Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic Environments

Hellmers, Sandra; Kromke, Tobias; Dasenbrock, Lena; Heinks, Andrea; Bauer, Jürgen; Hein, Andreas; Fudickar, Sebastian

doi:10.5220/0006543900390047

Cited by 12 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%

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%

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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“…On the contrary, frequent assessments would enable early detection of functional decline and initiate preventive measures [5].…”

Section: Introductionmentioning

confidence: 99%

“…In the context of HAR for detection of stair climbing, approaches mainly differ for the sensing modality, position of the sensor/s, features, algorithms used for classification, and target users. Sensors comprise IMUs [5,[12][13][14][15] and their combined use with barometers [16][17][18][19][20][21]. Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21].…”

Section: Introductionmentioning

confidence: 99%

“…Sensors comprise IMUs [5,[12][13][14][15] and their combined use with barometers [16][17][18][19][20][21]. Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21]. Common features are averages, ranges, Fourier Transform and Wavelet coefficients, statistical moments of sensor data, whereas classifiers are Support Vector Machine (SVM) [5,12,16,17,19], K-Nearest Neighbour (KNN) [5,16,17], Decision Tree and Random Forest [5,13,17,19], Artificial Neural Network (ANN) [22], and also convolutional NN [15].…”

Section: Introductionmentioning

confidence: 99%

“…Positions include wrist [16,17], chest [16,18,20], waist [5,12,17,19], calf and foot [13][14][15][16]21]. Common features are averages, ranges, Fourier Transform and Wavelet coefficients, statistical moments of sensor data, whereas classifiers are Support Vector Machine (SVM) [5,12,16,17,19], K-Nearest Neighbour (KNN) [5,16,17], Decision Tree and Random Forest [5,13,17,19], Artificial Neural Network (ANN) [22], and also convolutional NN [15]. Users span from healthy young people up to elderly patients with severe conditions such as Parkinson's disease or stroke [5,16,20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

et al. 2020

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Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB.

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Evaluation of Power-Based Stair Climb Performance via Inertial Measurement Units

Hellmers

Lau

Diekmann

et al. 2019

Biomedical Engineering Systems and Technologies

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Stair Climb Power Measurements via Inertial Measurement Units - Towards an Unsupervised Assessment of Strength in Domestic Environments

Cited by 12 publications

References 0 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

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Evaluation of Power-Based Stair Climb Performance via Inertial Measurement Units

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