studies that have identified parameters associated with elderly patients' fall risk, prediction models -- e.g., based on geriatric assessment data -- are currently not used on a regular basis. Furthermore, technical aids to objectively assess mobility-associated parameters are currently not used. OBJECTIVES: To assess group differences in clinical as well as common geriatric assessment data and sensory gait measurements between fallers and non-fallers in a geriatric sample, and to derive and compare two prediction models based on assessment data alone (model #1) and added sensory measurement data (model #2). METHODS: For a sample of n=110 geriatric in-patients (81 women, 29 men) the following fall risk-associated assessments were performed: Timed 'Up & Go' (TUG) test, STRATIFY score and Barthel index. During the TUG test the subjects wore a triaxial accelerometer, and sensory gait parameters were extracted from the data recorded. Group differences between fallers (n=26) and non-fallers (n=84) were compared using Student's t-test. Two classification tree prediction models were computed and compared. RESULTS: Significant differences between the two groups were found for the following parameters: time to complete the TUG test, transfer item (Barthel), recent falls (STRATIFY), pelvic sway while walking and step length. Prediction model #1 (using common assessment data only) showed a sensitivity of 38.5% and a specificity of 97.6%, prediction model #2 (assessment data plus sensory gait parameters) performed with 57.7% and 100%, respectively. DISCUSSION AND CONCLUSION: Significant differences between fallers and non-fallers among geriatric in-patients can be detected for several assessment subscores as well as parameters recorded by simple accelerometric measurements during a common mobility test. Existing geriatric assessment data may be used for falls prediction on a regular basis. Adding sensory data improves the specificity of our test markedly.

On health-enabling and ambient-assistive technologies. What has been achieved and where do we have to go?

OBJECTIVES: To provide an introductory overview on selected areas of health-enabling and ambient-assistive technologies and to suggest relevant fields for future research. METHODS: Summarizing the state of the art for 1) ambient environments and 2) health-related sensor technologies, 3) presenting relevant medical conditions and their relation to health-enabling technologies, and 4) illustrating the application of those technologies for lifestyle management with examples for activity monitoring and prevention of overweight. The suggestions for future research are based on own research projects in this field. RESULTS: Health-enabling and ambient-assistive technologies are regarded to have the potential to significantly improve quality of life and efficiency of health care in aging societies. Research on health-enabling and ambient-assistive technologies is highly multidisciplinary. CONCLUSIONS: It still has, to a large extent, to be exploratory. An increasing amount of outcomes research through well-designed evaluation studies that explore technical solutions in a social context and in relation to individual needs will be crucial.

Towards new scopes: sensor-enhanced regional health information systems - part 1: architectural challenges.

OBJECTIVES: To analyze utilization of sensor technology in telemonitoring and home care and to discuss concepts and challenges of sensor-enhanced regional health information systems (rHIS). METHODS: The study is based upon experience in sensor-based telemedicine and rHIS projects, and on an analysis of HIS-related journal publications from 2003 to 2005 conducted in the context of publishing the IMIA Yearbook of Medical Informatics. RESULTS: Health-related parameters that are subject to sensor-based measurement in home care and telemonitoring are identified. Publications related to telemonitoring, home care and smart houses are analyzed concerning scope and utilization of sensor technology. Current approaches for integrating sensor technology in rHIS based on a corresponding eHealth infrastructure are identified. Based on a coarse architecture of home care and telemonitoring systems ten challenges for sensor-enhanced rHIS are identified and discussed: integration of home and health telematic platforms towards a sensor-enhanced telematic platform, transmission rate guarantees, ad hoc connectivity, cascading data analysis, remote configuration, message and alert logistic, sophisticated user interfaces, unobtrusiveness, data safety and security, and electronic health record integration. CONCLUSIONS: Utilization of sensor technology in health care is an active field of research. Currently few research projects and standardization initiatives focus on general architectural considerations towards suitable telematic platforms for establishing sensor-enhanced rHIS. Further research finalized by corresponding standardization is needed. Part 2 of this paper will present experiences with a research prototype for a sensor-enhanced rHIS telematic platform.

Preserved vasopressin response to osmostimulation despite decreased basal vasopressin levels in long-term abstinent alcoholics.

BACKGROUND: Basal arginine vasopressin (AVP) plasma levels in alcoholic patients are persistently decreased over months of controlled alcohol abstinence. As a potential explanation of this phenomenon, a reduction of AVP immunoreactive neurons was described in the hypothalamus of alcohol-dependent humans and rodents. This study was therefore designed to examine whether long-term abstinent alcoholics have a compromised response of AVP to osmostimulation. METHODS: Fifteen male alcoholics, aged 42 +/- 2 years, were examined (1) over 12 months of strictly controlled abstinence (longitudinal study) and (2) during an osmostimulation test (5% NaCl infusion at 0.06 ml/kg/min over 2 hr) and were compared with 15 healthy male subjects, aged 41 +/- 2 years. AVP and routine laboratory parameters, including electrolytes and osmolality, were measured. RESULTS: Starting from lower basal concentrations, alcoholics showed increases similar to those of controls in AVP and plasma osmolality after osmostimulation. The first sensation of thirst was announced significantly later by alcoholics than by controls. Twenty-hour-posttest urine volume and sodium excretion were reduced in alcoholics compared with controls. CONCLUSIONS: Despite their persistently decreased basal AVP plasma levels, long-term abstinent alcoholics have a well preserved AVP response to osmostimulation. This finding indicates a peripheral suppression of AVP levels that is most likely due to a regulatory set-point shift toward hypotonic hyperhydration, rather than to a reduced central capacity of AVP secretion.

Persistent alterations of vasopressin and N-terminal proatrial natriuretic peptide plasma levels in long-term abstinent alcoholics.

BACKGROUND: During alcohol withdrawal and early abstinence, severe alterations of electrolyte and water homeostasis and their regulating hormones are well recognized. Almost nothing is known about regeneration of these functions with long-term abstinence. This cohort study was designed to monitor determinants of electrolyte and water balance over 280 days of abstinence in alcohol-dependent men compared with healthy controls. METHODS: Vasopressin (AVP), N-terminal proatrial natriuretic peptide, aldosterone, angiotensin II, and electrolytes, together with major parameters of kidney and liver function, were monitored in 35 male alcoholics aged 44 +/- 8 years. Of these, 21 could be followed up to 280 days of strictly controlled abstinence due to their participation in the Outpatient Long-Term Intensive Therapy for Alcoholics. The control group comprised 20 healthy male volunteers aged 39 +/- 7 years. RESULTS: Basal AVP levels were found to be suppressed over the whole study period. In contrast, N-terminal proatrial natriuretic peptide remained increased over all 280 days. No persistent alterations were found for aldosterone or angiotensin II. Sodium and potassium in plasma and urine returned to normal within a few weeks. Creatinine clearance, urea nitrogen in plasma and urine, urinary osmolality, hematocrit, and hemoglobin remained low as compared with controls over the entire study. CONCLUSIONS: Chronic alcohol abuse causes severe and persistent alterations in the hormonal regulatory systems of electrolyte and water balance. The suppressed basal secretion of AVP may reflect a dysregulation in the brain that influences the hypothalamic-pituitary-adrenal axis function, mood, memory, addiction behavior, and craving during alcohol abstinence. These findings may provide a ground for future therapeutic approaches to stable abstinence.

Telemedical support of prehospital emergency care in mass casualty incidents.

In the German emergency medical service system (EMSS) medical treatment can be improved in most of mass casualty incidents (MCI). Currently, the incident commander who is responsible for classification of the victims (depending on their urgency and condition, the so called triage) and ordered transportation uses paper-based documentation. Triage tags are used to identify and classify patients and gather treatment information. This can cause problems in medical treatment and in transportation of injured victims. Object-oriented modelling, simulation, and visualisation of processes can show deficits in treatment and data processing and thereby help to optimise medical workflow and logistics. If documentation by paramedics and emergency physicians is done electronically, all patient records could be send to a telemedical centre for central data administration. A telemedical supported triage tag helps identifying victims and managing detailed identification protocols. The paper-based documentation in emergency would become obsolete, if hospitals can query all protocols, diagnoses, and findings from the telemedical centre. Safety and security aspects can be guaranteed. The complete medical treatment workflow can be supported by telemedicine. Therefore, in case of MCI, telemedicine can optimise medical treatment and exonerate the paramedics from unnecessary documentation.