Towards a minimized unsupervised technical assessment of physical performance in domestic environments

Hellmers, Sandra; Steen, Enno-Edzard; Dasenbrock, Lena; Heinks, Andrea; Bauer, Jürgen; Fudickar, Sebastian; Hein, Andreas

doi:10.1145/3154862.3154882

Cited by 17 publications

(13 citation statements)

References 17 publications

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“…In theory, the optimal screening method would be a continuous data collection, e.g., by wearable devices. While various systems were proposed for the extraction of stair climb power [21] and mobility in general [22,23], the monitored biomechanical parameters are most meaningful within a defined context. However, monitoring systems for home-use are susceptible to unrecognized contextual variations [22].…”

Section: Introductionmentioning

confidence: 99%

“…While various systems were proposed for the extraction of stair climb power [21] and mobility in general [22,23], the monitored biomechanical parameters are most meaningful within a defined context. However, monitoring systems for home-use are susceptible to unrecognized contextual variations [22]. Therefore, a frequently conducted assessment in a standardized setting (e.g., within a medical screening) is more suitable and assures higher intra-test reliability.…”

Section: Introductionmentioning

confidence: 99%

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Measurement System for Unsupervised Standardized Assessment of Timed “Up & Go” and Five Times Sit to Stand Test in the Community—A Validity Study

Fudickar

Hellmers

Lau

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement System for Unsupervised Standardized Assessment of Timed “Up & Go” and Five Times Sit to Stand Test in the Community—A Validity Study

Fudickar

Hellmers

Lau

et al. 2020

Sensors

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“…This test assesses the basic mobility skills, which make up important abilities for independent living. Besides balance and strength, mobility is one of the essential components of physical function [ 2 ]. The TUG consists of several components of everyday movements (see Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements

Hellmers

Izadpanah

Dasenbrock

et al. 2018

Sensors

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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.

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“…Performance-based tests are important clinical tools used to identify functional decline in older adults and patients with either neurological or motor impairments [1]. Among them, the Sit-to-Stand test (STS) is strictly correlated with functional capacity and with independence in the activities of daily living [2][3][4][5][6][7][8][9]. Furthermore, its simplicity allows it to be performed both in clinical and in-home environments across a broad range of patients' functional conditions [1,5].…”

Section: Introductionmentioning

confidence: 99%

Categorisation of Sit-To-Stand Motion Pattern. Human vs Automated Quantitative Assessments.

Job

Battista

Stanzani

et al. 2020

Preprint

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BACKGROUND: The Sit-to-Stand (STS) test is widely used in clinical practice as an indicator of lower-limb functionality decline, especially for older adults. Hitherto, due to its high variability, there is no standard approach for categorising the STS motion pattern, and the vision-based evaluation remains the most reliable method to evaluate people’ performance. This paper presents a comparative analysis between visual assessments and an automated-software approach for the categorisation of STS, relying on registrations from a force plate. METHODS: A group of 5 participants (30 ± 6 years) took part in 2 different sessions of visual inspections on 200 STS movements randomly extracted from a dataset of 742 acquisitions under self-paced and controlled speed conditions. Assessors were asked to identify three specific STS events from the Ground Reaction Force, simultaneously with the software analysis: the start of the trunk movement (Initiation), the beginning of the stable upright stance (Standing) and the sitting movement (Sitting). The Test-Retest Reliability between first and second visual evaluations was compared with the Inter-Rater Agreement between visual and software assessments, as indexes of human and software performance, respectively.RESULTS: No statistical differences between methods were found for the identification of the Initiation and the Sitting events at self-paced speed and for only the Sitting event at controlled speed. The estimated significant values of maximum discrepancy between visual and automated assessments were 0.200 s [0,039; 0.361] in unconstrained conditions and 0,340 s [0,014; 0,666] for standardised movements.CONCLUSIONS: The software assessments displayed an overall good agreement against visual evaluations of the Ground Reaction Force, relying, at the same time, on objective measures. In this sense, the proposed approach can provide robust and consistent data in the field of Big Data analytics, augmenting the performance of artificial intelligence methods for Human Activity Recognition tasks.

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Towards a minimized unsupervised technical assessment of physical performance in domestic environments

Cited by 17 publications

References 17 publications

Measurement System for Unsupervised Standardized Assessment of Timed “Up & Go” and Five Times Sit to Stand Test in the Community—A Validity Study

Measurement System for Unsupervised Standardized Assessment of Timed “Up & Go” and Five Times Sit to Stand Test in the Community—A Validity Study

Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements

Categorisation of Sit-To-Stand Motion Pattern. Human vs Automated Quantitative Assessments.

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