Climbing/Descending Stairs Detection Using Inertial Sensors and Implementing PCA and a SVM Classifier

Álvarez, Raúl Alonso; Pulido, E F Jorge; Sierra, Daniel A.

doi:10.1007/978-981-10-4086-3_146

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The use of Support Vector Machines for the characterization of movements using accelerometers has been successfully explored before [17]–[21]. Furthermore, SVM’s suitability for binary classification problems with small numbers of features makes it an ideal choice of algorithm for this study [31].…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning algorithms have proven successful in characterizing motor activities extracted from accelerometers [15], [16]. Specifically, support vector machines (SVMs) have been used for movement characterization and activity recognition in accelerometers and other activity monitoring devices with surprising success rates [17]–[21]. While studies applying machine learning and big data approaches for assessment in the ICU have been explored before, they have been focused towards determining agitation and sedation patterns, and delirium state, but not motor impairment [22]–[24].…”

Section: Introductionmentioning

confidence: 99%

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Use of Accelerometry for Long Term Monitoring of Stroke Patients

Lucas

Hermiz

LaBuzetta

et al. 2019

IEEE J. Transl. Eng. Health Med.

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Stroke patients are monitored hourly by physicians and nurses in an attempt to better understand their physical state. To quantify the patients’ level of mobility, hourly movement (i.e. motor) assessment scores are performed, which can be taxing and time-consuming for nurses and physicians. In this paper, we attempt to find a correlation between patient motor scores and continuous accelerometer data recorded in subjects who are unilaterally impaired due to stroke. The accelerometers were placed on both upper and lower extremities of four severely unilaterally impaired patients and their movements were recorded continuously for 7 to 14 days. Features that incorporate movement smoothness, strength, and characteristic movement patterns were extracted from the accelerometers using time-frequency analysis. Support vector classifiers were trained with the extracted features to test the ability of the long term accelerometer recordings in predicting dependent and antigravity sides, and significantly above baseline performance was obtained in most instances ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$P < 0.05$ \end{document} ). Finally, a leave-one-subject-out approach was carried out to assess the generalizability of the proposed methodology, and above baseline performance was obtained in two out of the three tested subjects. The methodology presented in this paper provides a simple, yet effective approach to perform long term motor assessment in neurocritical care patients.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Accelerometry for Long Term Monitoring of Stroke Patients

Lucas

Hermiz

LaBuzetta

et al. 2019

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

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Pre-and-post impact fall detection based on support vector machines using inertial and barometric pressure data

Álvarez-Jiménez,

Pulido Herrera,

Ruiz-Olaya

et al. 2024

Tecnura

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Objective: This paper presents a novel real-time algorithm for fall detection, which contextualizes falls by identifying activities occurring both pre- and post-impact utilizing machine learning techniques and wearable sensors. Methodology: The activities selected to contextualize fall events included standing, lying, walking, running, climbing stairs, and using the elevator. Data were collected using an inertial measurement unit and a barometric altimeter positioned on the participants’ lower backs. Thirteen healthy subjects were observed performing the activities and fall events were recorded from five healthy subjects. The proposed algorithm combines thresholding and cascade support vector machines (SVMs), whose robustness is enhanced by a verification process of the subject’s posture aimed at determining the occurrence of the fall more accurately. Results: The performance of the algorithm was evaluated in terms of the hit rate (HT) both offline and in real-time. From the activities studied, stairs climbing proved to be the most challenging to detect, with an offline HT of 85% and an online HT of 76 %. The overall offline performance was superior, with an HT of 96 %, compared to the performance achieved online, an HT of 91 %; in both cases the fall detection HT was 100 %. Conclusions: The algorithm can be used to recognize fall events occurring to any user, as it has the advantage of not needing prior adaptation due to the nonlinear nature of the SVMs. The cascade SVMs allow for using small sets of variables, leading to low computational cost and a suitable real-time implementation. These features, in addition to the posture verification process, make our algorithm suitable for activity recognition in non-laboratory environments.

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Climbing/Descending Stairs Detection Using Inertial Sensors and Implementing PCA and a SVM Classifier

Cited by 2 publications

References 7 publications

Use of Accelerometry for Long Term Monitoring of Stroke Patients

Use of Accelerometry for Long Term Monitoring of Stroke Patients

Pre-and-post impact fall detection based on support vector machines using inertial and barometric pressure data

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