Dataset Information

Accelerometer-Based Machine Learning Categorization of Body Position in Adult Populations.

ABSTRACT: This manuscript describes tests and results of a study to evaluate classification algorithms derived from accelerometer data collected on healthy adults and older adults to better classify posture movements. Specifically, tests were conducted to 1) compare performance of 1 sensor vs. 2 sensors; 2) examine custom trained algorithms to classify for a given task 3) determine overall classifier accuracy for healthy adults under 55 and older adults (55 or older). Despite the current variety of commercially available platforms, sensors, and analysis software, many do not provide the data granularity needed to characterize all stages of movement. Additionally, some clinicians have expressed concerns regarding validity of analysis on specialized populations, such as hospitalized older adults. Accurate classification of movement data is important in a clinical setting as more hospital systems are using sensors to help with clinical decision making. We developed custom software and classification algorithms to identify laying, reclining, sitting, standing, and walking. Our algorithm accuracy is 93.2% for healthy adults under 55 and 95% for healthy older adults over 55 for the tasks in our setting. The high accuracy of this approach will aid future investigation into classifying movement in hospitalized older adults. Results from these tests also indicate that researchers and clinicians need to be aware of sensor body position in relation to where the algorithm used was trained. Additionally, results suggest more research is needed to determine if algorithms trained on one population can accurately be used to classify data from another population.

SUBMITTER: Jarvis L

PROVIDER: S-EPMC7548108 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Accelerometer-Based Machine Learning Categorization of Body Position in Adult Populations.

Jarvis Leighanne L Moninger Sarah S Pavon Juliessa J Throckmorton Chandra C Caves Kevin K

Computers helping people with special needs : ... International Conference, ICCHP ... : proceedings. International Conference on Computers Helping People with Special Needs 20200904

This manuscript describes tests and results of a study to evaluate classification algorithms derived from accelerometer data collected on healthy adults and older adults to better classify posture movements. Specifically, tests were conducted to 1) compare performance of 1 sensor vs. 2 sensors; 2) examine custom trained algorithms to classify for a given task 3) determine overall classifier accuracy for healthy adults under 55 and older adults (55 or older). Despite the current variety of commer ...[more]

PMID: 33047112

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Project description:BackgroundDistribution of long-lasting insecticidal bed nets (LLINs) is one of the main control strategies for malaria. Improving malaria prevention programmes requires understanding usage patterns in households receiving LLINs, but there are limits to what standard cross-sectional surveys of self-reported LLIN use can provide. This study was designed to assess the performance of an accelerometer-based approach for measuring a range of LLIN use behaviours as a proof of concept for more granular LLIN-use monitoring over longer time periods.MethodsThis study was carried out under controlled conditions from May to July 2018 in Liverpool, UK. A single accelerometer was affixed to the side panel of an LLIN and participants carried out five LLIN use behaviours: (1) unfurling a net; (2) entering an unfurled net; (3) lying still as if sleeping; (4) exiting from under a net; and, (5) folding up a net. The randomForest package in R, a supervised non-linear classification algorithm, was used to train models on 20-s epochs of tagged accelerometer data. Models were compared in a validation dataset using overall accuracy, sensitivity and specificity, receiver operating curves and the area under the curve (AUC).ResultsThe five-category model had overall accuracy of 82.9% in the validation dataset, a sensitivity of 0.681 for entering a net, 0.632 for exiting, 0.733 for net down, and 0.800 for net up. A simplified four-category model, combining entering/exiting a net into one category had accuracy of 94.8%, and increased sensitivity for net down (0.756) and net up (0.829). A further simplified three-category model, identifying sleeping, net up, and a combined net down/enter/exit category had accuracy of 96.2% (483/502), with an AUC of 0.997 for net down and 0.987 for net up. Models for detecting entering/exiting by adults were significantly more accurate than for children (87.8% vs 70.0%; p < 0.001) and had a higher AUC (p = 0.03).ConclusionsUnderstanding how LLINs are used is crucial for planning malaria prevention programmes. Accelerometer-based systems provide a promising new methodology for studying LLIN use. Further work exploring accelerometer placement, frequency of measurements and other machine learning approaches could make these methods even more accurate in the future.

Dataset Information

Accelerometer-Based Machine Learning Categorization of Body Position in Adult Populations.

Publications

Accelerometer-Based Machine Learning Categorization of Body Position in Adult Populations.

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