The VERSA study (prediction for maintaining self-employment in old age) is a subproject within the BMBF-funded collaborative AEQUIPA (physical activity and health equity: primary prevention for healthy aging) project. The study aims to examine whether sensor-based assessments as a two-point delta-measure over 6 months are an appropriate approach to predict functional decline in advance.
Background and Needs
Muscle power, balance and endurance are crucial for stable movements and, thus, represent the main technically measurable indicators for functional degradation [Hellmers2017]. In order to maintain these factors and to prevent functional disruption, regular training is necessary - especially in and before phases of acute functional degradation. In order to initiate preventive interventions in advance of such phases, measuring techniques for motion analysis that can be used in daily living and enable preventive and precise detction of functional degradation, are required.
Approach
The VERSA study aims to develop a motion analysis system (as a combined set of hardware and detection algorithms) for the predictive detection of functional degradation to recognize gradual functional degradations in the elderly as early as possible. Thereby, 251 subjects between 70 and 89 years were included in this study. The measurements are taken at three measuring times within a period of two years. In this case, established geriatric tests of physical functionality and mobility, the measurement of body composition, and sensor-based motion analyzes are used to characterize subjects (see [Hellmers2016]). In addition, movement data are recorded and evaluated for motion analysis and longer-term analysis of the individual physical activity by inertial sensors integrated into a belt (accelerometer, gyroscope, magnetometer and barometer).
In order to investigate the motion analysis system's applicability for unstandardized everyday activities in unsupervised settings, participants also carry the inertial sensors for a subsequent week following each assessment in their everyday life. This setting promises ideal prerequisites for more detailed screening, as well as support and reviews of primary prevention measures.
Grants and Cooperation
The VERSA study, as part of the AEQUIPA project is funded from 02/2015 till 01/2018 by the federal Bundesministerium für Bildung und Forschung (BMBF).
Contributing Partners:
Universitätsklinik für Geriatrie Oldenburg der Carl von Ossietzky Universität Oldenburg
Geriatrisches Zentrum am AGAPLESION BETHANIEN KRANKENHAUS HEIDELBERG, Lehrstuhl für Geriatrie der Universität Heidelberg (Prof. Bauer)
Abteilung Technik und Gesundheit für Menschen an der Jade Hochschule Oldenburg (Prof. Koppelin)
OFFIS (Institut für Informatik, Prof. Boll-Westermann)
S. Hellmers, S. Fudickar, L. Dasenbrock, A. Heinks, J. M. Bauer, and A. Hein, "Understanding Jump Landing as an Oscillating System: A Model-based Approach of Balance and Strength Analyses" in Proc. Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, Porto, 2017.
doi: 10.5220/0006171101590168
@inproceedings{Hellmers2017, abstract = {Counter movement jumps (CMJ) are well-suited to measure the muscle power and balance. Since it has been clarified that well accepted CMJ amplification-based balance measures (such as TTS or CoP) are significantly influenced by algorithmic and measurement settings and thus, measurement results have limited meaningfulness among force platforms, we introduce a new model-based approach measuring the postural stability. In this, during the landing and recovery phases after vertical jumps, the lower extremities can be represented by an oscillating system and the corresponding transfer function is described by a second-order delay (PT2) element. In an initial prospective study with 20 subjects aged over 70 years, we observed an inverse relationship between the natural frequency and the jump height and could also identify an influence of sex, and body weight on the jump height. Furthermore, we also found a strong relation between natural frequency and dynamic postural stability index (DPSI), even though these results must be statistically ensured statistically using a larger cohort, due to the current limited number of subjects. Nevertheless, we could confirm the general applicability of the Systems and Control Technology perspective on describing human movements in a potentially more robust manner than current amplification based approaches.},
address = {Porto},
author = {Hellmers, Sandra and Fudickar, Sebastian and Dasenbrock, Lena and Heinks, Andrea and Bauer, J{\"{u}}rgen M. and Hein, Andreas},
booktitle = {Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies},
doi = {10.5220/0006171101590168},
file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hellmers et al. - 2017 - Understanding the Jump Landing as an Oscillating System A Model-based Approach of Balance and Strength Analyzes.pdf:pdf},
isbn = {978-989-758-213-4},
keywords = {UNIAMT,accepted,full paper},
mendeley-tags = {UNIAMT,accepted,full paper},
pages = {159--168},
publisher = {SCITEPRESS - Science and Technology Publications},
title = {{Understanding Jump Landing as an Oscillating System: A Model-based Approach of Balance and Strength Analyses}},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006171101590168},
year = {2017}
L. C. Büker, F. Zuber, A. Hein, and S. Fudickar, "HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints" Sensors, vol. 21, iss. 4.
doi: 10.3390/s21041356
@Article{Bueker2021,
author = {Büker, Linda Christin and Zuber, Finnja and Hein, Andreas and Fudickar, Sebastian},
TITLE = {HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints},
JOURNAL = {Sensors},
VOLUME = {21},
YEAR = {2021},
NUMBER = {4},
ARTICLE-NUMBER = {1356},
URL = {https://www.mdpi.com/1424-8220/21/4/1356},
ISSN = {1424-8220},
ABSTRACT = {With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).},
DOI = {10.3390/s21041356}
}
R. Diekmann, S. Hellmers, L. Elgert, S. Fudickar, A. Heinks, S. Lau, J. M. Bauer, T. Zieschang, and A. Hein, "Minimizing comprehensive geriatric assessment to identify deterioration of physical performance in a healthy community-dwelling older cohort: longitudinal data of the AEQUIPA Versa study" Aging Clinical and Experimental Research.
doi: 10.1007/s40520-020-01562-8
@article{Diekmann2020, abstract = {It is important to identify the relevant parameters of physical performance to prevent early functional decline and to prolong independent living. The aim of this study is to describe the development of physical performance in a healthy community-dwelling older cohort aged 70+ years using comprehensive assessment over two years and to subsequently identify the most relevant predictive tests for physical decline to minimize assessment. Physical performance was measured by comprehensive geriatric assessment. Predictors for the individual decline of physical performance by Principal Component and k-means Cluster Analysis were developed, and sensitivity and specificity determined accordingly. 251 subjects ({\O} 75.4 years) participated in the study. Handgrip strength was low in 21.1{\%}. The follow-up results of tests were divergent. Handgrip strength [− 16.95 (SD 11.55)] and the stair climb power test (power) [− 9.15 (SD 16.84)] yielded the highest percentage changes. Four most relevant tests (handgrip strength, stair climb power time, timed up {\&} go and 4-m gait speed) were identified. A predictor based on baseline data was determined (sensitivity 82{\%},
specificity 96{\%}) to identify subjects characterized by a high degree of physical decline within two years. Although the cohort of older adults is heterogeneous, most of the individuals in the study exhibited high levels of physical performance; only a few subjects suffered a relevant decline within the 2-year follow-up. Four most relevant tests were identified to predict relevant decline of physical function. In spite of ceiling effects of the geriatric assessment in high-performers, we assume that it is possible to predict an individual's risk of physical decline within 2 years with four tests of a comprehensive geriatric assessment.},
author = {Diekmann, R. and Hellmers, S. and Elgert, L. and Fudickar, S. and Heinks, A. and Lau, S. and Bauer, J. M. and Zieschang, T. and Hein, A.},
doi = {10.1007/s40520-020-01562-8},
issn = {17208319},
journal = {Aging Clinical and Experimental Research},
pmid = {32358730},
publisher = {Springer International Publishing},
title = {{Minimizing comprehensive geriatric assessment to identify deterioration of physical performance in a healthy community-dwelling older cohort: longitudinal data of the AEQUIPA Versa study}},
year = {2020},
url = {https://doi.org/10.1007/s40520-020-01562-8}
}
S. Hellmers, L. Peng, S. Lau, R. Diekmann, L. Elgert, J. M. Bauer, A. Hein, and S. Fudickar, "Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life" in Proc. Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, Valetta (Malta), 2020.
doi: 10.5220/0009095505790585
@inproceedings{Hellmers2020, abstract = {The trend of an ageing population is becoming more and more obvious. Staying healthy in old age is an important social issue. Thereby, physical activity is essential for the preservation of physical function. We developed an algorithm for determining the activity level of seniors in everyday life. The proposed algorithm is based on machine learning activity detection using inertial measurement unit data. A series of activity scores is obtained by executing the algorithm from data on the type of activity, total activity time and activity intensity. To evaluate the performance of the proposed algorithm, a study with 251 participants aged above 70 (75.41 ± 3.88) years was conducted and the correlation between individual activity scores and clinical mobility assessments was determined. Results showed a relation between the Six Minute Walking Test and the total score in terms of activity level as well as the walk score. Additionally, the MVPA- and walk-score show a clear trend regarding the frailty status of the participants. Therefore, these scores are indicators of the physical function and hence validate the utility of the developed algorithm.},
address = {Valetta (Malta)},
author = {Hellmers, Sandra and Peng, Lianying and Lau, Sandra and Diekmann, Rebecca and Elgert, Lena and Bauer, J{\"{u}}rgen M. and Hein, Andreas and Fudickar, Sebastian},
booktitle = {Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF},
pages = {579--585},
publisher = {SCITEPRESS – Science and Technology Publications, Lda.},
title = {{Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life}},
year = {2020},
doi = {10.5220/0009095505790585},
url = {https://doi.org/10.5220/0009095505790585}
}
S. Fudickar, S. Hellmers, S. Lau, R. Diekmann, J. M. Bauer, and A. Hein, "Measurement System for Unsupervised Standardized Assessment of Timed “Up & Go” and Five Times Sit to Stand Test in the Community—A Validity Study" Sensors, vol. 20, iss. 10, p. 2824.
doi: 10.3390/s20102824
@article{Fudickar2020a, abstract = {Comprehensive and repetitive assessments are needed to detect physical changes in an older population to prevent functional decline at the earliest possible stage and to initiate preventive interventions. Established instruments like the Timed “Up {\&} Go” (TUG) Test and the Sit-to-Stand Test (SST) require a trained person (e.g., physiotherapist) to assess physical performance. More often, these tests are only applied to a selected group of persons already functionally impaired and not to those who are at potential risk of functional decline. The article introduces the Unsupervised Screening System (USS) for unsupervised self-assessments by older adults and evaluates its validity for the TUG and SST. The USS included ambient and wearable movement sensors to measure the user's test performance. Sensor datasets of the USS's light barriers and Inertial Measurement Units (IMU) were analyzed for 91 users aged 73 to 89 years compared to conventional stopwatch measurement. A significant correlation coefficient of 0.89 for the TUG test and of 0.73 for the SST were confirmed among USS's light barriers. Correspondingly, for the inertial data-based measures, a high and significant correlation of 0.78 for the TUG test and of 0.87 for SST were also found. The USS was a validated and reliable tool to assess TUG and SST.},
author = {Fudickar, Sebastian and Hellmers, Sandra and Lau, Sandra and Diekmann, Rebecca and Bauer, J{\"{u}}rgen M. and Hein, Andreas},
doi = {10.3390/s20102824},
issn = {14248220},
journal = {Sensors},
keywords = {5xsst,assessment,evaluation,five times sit-to-stand test,functional,go,inertial,machine learning,measurement units,system usability,technology,test,timed,tug,unsupervised,up,validity analysis},
number = {10},
pages = {2824},
pmid = {32429306},
title = {{Measurement System for Unsupervised Standardized Assessment of Timed “Up {\&} Go” and Five Times Sit to Stand Test in the Community—A Validity Study}},
volume = {20},
year = {2020},
url = {https://doi.org/10.3390/s20102824}
}
S. Hellmers, S. Lau, R. Diekmann, L. Dasenbrock, T. Kromke, J. M. Bauer, S. Fudickar, and A. Hein, "Evaluation of Power-Based Stair Climb Performance via Inertial Measurement Units" in Proc. Biomedical Engineering Systems and Technologies, Cham, 2019.
@inproceedings{10.1007/978-3-030-29196-9_13,
author="Hellmers, Sandra and Lau, Sandra and Diekmann, Rebecca and Dasenbrock, Lena and Kromke, Tobias and Bauer, J{\"u}rgen M. and Fudickar, Sebastian and Hein, Andreas", editor="Cliquet Jr., Alberto and Wiebe, Sheldon and Anderson, Paul and Saggio, Giovanni and Zwiggelaar, Reyer and Gamboa, Hugo and Fred, Ana and Berm{\'u}dez i Badia, Sergi", title="Evaluation of Power-Based Stair Climb Performance via Inertial Measurement Units", booktitle="Biomedical Engineering Systems and Technologies", year="2019", publisher="Springer International Publishing", address="Cham", pages="238--261", abstract="The stair climbing test (SCT) is a standard geriatric assessment to measure lower-limb strength being one of the essential components of physical function. To detect functional decline as early as possible, regular assessments of mobility, balance, and strength are necessary. Inertial measurement units (IMU) are a promising technology for flexible and unobtrusive measurements of the SCTs. We introduce an automated assessment via IMUs in a study of 83 participants aged 70--87 (75.64 {\textpm} 4,17) years. The activity of stair ascending has been automatically classified via a k-nearest-neighbor classifier and the performance was evaluated regarding the power. Therefore, we considered both, stair climb average power and peak power. Stair ascending was correctly classified in 93{\%} of the cases with a mean deviation of 2.35{\%} of the average power value in comparison to conventional measurements. Additionally, we showed the medical sensitivity of our system regarding the detection of transitions towards the frail status in controlled conditions and also confirmed the general suitability of automated stair climb analyses in unsupervised home-assessments.", isbn="978-3-030-29196-9" }
S. Hellmers, S. Fudickar, S. Lau, L. Elgert, R. Diekmann, J. M. Bauer, and A. Hein, "Measurement of the Chair Rise Performance of Older People Based on Force Plates and IMUs" Sensors, vol. 19, iss. 6, p. 1370.
doi: 10.3390/s19061370
@Article{Hellmers2019,
author = {Hellmers, Sandra and Fudickar, Sebastian and Lau, Sandra and Elgert, Lena and Diekmann, Rebecca and Bauer, Jürgen M. and Hein, Andreas},
TITLE = {Measurement of the Chair Rise Performance of Older People Based on Force Plates and IMUs},
JOURNAL = {Sensors},
VOLUME = {19},
YEAR = {2019},
NUMBER = {6},
pages = {1370},
URL = {http://www.mdpi.com/1424-8220/19/6/1370},
ISSN = {1424-8220},
ABSTRACT = {An early detection of functional decline with age is important to start interventions at an early state and to prolong the functional fitness. In order to assure such an early detection, functional assessments must be conducted on a frequent and regular basis. Since the five time chair rise test (5CRT) is a well-established test in the geriatric field, this test should be supported by technology. We introduce an approach that automatically detects the execution of the chair rise test via an inertial sensor integrated into a belt. The system’s suitability was evaluated via 20 subjects aged 72–89 years (78.2 ± 4.6 years) and was measured by a stopwatch, the inertial measurement unit (IMU), a Kinect® camera and a force plate. A Multilayer Perceptrons-based classifier detects transitions in the IMU data with an F1-Score of around 94.8%. Valid executions of the 5CRT are detected based on the correct occurrence of sequential movements via a rule-based model. The results of the automatically calculated test durations are in good agreement with the stopwatch measurements (correlation coefficient r = 0.93 (p < 0.001)). The analysis of the duration of single test cycles indicates a beginning fatigue at the end of the test. The comparison of the movement pattern within one person shows similar movement patterns, which differ only slightly in form and duration, whereby different subjects indicate variations regarding their performance strategies.},
DOI = {10.3390/s19061370}
}
S. Hellmers, B. Izadpanah, L. Dasenbrock, R. Diekmann, J. M. Bauer, A. Hein, and S. Fudickar, "Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements" Sensors, vol. 18, iss. 10.
doi: 10.3390/s18103310
@article{AHellmers2018, abstract = {One of the most common assessments for the mobility of older people is the Timed Up and Go test (TUG). Due to its sensitivity regarding the indication of Parkinson's disease (PD) or increased fall risk in elderly people, this assessment test becomes increasingly relevant, should be automated and should become applicable for unsupervised self-assessments to enable regular examinations of the functional status. With Inertial Measurement Units (IMU) being well suited for automated analyses, we evaluate an IMU-based analysis-system, which automatically detects the TUG execution via machine learning and calculates the test duration. as well as the duration of its single components. The complete TUG was classified with an accuracy of 96% via a rule-based model in a study with 157 participants aged over 70 years. A comparison between the TUG durations determined by IMU and criterion standard measurements (stopwatch and automated/ambient TUG (aTUG) system) showed significant correlations of 0.97 and 0.99, respectively. The classification of the instrumented TUG (iTUG)-components achieved accuracies over 96%, as well. Additionally, the system's suitability for self-assessments was investigated within a semi-unsupervised situation where a similar movement sequence to the TUG was executed. This preliminary analysis confirmed that the self-selected speed correlates moderately with the speed in the test situation, but differed significantly from each other.},
author = {Hellmers, Sandra and Izadpanah, Babak and Dasenbrock, Lena and Diekmann, Rebecca and Bauer, J{\"{u}}rgen M and Hein, Andreas and Fudickar, Sebastian},
doi = {10.3390/s18103310},
file = {:home/s/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hellmers et al. - 2018 - Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements.pdf:pdf},
issn = {1424-8220},
journal = {Sensors},
number = {10},
title = {{Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements}},
url = {http://www.mdpi.com/1424-8220/18/10/3310},
volume = {18},
year = {2018}
}
S. Hellmers, T. Kromke, L. Dasenbrock, A. Heinks, J. M. Bauer, A. Hein, and S. Fudickar, "Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic Environments" in Proc. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, 2018.
doi: 10.5220/0006543900390047
@inproceedings{HellmersHeinFudickar2018, abstract = {In order to initiate interventions at an early stage of functional decline and thus, to extend independent living, the early detection of changes in functional ability is important. The Stair Climb Power Test (SCPT) is a standard test in geriatric assessments for strength as one of the essential components of functional ability. This test is also well suited for regular and frequent power measurements in daily life since the activity of climbing stairs is usually frequently performed. We introduce an automated assessment of the SCPT based on inertial measurement units (IMU) in a study of 83 participants aged 70-87 years. For power evaluations of the lower extremities, the activity of climbing stairs was automatically classified via machine learning and the power was calculated based on the test duration and covered height. Climbing stairs was correctly classified in 93{\%} of the cases. We also achieved a good correlation of the power calculations with the conventional stop watch measurements with a mean deviation of 2.35{\%}. The system's sensitivity to detect the transition towards frailty has been confirmed. Furthermore, we discussed the general suitability of the automated stair climb power algorithm in unsupervised, standardized home-assessments.},
author = {{Hellmers, Sandra and Kromke, Tobias and Dasenbrock, Lena and Heinks, Andrea and Bauer, J{\"{u}}rgen M. and Hein, Andreas and Fudickar, Sebastian}},
booktitle = {Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies},
doi = {10.5220/0006543900390047},
isbn = {978-989-758-281-3},
keywords = {UNIAMT,accepted,full paper},
mendeley-tags = {UNIAMT,accepted,full paper},
pages = {39--47},
publisher = {SCITEPRESS - Science and Technology Publications},
title = {{Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic Environments}},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006543900390047},
year = {2018}
}
S. Hellmers, S. Fudickar, L. Dasenbrock, A. Heinks, J. M. Bauer, and A. Hein, "A Model-Based Approach for Jump Analyses Regarding Strength and Balance" in Proc. Biomedical Engineering Systems and Technologies, Cham, 2018.
doi: 10.1007/978-3-319-94806-5_19
@inproceedings{HellmersSandra2018, abstract = {To identify the functional decline as related to aging, geriatric assessments are an established instrument. Within such assessments, the functional ability is evaluated and consists of the three major components: strength, mobility, and balance. Counter movement jumps (CMJ) are well-suited to test these three essential elements of functional ability within a single assessment item. Since common balance measures have been shown to be significantly prone to algorithmic and technical variations, a robust alternative method is required. Thus, we introduce a model-based approach for balance and strength analyses, where the human lower extremities are modeled as an oscillating system during the phase of landing and recovery after a vertical jump. In the System and Control Technology, a transfer function of an oscillating system is described by a second-order delay element (PT2-element), which is characterized by the parameters natural frequency and damping. We analyze the jumps of 30 participants (70-87 years) regarding their jump phases and the mentioned parameters. A linear correlation between jump power and jump height, which are sensitive indicators of the muscle performance and the strength could be confirmed. While a correlation between jump power and spring constant could be observed, a significant relationship between the balance ability and natural frequency could not be identified.},
address = {Cham},
author = {Hellmers, Sandra and Fudickar, Sebastian and Dasenbrock, Lena and Heinks, Andrea and Bauer, J{\"{u}}rgen M. and Hein, Andreas},
booktitle = {Biomedical Engineering Systems and Technologies},
doi = {10.1007/978-3-319-94806-5_19},
editor = {{Peixoto Nathalia} and and Silveira, Margarida and {and Ali Hesham H.} and {and Maciel Carlos} and {and van den Broek Egon L}},
file = {:home/s/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hellmers Sandra et al. - 2018 - A Model-Based Approach for Jump Analyses Regarding Strength and Balance.pdf:pdf},
isbn = {978-3-319-94806-5},
keywords = {UNIAMT,accepted,full paper},
pages = {354--375},
publisher = {Springer International Publishing},
title = {{A Model-Based Approach for Jump Analyses Regarding Strength and Balance}},
url = {http://doi.org/10.1007/978-3-319-94806-5_19},
year = {2018}
}
S. Hellmers, E. Steen, L. Dasenbrock, A. Heinks, J. M. Bauer, S. Fudickar, and A. Hein, "Towards a minimized unsupervised technical assessment of physical performance in domestic environments" in Proc. Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare - PervasiveHealth '17, New York, New York, USA, 2017.
doi: 10.1145/3154862.3154882
@inproceedings{Hellmers2017a, abstract = {Early detection of changes in mobility associated with functional decline can increase the therapeutic success by prolonging self-determined living. To get an unbiased and high frequently status of the physical performance of the persons at risk, unsupervised assessments of their functional abilities should ideally take place in their homes.$\backslash$r$\backslash$nThus, we have developed a minimized unsupervised technical assessment of physical performance in domestic environments.$\backslash$r$\backslash$nBy conducting an exploratory factor analysis, based on the results of 79 study participants with a minimum age of 70 years, we could clarify that common assessment items mainly represent three key parameters of functional performance$\backslash$r$\backslash$nmobility and endurance, strength and balance. Consequently, we identified a minimal set of assessment items that is suitable for home-assessments and that, since covering all three parameters, is able to generate clinical meaningful and relevant insights about the functional status. Regarding the parameter mobility, we developed a technical assessment of physical performance for domestic environments, which utilizes short distance walk times assessed via ambient presence sensors as an indicator for potential functional decline. In a field trial over ten months with 20 participants with a mean age of 84.25 years, we could confirm the general feasibility of our approach and the proposed system.},
address = {New York, New York, USA},
author = {Hellmers, Sandra and Steen, Enno-Edzard and Dasenbrock, Lena and Heinks, Andrea and Bauer, J{\"{u}}rgen M. and Fudickar, Sebastian and Hein, Andreas},
booktitle = {Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare - PervasiveHealth '17},
doi = {10.1145/3154862.3154882},
file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hellmers et al. - 2017 - Towards a Minimized Unsupervised Technical Assessment of Physical Performance in Domestic Environments.pdf:pdf},
isbn = {9781450363631},
keywords = {UNIAMT,accepted,full paper},
mendeley-tags = {UNIAMT,accepted,full paper},
pages = {207--216},
publisher = {ACM Press},
title = {{Towards a minimized unsupervised technical assessment of physical performance in domestic environments}},
url = {http://dl.acm.org/citation.cfm?doid=3154862.3154882},
year = {2017}
}
S. Hellmers, S. Fudickar, E. Lange, C. Lins, and A. Hein, "Validation of a motion capture suit for clinical gait analysis" in Proc. Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare - PervasiveHealth '17, New York, New York, USA, 2017.
doi: 10.1145/3154862.3154884
@inproceedings{Hellmers2017b, abstract = {Gait analysis is often supported by technology. Due to limitations in optical systems, such as limited measurement volumes and the requirement of a laboratory environment, low-cost inertial measurement unit (IMU) based motion capture system might be better suited for gait analysis since they involve no spatial limitations and are flexible applicable. In this paper we investigate, if a low-cost IMU-based motion capture suits are an adequate alternative for clinical gait analysis in terms of accuracy of the determination of joint flexions and gait parameters. For this reason, we developed a gait analysis system and a gait analysis algorithm, which detects joint positions based on the Joint Coordinate System and determines knee, hip, and ankle flexions, as well as spatiotemporal parameters such as the number of steps, cadence, step duration and step length, and the specific gait phases. We evaluated and validated the IMU-based system in comparison to camera-based measurements (as gold standard) with three different healthy adult subjects. The evaluation indicates that the full-body motion capture system achieves a high degree of precision (0.86) and recall (0.98) in the recognition of gait cycles. The harmonic mean F(0.15) of the two factors precision and recall is on average 0.96 and the mentioned temporal gait parameters can be determined with an error below 10 ms. The mean derivation in the determination of joint angles amounts 1.35+-2°. Consequently, the article at hand indicates that low-cost IMU-based motion capture suits are an accurate alternative for gait analysis.},
address = {New York, New York, USA},
author = {Hellmers, Sandra and Fudickar, Sebastian and Lange, Eugen and Lins, Christian and Hein, Andreas},
booktitle = {Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare - PervasiveHealth '17},
doi = {10.1145/3154862.3154884},
file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hellmers et al. - 2017 - Validation of a Motion Capture Suit for Clinical Gait Analysis.pdf:pdf},
isbn = {9781450363631},
keywords = {UNIAMT,accepted,full paper},
mendeley-tags = {UNIAMT,accepted,full paper},
pages = {120--126},
publisher = {ACM Press},
title = {{Validation of a motion capture suit for clinical gait analysis}},
url = {http://dl.acm.org/citation.cfm?doid=3154862.3154884},
year = {2017}
}
S. Hellmers, S. Fudickar, C. Büse, L. Dasenbrock, A. Heinks, J. M. Bauer, and A. Hein, Technology Supported Geriatric AssessmentCham: Springer International Publishing.
doi: 10.1007/978-3-319-52322-4_6
@incollection{Hellmers2017c, abstract = {Healthy aging is a core societal aim especially regarding the demographic change. But with aging, functional decline can occur and this is a major challenge for health care systems. For the evaluation of the health of older adults and the identification of early changes associated with functional and cognitive decline, clinical geriatric assessments are a well-established approach. Ideally, the assessments should take place at home of the older adults or even in their daily life, to get an unbiased functional status. Therefore, we introduce a technology supported geriatric assessment as an intermediate step to a home-assessment or in future to sensor-based-assessments in daily life. Beside various ambient sensors, a sensor belt is used during the assessments and for 1 week in the participants' daily life. We discuss the suitability of our measuring devices for an ambient home-assessment and evaluate the sensors in comparison to valid measurements. Thereby, we show that light barrier measurements achieve a high sensitivity and a good correlation to manual measurements through study nurses or physical therapists.},
address = {Cham},
author = {Hellmers, Sandra and Fudickar, Sebastian and B{\"{u}}se, Clemens and Dasenbrock, Lena and Heinks, Andrea and Bauer, J{\"{u}}rgen M. and Hein, Andreas},
booktitle = {Ambient Assisted Living: 9. AAL-Kongress, Frankfurt/M, Germany, April 20 - 21, 2016},
doi = {10.1007/978-3-319-52322-4_6},
editor = {Wichert, Reiner and Mand, Beate},
file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hellmers et al. - 2016 - Technology supported geriatric assessment.pdf:pdf},
isbn = {978-3-319-52322-4},
keywords = {UNIAMT,accepted,full paper},
mendeley-tags = {UNIAMT,accepted,full paper},
pages = {85--100},
publisher = {Springer International Publishing},
title = {{Technology Supported Geriatric Assessment}},
url = {http://dx.doi.org/10.1007/978-3-319-52322-4_6},
year = {2017}
}
