Technische Systeme im Pflege- und Versorgungsmix für ältere Menschen: Expertise zum Siebten Altenbericht der Bundesregierung

Technische Assistenzsysteme können körperbezogen oder raumbezogen (z.B. in der Wohnung) gesundheitsrelevante Daten bei älteren Menschen aufnehmen, analysieren und gegebenenfalls weiterleiten. Ihre Aufgaben umfassen unter anderem die Alarmierung und Notfallidentifikation sowie die Unterstützung bei Erkrankungen und Funktionsdefiziten. Sie werden auch für nicht mit der Gesund-heitsversorgung in Zusammenhang stehende Zwecke verwendet. Bei der Entwicklung altersgerechter technischer Assistenzsysteme gab es erhebliche Fortschritte. Es ist zu erwarten, dass diese zu neuen Lebensweisen und neuen Versorgungsformen führen und dass sich das persönliche Umfeld einer Person (und hier insbesondere die Wohnung) zum neuen, zusätzlichen Gesundheitsstandort entwickeln wird. Es ist weiterhin zu erwarten, dass durch die Nutzung solcher Systeme neue diagnostische und therapeutische Verfahren entwickelt werden können, die verbesserte Möglichkeiten der Pflege als auch der ärztlichen Versorgung erwarten lassen und die zu einer längeren selbstständigen Lebensführung beitragen können. Neue Herausforderungen ergeben sich im Datenschutz, bei der informationellen Selbstbestimmung und bei der Finanzierung. Auch bei der Nutzung technischer Assistenzsysteme geht es darum, zu einer möglichst langen selbstständigen Lebensführung und zu einem aktiven Altern in Selbst- und Mitverantwortung beizutragen. Ob und inwieweit dies der Fall ist, muss weiter belegt werden. Hierzu sind nach wissenschaftlichen Standards geplante Studien notwendig, welche Aspekte wie diagnostische Relevanz und therapeutische Wirksamkeit sowie Lebensqualität untersuchen

Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

Background Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design. Results Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%). Conclusion Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment

a smartwatch step counter for slow and intermittent ambulation

The ambulatory monitoring of human movement can provide valuable information regarding the degree of functional ability and general level of activity of individuals. Since walking is a basic everyday movement, automatic step detection or step counting is very important in developing ambulatory monitoring systems. This paper is concerned with the development and the preliminary validation of a step counter (SC) designed to operate also in conditions of slow and intermittent ambulation. The SC was based on processing the accelerometer data measured by a Gear 2 smartwatch running a custom wearable app, named ADAM. A data set of eight users, for a total of 80 trials, was used to tune ADAM. Finally, ADAM was compared with two different commercial SCs: the native SC running on the Gear 2 smart watch and a waist-worn SC, the Geonaute ONSTEP 400. A second data set of eight additional users for a total of 80 trials was used for the assessment study. The three SCs performed quite similarly in conditions of normal walking over long paths (1%–3% of mean absolute relative error); ADAM outperformed the two other SCs in conditions of slow and intermittent ambulation; the error incurred by ADAM was limited to 5%, which is significantly lower than errors of 20%–30% incurred by the two other SCs

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

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

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

Abstract 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.</p

Adopting Modern Fitness Sensors to Improve Patient Care

Technology found in modern fitness sensor devices advances at a very fast pace and current smartwatches are on the verge of closing the gap between being an everyday object and a medically reliable monitoring device. In this thesis, the possibility of adopting fitness sensor devices in medical environments is explored and use cases in which sensor devices can be deployed are examined. Their successful transfer from the area of sports to medical analyses and treatments may help patients to deal with their illnesses and to improve the level of patient care found today. Privacy and security issues as well as social concerns associated with such a disruptive evolution are discussed and practical tests of a pulse oximeter in various activities of daily living are conducted. The collected health data depicts a close representation of the performed activities. Furthermore, three types of fitness sensor devices were used in different real-life scenarios and the resulting data is compared. The results show that the recorded vital signs may differ significantly, depending on the scenario. ii

Biomedical engineering for healthy ageing. Predictive tools for falls

Falls are common and burdensome accidents among the elderly. About one third of the population aged 65 years or more experience at least one fall each year. Fall risk assessment is believed to be beneficial for fall prevention. This thesis is about prognostic tools for falls for community-dwelling older adults. We provide an overview of the state of the art. We then take different approaches: we propose a theoretical probabilistic model to investigate some properties of prognostic tools for falls; we present a tool whose parameters were derived from data of the literature; we train and test a data-driven prognostic tool. Finally, we present some preliminary results on prediction of falls through features extracted from wearable inertial sensors. Heterogeneity in validation results are expected from theoretical considerations and are observed from empirical data. Differences in studies design hinder comparability and collaborative research. According to the multifactorial etiology of falls, assessment on multiple risk factors is needed in order to achieve good predictive accuracy